This document describes using dummy predictor variables in multiple regression analysis. It provides an example using hypothetical data on faculty salaries. Key points:
- Dummy variables allow inclusion of categorical predictors like gender or political party in regression by coding them numerically.
- For k categories, k-1 dummy variables are needed. This example uses gender (coded 0,1) and college (coded 1,2,3) as predictors.
- Regression and ANOVA provide equivalent information about differences in mean salaries for gender and across colleges. Dummy variable regression tests are equivalent to ANOVA comparisons.
- The document screens the salary data for violations of regression assumptions like normality before running analyses.
Chapter 12 - DUMMY PREDICTOR VARIABLES IN MULTIPLE REGRESSION12..docxcravennichole326
Chapter 12 - DUMMY PREDICTOR VARIABLES IN MULTIPLE REGRESSION
12.1 Research Situations Where Dummy Predictor Variables Can Be Used
Previous examples of regression analysis have used scores on quantitative X variables to predict scores on a quantitative Y variable. However, it is possible to include group membership or categorical predictor variables as predictors in regression analysis. This can be done by creating dummy (dichotomous) predictor variables to represent information about group membership. A dummy or dichotomous predictor variable provides yes/no information for questions about group membership. For example, a simple dummy variable to represent gender corresponds to the following question: Is the participant female (0) or male (1)? Gender is an example of a two-group categorical variable that can be represented by a single dummy variable.
When we have more than two groups, we can use a set of dummy variables to provide information about group membership. For example, suppose that a study includes members of k = 3 political party groups. The categorical variable political party has the following scores: 1 = Democrat, 2 = Republican, and 3 = Independent. We might want to find out whether mean scores on a quantitative measure of political conservatism (Y) differ across these three groups. One way to answer this question is to perform a one-way analysis of variance (ANOVA) that compares mean conservatism (Y) across the three political party groups. In this chapter, we will see that we can also use regression analysis to evaluate how political party membership is related to scores on political conservatism.
However, we should not set up a regression to predict scores on conservatism from the multiple-group categorical variable political party, with party membership coded 1 = Democrat, 2 = Republican, and 3 = Independent. Multiple-group categorical variables usually do not work well as predictors in regression, because scores on a quantitative outcome variable, such as “conservatism,” will not necessarily increase linearly with the score on the categorical variable that provides information about political party. The score values that represent political party membership may not be rank ordered in a way that is monotonically associated with changes in conservatism; as we move from Group 1 = Democrat to Group 2 = Republican, scores on conservatism may increase, but as we move from Group 2 = Republican to Group 3 = Independent, conservatism may decrease. Even if the scores that represent political party membership are rank ordered in a way that is monotonically associated with level of conservatism, the amount of change in conservatism between Groups 1 and 2 may not be equal to the amount of change in conservatism between Groups 2 and 3. In other words, scores on a multiple-group categorical predictor variable (such as political party coded 1 = Democrat, 2 = Republican, and 3 = Independent) are not necessarily linearly related to scores ...
Assessment 3 ContextYou will review the theory, logic, and a.docxgalerussel59292
Assessment 3 Context
You will review the theory, logic, and application of t-tests. The t-test is a basic inferential statistic often reported in psychological research. You will discover that t-tests, as well as analysis of variance (ANOVA), compare group means on some quantitative outcome variable.
Recall that null hypothesis tests are of two types: (1) differences between group means and (2) association between variables. In both cases there is a null hypothesis and an alternative hypothesis. In the group means test, the null hypothesis is that the two groups have equal means, and the alternative hypothesis is that the two groups do not have equal means. In the association between variables type of test, the null hypothesis is that the correlation coefficient between the two variables is zero, and the alternative hypothesis is that the correlation coefficient is not zero.
Notice in each case that the hypotheses are mutually exclusive. If the null is false, the alternative must be true. The purpose of null hypothesis statistical tests is generally to show that the null has a low probability of being true (the p value is less than .05) – low enough that the researcher can legitimately claim it is false. The reason this is done is to support the allegation that the alternative hypothesis is true.
In this context you will be studying the details of the first type of test. This is the test of difference between group means. In variations on this model, the two groups can actually be the same people under different conditions, or one of the groups may be assigned a fixed theoretical value. The main idea is that two mean values are being compared. The two groups each have an average score or mean on some variable. The null hypothesis is that the difference between the means is zero. The alternative hypothesis is that the difference between the means is not zero. Notice that if the null is false, the alternative must be true. It is first instructive to consider some of the details of groups. Means, and difference between them.
Null Hypothesis Significance Test
The most common forms of the Null Hypothesis Significance Test (NHST) are three types of t tests, and the test of significance of a correlation. The NHST also extends to more complex tests, such as ANOVA, which will be discussed separately. Below, the null hypothesis and the alternative hypothesis are given for each of the following tests. It would be a valuable use of your time to commit the information below to memory. Once this is done, then when we refer to the tests later, you will have some structure to make sense of the more detailed explanations.
1. One-sample t test: The question in this test is whether a single sample group mean is significantly different from some stated or fixed theoretical value - the fixed value is called a parameter.
· Null Hypothesis: The difference between the sample group mean and the fixed value is zero in the population.
· Alternative hypothesis: T.
Chapter 12 - DUMMY PREDICTOR VARIABLES IN MULTIPLE REGRESSION12..docxcravennichole326
Chapter 12 - DUMMY PREDICTOR VARIABLES IN MULTIPLE REGRESSION
12.1 Research Situations Where Dummy Predictor Variables Can Be Used
Previous examples of regression analysis have used scores on quantitative X variables to predict scores on a quantitative Y variable. However, it is possible to include group membership or categorical predictor variables as predictors in regression analysis. This can be done by creating dummy (dichotomous) predictor variables to represent information about group membership. A dummy or dichotomous predictor variable provides yes/no information for questions about group membership. For example, a simple dummy variable to represent gender corresponds to the following question: Is the participant female (0) or male (1)? Gender is an example of a two-group categorical variable that can be represented by a single dummy variable.
When we have more than two groups, we can use a set of dummy variables to provide information about group membership. For example, suppose that a study includes members of k = 3 political party groups. The categorical variable political party has the following scores: 1 = Democrat, 2 = Republican, and 3 = Independent. We might want to find out whether mean scores on a quantitative measure of political conservatism (Y) differ across these three groups. One way to answer this question is to perform a one-way analysis of variance (ANOVA) that compares mean conservatism (Y) across the three political party groups. In this chapter, we will see that we can also use regression analysis to evaluate how political party membership is related to scores on political conservatism.
However, we should not set up a regression to predict scores on conservatism from the multiple-group categorical variable political party, with party membership coded 1 = Democrat, 2 = Republican, and 3 = Independent. Multiple-group categorical variables usually do not work well as predictors in regression, because scores on a quantitative outcome variable, such as “conservatism,” will not necessarily increase linearly with the score on the categorical variable that provides information about political party. The score values that represent political party membership may not be rank ordered in a way that is monotonically associated with changes in conservatism; as we move from Group 1 = Democrat to Group 2 = Republican, scores on conservatism may increase, but as we move from Group 2 = Republican to Group 3 = Independent, conservatism may decrease. Even if the scores that represent political party membership are rank ordered in a way that is monotonically associated with level of conservatism, the amount of change in conservatism between Groups 1 and 2 may not be equal to the amount of change in conservatism between Groups 2 and 3. In other words, scores on a multiple-group categorical predictor variable (such as political party coded 1 = Democrat, 2 = Republican, and 3 = Independent) are not necessarily linearly related to scores ...
Assessment 3 ContextYou will review the theory, logic, and a.docxgalerussel59292
Assessment 3 Context
You will review the theory, logic, and application of t-tests. The t-test is a basic inferential statistic often reported in psychological research. You will discover that t-tests, as well as analysis of variance (ANOVA), compare group means on some quantitative outcome variable.
Recall that null hypothesis tests are of two types: (1) differences between group means and (2) association between variables. In both cases there is a null hypothesis and an alternative hypothesis. In the group means test, the null hypothesis is that the two groups have equal means, and the alternative hypothesis is that the two groups do not have equal means. In the association between variables type of test, the null hypothesis is that the correlation coefficient between the two variables is zero, and the alternative hypothesis is that the correlation coefficient is not zero.
Notice in each case that the hypotheses are mutually exclusive. If the null is false, the alternative must be true. The purpose of null hypothesis statistical tests is generally to show that the null has a low probability of being true (the p value is less than .05) – low enough that the researcher can legitimately claim it is false. The reason this is done is to support the allegation that the alternative hypothesis is true.
In this context you will be studying the details of the first type of test. This is the test of difference between group means. In variations on this model, the two groups can actually be the same people under different conditions, or one of the groups may be assigned a fixed theoretical value. The main idea is that two mean values are being compared. The two groups each have an average score or mean on some variable. The null hypothesis is that the difference between the means is zero. The alternative hypothesis is that the difference between the means is not zero. Notice that if the null is false, the alternative must be true. It is first instructive to consider some of the details of groups. Means, and difference between them.
Null Hypothesis Significance Test
The most common forms of the Null Hypothesis Significance Test (NHST) are three types of t tests, and the test of significance of a correlation. The NHST also extends to more complex tests, such as ANOVA, which will be discussed separately. Below, the null hypothesis and the alternative hypothesis are given for each of the following tests. It would be a valuable use of your time to commit the information below to memory. Once this is done, then when we refer to the tests later, you will have some structure to make sense of the more detailed explanations.
1. One-sample t test: The question in this test is whether a single sample group mean is significantly different from some stated or fixed theoretical value - the fixed value is called a parameter.
· Null Hypothesis: The difference between the sample group mean and the fixed value is zero in the population.
· Alternative hypothesis: T.
this activity is designed for you to explore the continuum of an a.docxhowardh5
this activity is designed for you to explore the continuum of an addictive behavior of your choice.
Addictive behavior appears in stages. The earliest stage is non-use, which finally leads up to out-of-control dependence. The stages in between are important to identify, as it is much easier to correct an early-stage issue as opposed to a late-stage problem.
After reviewing the module readings and tasks, use the module notes as a reference and alcohol or substance abuse addiction as an example to identify the various levels of addiction.
You may choose to develop a time line identifying the stages or develop a written essay (no more than 500 words in Word format) to describe the escalation of addictive behaviors.
You are to include at least two references from academic sources that you have researched on this topic in the Excelsior College Library and use appropriate citations in American Psychological Association (APA) style.
You cannot just do a Google search for the topic! Academic sources are required. You may use Google Scholar or other libraries.
Chapter 13
Qualitative Data Analysis
1
Process of Qualitative Data Analysis
Preparing the Qualitative Data
Transform the data into readable text
Check for and resolve transcription errors
Manage the data
Organize by attribute coding
Two Separate Processes
5
Coding: Involves labeling and breaking down the data to find:
Patterns
Themes
Interpretation: Giving meaning to the identified patterns and themes
Coding
Starts with identifying the unit of analysis
Coding categories may reflect realms of meaning or different activities.
Coding categories can be theoretically-based or inductively created emerging from the data.
Use of Analytical Memos
7
Analytical memos help researchers w/ process of breaking down the data
Personal reflections on the research experience, methodological issues, or patterns in the data
Comes in 3 varieties:
Code notes
Operational notes
Theoretical notes
Data Displays
Taxonomy: system of ordered classification
Data matrix: individuals or other units represent columns and coding categories represent rows
Typologies: representation of findings based on the interrelationship between two or more ideas, concepts, or variables
Flow charts: diagrams that display processes
Taxonomy of Survival Strategies
Data Matrix: Homeless Individuals by Dimensions
Drawing and Evaluating Conclusions
Conclusions may result in:
Rich descriptions
Identification of themes
Inferences about patterns and concepts
Theoretical propositions
Evaluation of the data can occur by:
Comparing notes among observers
Using multiple sources of data
Examining exceptions to the data patterns
Member checking
Variations in Qualitative Data Analysis: Grounded Theory
Objective is to develop theory from data
Emphasizes people’s actions and voices as the main sources of d.
Chi-square tests are great to show if distributions differ or i.docxMARRY7
Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you?
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: ...
Calculating Analysis of Variance (ANOVA) and Post Hoc Analyses Follo.docxaman341480
Calculating Analysis of Variance (ANOVA) and Post Hoc Analyses Following ANOVA
Analysis of variance (ANOVA)
is a statistical procedure that compares data between two or more groups or conditions to investigate the presence of differences between those groups on some continuous dependent variable (see
Exercise 18
). In this exercise, we will focus on the
one-way ANOVA
, which involves testing one independent variable and one dependent variable (as opposed to other types of ANOVAs, such as factorial ANOVAs that incorporate multiple independent variables).
Why ANOVA and not a
t
-test? Remember that a
t
-test is formulated to compare two sets of data or two groups at one time (see
Exercise 23
for guidance on selecting appropriate statistics). Thus, data generated from a clinical trial that involves four experimental groups, Treatment 1, Treatment 2, Treatments 1 and 2 combined, and a Control, would require 6
t
-tests. Consequently, the chance of making a Type I error (alpha error) increases substantially (or is inflated) because so many computations are being performed. Specifically, the chance of making a Type I error is the number of comparisons multiplied by the alpha level. Thus, ANOVA is the recommended statistical technique for examining differences between more than two groups (
Zar, 2010
).
ANOVA is a procedure that culminates in a statistic called the
F
statistic. It is this value that is compared against an
F
distribution (see
Appendix C
) in order to determine whether the groups significantly differ from one another on the dependent variable. The formulas for ANOVA actually compute two estimates of variance: One estimate represents differences between the groups/conditions, and the other estimate represents differences among (within) the data.
Research Designs Appropriate for the One-Way ANOVA
Research designs that may utilize the one-way ANOVA include the randomized experimental, quasi-experimental, and comparative designs (
Gliner, Morgan, & Leech, 2009
). The independent variable (the “grouping” variable for the ANOVA) may be active or attributional. An active independent variable refers to an intervention, treatment, or program. An attributional independent variable refers to a characteristic of the participant, such as gender, diagnosis, or ethnicity. The ANOVA can compare two groups or more. In the case of a two-group design, the researcher can either select an independent samples
t
-test or a one-way ANOVA to answer the research question. The results will always yield the same conclusion, regardless of which test is computed; however, when examining differences between more than two groups, the one-way ANOVA is the preferred statistical test.
Example 1: A researcher conducts a randomized experimental study wherein she randomizes participants to receive a high-dosage weight loss pill, a low-dosage weight loss pill, or a placebo. She assesses the number of pounds lost from baseline to post-treatment
378
for the thre ...
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS11 .docxnovabroom
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS
11: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Testing the Difference Between Two Sample Means
Lightboard Lecture Video
· Independent t Tests
Time to Practice Video
· Chapter 11: Problem 5
Difficulty Scale
(A little longer than the previous chapter but basically the same kind of procedures and very similar questions. Not too hard, but you have to pay attention.)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Using the t test for independent means when appropriate
· Computing the observed t value
· Interpreting the t value and understanding what it means
· Computing the effect size for a t test for independent means
INTRODUCTION TO THE T TEST FOR INDEPENDENT SAMPLES
Even though eating disorders are recognized for their seriousness, little research has been done that compares the prevalence and intensity of symptoms across different cultures. John P. Sjostedt, John F. Schumaker, and S. S. Nathawat undertook this comparison with groups of 297 Australian and 249 Indian university students. Each student was measured on the Eating Attitudes Test and the Goldfarb Fear of Fat Scale. High scores on both measures indicate the presence of an eating disorder. The groups’ scores were compared with one another. On a comparison of means between the Indian and the Australian participants, Indian students scored higher on both of the tests, and this was due mainly to the scores of women. The results for the Eating Attitudes Test were t(544) = −4.19, p < .0001, and the results for the Goldfarb Fear of Fat Scale were t(544) = −7.64, p < .0001.
Now just what does all this mean? Read on.
Why was the t test for independent means used? Sjostedt and his colleagues were interested in finding out whether there was a difference in the average scores of one (or more) variable(s) between the two groups. The t test is called independent because the two groups were not related in any way. Each participant in the study was tested only once. The researchers applied a t test for independent means, arriving at the conclusion that for each of the outcome variables, the differences between the two groups were significant at or beyond the .0001 level. Such a small chance of a Type I error means that there is very little probability that the difference in scores between the two groups was due to chance and not something like group membership, in this case representing nationality, culture, or ethnicity.
Want to know more? Go online or to the library and find …
Sjostedt, J. P., Schumaker, J. F., & Nathawat, S. S. (1998). Eating disorders among Indian and Australian university students. Journal of Social Psychology, 138(3), 351–357.
LIGHTBOARD LECTURE VIDEO
Independent t Tests
THE PATH TO WISDOM AND KNOWLEDGE
Here’s how you can use Figure 11.1, the flowchart introduced in Chapter 9, to select the appropriate test statistic, the t test for independent means. Follow along the highlighted sequence of steps in Figure 1.
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS11 .docxhyacinthshackley2629
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS
11: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Testing the Difference Between Two Sample Means
Lightboard Lecture Video
· Independent t Tests
Time to Practice Video
· Chapter 11: Problem 5
Difficulty Scale
(A little longer than the previous chapter but basically the same kind of procedures and very similar questions. Not too hard, but you have to pay attention.)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Using the t test for independent means when appropriate
· Computing the observed t value
· Interpreting the t value and understanding what it means
· Computing the effect size for a t test for independent means
INTRODUCTION TO THE T TEST FOR INDEPENDENT SAMPLES
Even though eating disorders are recognized for their seriousness, little research has been done that compares the prevalence and intensity of symptoms across different cultures. John P. Sjostedt, John F. Schumaker, and S. S. Nathawat undertook this comparison with groups of 297 Australian and 249 Indian university students. Each student was measured on the Eating Attitudes Test and the Goldfarb Fear of Fat Scale. High scores on both measures indicate the presence of an eating disorder. The groups’ scores were compared with one another. On a comparison of means between the Indian and the Australian participants, Indian students scored higher on both of the tests, and this was due mainly to the scores of women. The results for the Eating Attitudes Test were t(544) = −4.19, p < .0001, and the results for the Goldfarb Fear of Fat Scale were t(544) = −7.64, p < .0001.
Now just what does all this mean? Read on.
Why was the t test for independent means used? Sjostedt and his colleagues were interested in finding out whether there was a difference in the average scores of one (or more) variable(s) between the two groups. The t test is called independent because the two groups were not related in any way. Each participant in the study was tested only once. The researchers applied a t test for independent means, arriving at the conclusion that for each of the outcome variables, the differences between the two groups were significant at or beyond the .0001 level. Such a small chance of a Type I error means that there is very little probability that the difference in scores between the two groups was due to chance and not something like group membership, in this case representing nationality, culture, or ethnicity.
Want to know more? Go online or to the library and find …
Sjostedt, J. P., Schumaker, J. F., & Nathawat, S. S. (1998). Eating disorders among Indian and Australian university students. Journal of Social Psychology, 138(3), 351–357.
LIGHTBOARD LECTURE VIDEO
Independent t Tests
THE PATH TO WISDOM AND KNOWLEDGE
Here’s how you can use Figure 11.1, the flowchart introduced in Chapter 9, to select the appropriate test statistic, the t test for independent means. Follow along the highlighted sequence of steps in Figure 1.
When you are working on the Inferential Statistics Paper I want yo.docxalanfhall8953
When you are working on the Inferential Statistics Paper I want you to format your paper with the following information
I. Introduction – What are inferential statistics and what is the research problem and hypothesis of the article?
II. Methods – Who are the subjects and variables within the article?
III. Results – What is the statistical analysis used, why were these tests chosen? What were the results of these tests and what do they mean?
IV. Discussion – What were the strengths of this article? What would you have done differently in terms of variables and statistical analysis? Why?
V. Conclusion – Reiterate the introduction and include relevant information that answers the questions regarding the hypothesis.
`
Read: Chapter 3 and 4 of Statistics for the Behavioral and Social Sciences.
Participate in One discussion.
Discussion 1 –Standard Normal Distribution– This allows you to look at any data set into the standard distribution form.
Quiz – Hypothesis testing
Submit your Inferential Statics Article Critique – Read Differential Effects of a Body Image Exposure Session on Smoking Urge Between Physically Active and Sedentary Female Smokers. What is the research question and hypothesis? Identify what variables were present, what inferential statistics were used and why, and if proper research methods were used. See grading rubric for full details.
Discussion Post Expectations:
Your initial post (your answer) is due by Day 3 (Thursday) of this week for Discussion 1.
When grading the Standard Normative Distribution discussion I will be looking for your answer to contain:
Week 2 Discussion 1 Board Rubric
Earned
Weight
Content Criteria
0.5
Student identifies and defines what Standard Normative Distribution (SND) is.
Student explains why it is needed to use a SND to compare two data sets.
0.5
Student identifies the purpose of a z-score in a SND.
0.5
Student identifies the purpose of a percentage in a SND.
0.25
Student explains whether a z-score or a percentage does a better job of identifying proportion of a SND.
0.25
The student responds to at least two classmates’ initial posts by Day 7.
1
Student uses correct spelling, grammar and sentence structure.
2
5
Grading - The discussions are both worth a total of 5 points. The breakdown of the grading for this week’s assignment (per discussion assignment) will be as follows:
Posting your answer by the due date (Day 3, Thursday) is worth 4 points. These five points will be based on the information outlined within the Discussion Assignment Expectations. Content will be worth 2 points and format; spelling and grammar will be worth 2 points.
Responding to two of your classmates (for each assignment) is worth 1 point. The answers must be substantive and go beyond “I agree” or “Good job” to qualify for this point.
Intellectual Elaboration:
In Wee.
RUNNING HEAD ONE WAY ANOVA1ONE WAY ANOVA .docxtoltonkendal
RUNNING HEAD: ONE WAY ANOVA 1
ONE WAY ANOVA 8
One-Way ANOVA
Stacy Hernandez
PSY7620
Dr. Lorie Fernandez
Capella University
Data Analysis and Application (DAA)
The one way ANOVA is used to determine whether there are any significant differences between the means of two or more independent groups. In this sample, the file grades.sav is used with section (independent variable) and quiz3 (dependent variable).
Data File Description
1. The one way ANOVA is used to determine whether there are any significant differences between the means of two or more independent groups.
2. In this sample, the file grades.sav is used with section (independent variable) and quiz3 (dependent variable).
3. The sample size (N) is 105.
Testing Assumptions
The dependent variable, quiz3, is measured at the interval or ratio level (meaning continuous). The dependent variable (quiz3) in this case, is therefore continuous since it ranges from one to 10. The independent variable (section) should consist of two or more categorical independent groups. In this case, the independent variable (section), has three groups, therefore it meets this assumption. There should be independence of observation, meaning that there is no relationship between the observations in each group or between the groups themselves. There should be no significant outliers, although there are single data points within the data that do not follow a normal pattern. Therefore, the outliers found will a negative effect on the one-way ANOVA, reducing the validity of the results.
Note the above boxplot indicates outliers in section two, with the id of 21.
The dependent variable (quiz3) should approximately have a normal distribution for each category of the independent variable (section). The null hypothesis is that the data is of a normal distribution, that the mean (average value of the dependent variable) is the same for all groups.
Ho – the observed distribution fits the normal distribution.
The alternative hypothesis is that the data does not have a normal distribution; the average is not the same for all groups.
Ha – the observed distribution does not fit the normal distribution.
It is observed that the data is not normally distributed. Most sections have quiz3 values between five and nine note this is a visual estimate. Note that the largest group also has the largest value of quiz3. The statistics from the histogram of quiz3 reveal that the Mean is 8.05; the Standard Deviation is 2.322, with a total number N of 105.
Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Deviation
Skewness
Kurtosis
Statistic
Statistic
Statistic
Statistic
Statistic
Statistic
Std. Error
Statistic
Std. Error
quiz3
105
0
10
8.05
2.322
-1.177
.236
.805
.467
section
105
1
3
2.00
.797
.000
.236
-1.419
.467
Valid N
105
When looking at skewness, for a perfectly normal and symmetrical distribution, it has a value of zero (Warner, 20 ...
Overview of Multivariate Statistical MethodsThomasUttaro1
This is an overview of advanced multivariate statistical methods which have become very relevant in many domains over the last few decades. These methods are powerful and can exploit the massive datasets implemented today in meaningful ways. Typically analytics platforms do not deploy these statistical methods, in favor of straightforward metrics and machine learning, and thus they are often overlooked. Additional references are available as documented.
Issues Identify at least seven issues you see in the case1..docxbagotjesusa
Issues: Identify at least seven issues you see in the case
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
What is the Key issue you see in the case: __________________________
What facts pertain to the case: Identify at least three important facts that pertain to the case
1.
2.
3.
4.
5.
What assumptions do you plan to make in your analysis: None is an acceptable answer
1.
2.
3
What people and organizations may have an impact on the case: There should be at least five.
1.
2.
3.
4.
5.
6.
7.
8.
9.
You are writing the case from the perspective of which person or organization:______________
What tools of Analysis would you use in this case: You only need to identify them and explain what information each will give you that you feel is important.
Based upon the above information – provide three alternatives
Alternative 1 is the Status Quo or to do nothing different that the current situation.
Identify at least three arguments in favor and three against this approach
Pros
1.
2.
3.
4.
5.
Cons
1.
2.
3.
4.
5.
Alternative 2 ____________________________________________________
Identify at least three arguments in favor and three against this approach
Pros
1.
2.
3.
4.
5.
Cons
1.
2.
3.
4.
5.
Alternative 3 ______________________________________________
Identify at least three arguments in favor and three against this approach
Pros
1.
2.
3.
4.
5.
Cons
1.
2.
3.
4.
5.
Given the information above select your recommended alternative and explain why you feel it is the best alternative: This should take three to five paragraphs and be based upon the information presented in your case.
.
Issues and disagreements between management and employees lead.docxbagotjesusa
Issues and disagreements between management and employees lead to formation of labor unions. Over the decades, the role of labor unions has been interpreted in various ways by employees across the globe.
What are some of the reasons employees join labor unions?
Did you ever belong to a labor union? If you did, do you think union membership benefited you?
If you've never belonged to a union, do you think it would have benefited you in your current or past employment? Why or why not?
.
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this activity is designed for you to explore the continuum of an addictive behavior of your choice.
Addictive behavior appears in stages. The earliest stage is non-use, which finally leads up to out-of-control dependence. The stages in between are important to identify, as it is much easier to correct an early-stage issue as opposed to a late-stage problem.
After reviewing the module readings and tasks, use the module notes as a reference and alcohol or substance abuse addiction as an example to identify the various levels of addiction.
You may choose to develop a time line identifying the stages or develop a written essay (no more than 500 words in Word format) to describe the escalation of addictive behaviors.
You are to include at least two references from academic sources that you have researched on this topic in the Excelsior College Library and use appropriate citations in American Psychological Association (APA) style.
You cannot just do a Google search for the topic! Academic sources are required. You may use Google Scholar or other libraries.
Chapter 13
Qualitative Data Analysis
1
Process of Qualitative Data Analysis
Preparing the Qualitative Data
Transform the data into readable text
Check for and resolve transcription errors
Manage the data
Organize by attribute coding
Two Separate Processes
5
Coding: Involves labeling and breaking down the data to find:
Patterns
Themes
Interpretation: Giving meaning to the identified patterns and themes
Coding
Starts with identifying the unit of analysis
Coding categories may reflect realms of meaning or different activities.
Coding categories can be theoretically-based or inductively created emerging from the data.
Use of Analytical Memos
7
Analytical memos help researchers w/ process of breaking down the data
Personal reflections on the research experience, methodological issues, or patterns in the data
Comes in 3 varieties:
Code notes
Operational notes
Theoretical notes
Data Displays
Taxonomy: system of ordered classification
Data matrix: individuals or other units represent columns and coding categories represent rows
Typologies: representation of findings based on the interrelationship between two or more ideas, concepts, or variables
Flow charts: diagrams that display processes
Taxonomy of Survival Strategies
Data Matrix: Homeless Individuals by Dimensions
Drawing and Evaluating Conclusions
Conclusions may result in:
Rich descriptions
Identification of themes
Inferences about patterns and concepts
Theoretical propositions
Evaluation of the data can occur by:
Comparing notes among observers
Using multiple sources of data
Examining exceptions to the data patterns
Member checking
Variations in Qualitative Data Analysis: Grounded Theory
Objective is to develop theory from data
Emphasizes people’s actions and voices as the main sources of d.
Chi-square tests are great to show if distributions differ or i.docxMARRY7
Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you?
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: ...
Calculating Analysis of Variance (ANOVA) and Post Hoc Analyses Follo.docxaman341480
Calculating Analysis of Variance (ANOVA) and Post Hoc Analyses Following ANOVA
Analysis of variance (ANOVA)
is a statistical procedure that compares data between two or more groups or conditions to investigate the presence of differences between those groups on some continuous dependent variable (see
Exercise 18
). In this exercise, we will focus on the
one-way ANOVA
, which involves testing one independent variable and one dependent variable (as opposed to other types of ANOVAs, such as factorial ANOVAs that incorporate multiple independent variables).
Why ANOVA and not a
t
-test? Remember that a
t
-test is formulated to compare two sets of data or two groups at one time (see
Exercise 23
for guidance on selecting appropriate statistics). Thus, data generated from a clinical trial that involves four experimental groups, Treatment 1, Treatment 2, Treatments 1 and 2 combined, and a Control, would require 6
t
-tests. Consequently, the chance of making a Type I error (alpha error) increases substantially (or is inflated) because so many computations are being performed. Specifically, the chance of making a Type I error is the number of comparisons multiplied by the alpha level. Thus, ANOVA is the recommended statistical technique for examining differences between more than two groups (
Zar, 2010
).
ANOVA is a procedure that culminates in a statistic called the
F
statistic. It is this value that is compared against an
F
distribution (see
Appendix C
) in order to determine whether the groups significantly differ from one another on the dependent variable. The formulas for ANOVA actually compute two estimates of variance: One estimate represents differences between the groups/conditions, and the other estimate represents differences among (within) the data.
Research Designs Appropriate for the One-Way ANOVA
Research designs that may utilize the one-way ANOVA include the randomized experimental, quasi-experimental, and comparative designs (
Gliner, Morgan, & Leech, 2009
). The independent variable (the “grouping” variable for the ANOVA) may be active or attributional. An active independent variable refers to an intervention, treatment, or program. An attributional independent variable refers to a characteristic of the participant, such as gender, diagnosis, or ethnicity. The ANOVA can compare two groups or more. In the case of a two-group design, the researcher can either select an independent samples
t
-test or a one-way ANOVA to answer the research question. The results will always yield the same conclusion, regardless of which test is computed; however, when examining differences between more than two groups, the one-way ANOVA is the preferred statistical test.
Example 1: A researcher conducts a randomized experimental study wherein she randomizes participants to receive a high-dosage weight loss pill, a low-dosage weight loss pill, or a placebo. She assesses the number of pounds lost from baseline to post-treatment
378
for the thre ...
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS11 .docxnovabroom
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS
11: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Testing the Difference Between Two Sample Means
Lightboard Lecture Video
· Independent t Tests
Time to Practice Video
· Chapter 11: Problem 5
Difficulty Scale
(A little longer than the previous chapter but basically the same kind of procedures and very similar questions. Not too hard, but you have to pay attention.)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Using the t test for independent means when appropriate
· Computing the observed t value
· Interpreting the t value and understanding what it means
· Computing the effect size for a t test for independent means
INTRODUCTION TO THE T TEST FOR INDEPENDENT SAMPLES
Even though eating disorders are recognized for their seriousness, little research has been done that compares the prevalence and intensity of symptoms across different cultures. John P. Sjostedt, John F. Schumaker, and S. S. Nathawat undertook this comparison with groups of 297 Australian and 249 Indian university students. Each student was measured on the Eating Attitudes Test and the Goldfarb Fear of Fat Scale. High scores on both measures indicate the presence of an eating disorder. The groups’ scores were compared with one another. On a comparison of means between the Indian and the Australian participants, Indian students scored higher on both of the tests, and this was due mainly to the scores of women. The results for the Eating Attitudes Test were t(544) = −4.19, p < .0001, and the results for the Goldfarb Fear of Fat Scale were t(544) = −7.64, p < .0001.
Now just what does all this mean? Read on.
Why was the t test for independent means used? Sjostedt and his colleagues were interested in finding out whether there was a difference in the average scores of one (or more) variable(s) between the two groups. The t test is called independent because the two groups were not related in any way. Each participant in the study was tested only once. The researchers applied a t test for independent means, arriving at the conclusion that for each of the outcome variables, the differences between the two groups were significant at or beyond the .0001 level. Such a small chance of a Type I error means that there is very little probability that the difference in scores between the two groups was due to chance and not something like group membership, in this case representing nationality, culture, or ethnicity.
Want to know more? Go online or to the library and find …
Sjostedt, J. P., Schumaker, J. F., & Nathawat, S. S. (1998). Eating disorders among Indian and Australian university students. Journal of Social Psychology, 138(3), 351–357.
LIGHTBOARD LECTURE VIDEO
Independent t Tests
THE PATH TO WISDOM AND KNOWLEDGE
Here’s how you can use Figure 11.1, the flowchart introduced in Chapter 9, to select the appropriate test statistic, the t test for independent means. Follow along the highlighted sequence of steps in Figure 1.
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS11 .docxhyacinthshackley2629
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS
11: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Testing the Difference Between Two Sample Means
Lightboard Lecture Video
· Independent t Tests
Time to Practice Video
· Chapter 11: Problem 5
Difficulty Scale
(A little longer than the previous chapter but basically the same kind of procedures and very similar questions. Not too hard, but you have to pay attention.)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Using the t test for independent means when appropriate
· Computing the observed t value
· Interpreting the t value and understanding what it means
· Computing the effect size for a t test for independent means
INTRODUCTION TO THE T TEST FOR INDEPENDENT SAMPLES
Even though eating disorders are recognized for their seriousness, little research has been done that compares the prevalence and intensity of symptoms across different cultures. John P. Sjostedt, John F. Schumaker, and S. S. Nathawat undertook this comparison with groups of 297 Australian and 249 Indian university students. Each student was measured on the Eating Attitudes Test and the Goldfarb Fear of Fat Scale. High scores on both measures indicate the presence of an eating disorder. The groups’ scores were compared with one another. On a comparison of means between the Indian and the Australian participants, Indian students scored higher on both of the tests, and this was due mainly to the scores of women. The results for the Eating Attitudes Test were t(544) = −4.19, p < .0001, and the results for the Goldfarb Fear of Fat Scale were t(544) = −7.64, p < .0001.
Now just what does all this mean? Read on.
Why was the t test for independent means used? Sjostedt and his colleagues were interested in finding out whether there was a difference in the average scores of one (or more) variable(s) between the two groups. The t test is called independent because the two groups were not related in any way. Each participant in the study was tested only once. The researchers applied a t test for independent means, arriving at the conclusion that for each of the outcome variables, the differences between the two groups were significant at or beyond the .0001 level. Such a small chance of a Type I error means that there is very little probability that the difference in scores between the two groups was due to chance and not something like group membership, in this case representing nationality, culture, or ethnicity.
Want to know more? Go online or to the library and find …
Sjostedt, J. P., Schumaker, J. F., & Nathawat, S. S. (1998). Eating disorders among Indian and Australian university students. Journal of Social Psychology, 138(3), 351–357.
LIGHTBOARD LECTURE VIDEO
Independent t Tests
THE PATH TO WISDOM AND KNOWLEDGE
Here’s how you can use Figure 11.1, the flowchart introduced in Chapter 9, to select the appropriate test statistic, the t test for independent means. Follow along the highlighted sequence of steps in Figure 1.
When you are working on the Inferential Statistics Paper I want yo.docxalanfhall8953
When you are working on the Inferential Statistics Paper I want you to format your paper with the following information
I. Introduction – What are inferential statistics and what is the research problem and hypothesis of the article?
II. Methods – Who are the subjects and variables within the article?
III. Results – What is the statistical analysis used, why were these tests chosen? What were the results of these tests and what do they mean?
IV. Discussion – What were the strengths of this article? What would you have done differently in terms of variables and statistical analysis? Why?
V. Conclusion – Reiterate the introduction and include relevant information that answers the questions regarding the hypothesis.
`
Read: Chapter 3 and 4 of Statistics for the Behavioral and Social Sciences.
Participate in One discussion.
Discussion 1 –Standard Normal Distribution– This allows you to look at any data set into the standard distribution form.
Quiz – Hypothesis testing
Submit your Inferential Statics Article Critique – Read Differential Effects of a Body Image Exposure Session on Smoking Urge Between Physically Active and Sedentary Female Smokers. What is the research question and hypothesis? Identify what variables were present, what inferential statistics were used and why, and if proper research methods were used. See grading rubric for full details.
Discussion Post Expectations:
Your initial post (your answer) is due by Day 3 (Thursday) of this week for Discussion 1.
When grading the Standard Normative Distribution discussion I will be looking for your answer to contain:
Week 2 Discussion 1 Board Rubric
Earned
Weight
Content Criteria
0.5
Student identifies and defines what Standard Normative Distribution (SND) is.
Student explains why it is needed to use a SND to compare two data sets.
0.5
Student identifies the purpose of a z-score in a SND.
0.5
Student identifies the purpose of a percentage in a SND.
0.25
Student explains whether a z-score or a percentage does a better job of identifying proportion of a SND.
0.25
The student responds to at least two classmates’ initial posts by Day 7.
1
Student uses correct spelling, grammar and sentence structure.
2
5
Grading - The discussions are both worth a total of 5 points. The breakdown of the grading for this week’s assignment (per discussion assignment) will be as follows:
Posting your answer by the due date (Day 3, Thursday) is worth 4 points. These five points will be based on the information outlined within the Discussion Assignment Expectations. Content will be worth 2 points and format; spelling and grammar will be worth 2 points.
Responding to two of your classmates (for each assignment) is worth 1 point. The answers must be substantive and go beyond “I agree” or “Good job” to qualify for this point.
Intellectual Elaboration:
In Wee.
RUNNING HEAD ONE WAY ANOVA1ONE WAY ANOVA .docxtoltonkendal
RUNNING HEAD: ONE WAY ANOVA 1
ONE WAY ANOVA 8
One-Way ANOVA
Stacy Hernandez
PSY7620
Dr. Lorie Fernandez
Capella University
Data Analysis and Application (DAA)
The one way ANOVA is used to determine whether there are any significant differences between the means of two or more independent groups. In this sample, the file grades.sav is used with section (independent variable) and quiz3 (dependent variable).
Data File Description
1. The one way ANOVA is used to determine whether there are any significant differences between the means of two or more independent groups.
2. In this sample, the file grades.sav is used with section (independent variable) and quiz3 (dependent variable).
3. The sample size (N) is 105.
Testing Assumptions
The dependent variable, quiz3, is measured at the interval or ratio level (meaning continuous). The dependent variable (quiz3) in this case, is therefore continuous since it ranges from one to 10. The independent variable (section) should consist of two or more categorical independent groups. In this case, the independent variable (section), has three groups, therefore it meets this assumption. There should be independence of observation, meaning that there is no relationship between the observations in each group or between the groups themselves. There should be no significant outliers, although there are single data points within the data that do not follow a normal pattern. Therefore, the outliers found will a negative effect on the one-way ANOVA, reducing the validity of the results.
Note the above boxplot indicates outliers in section two, with the id of 21.
The dependent variable (quiz3) should approximately have a normal distribution for each category of the independent variable (section). The null hypothesis is that the data is of a normal distribution, that the mean (average value of the dependent variable) is the same for all groups.
Ho – the observed distribution fits the normal distribution.
The alternative hypothesis is that the data does not have a normal distribution; the average is not the same for all groups.
Ha – the observed distribution does not fit the normal distribution.
It is observed that the data is not normally distributed. Most sections have quiz3 values between five and nine note this is a visual estimate. Note that the largest group also has the largest value of quiz3. The statistics from the histogram of quiz3 reveal that the Mean is 8.05; the Standard Deviation is 2.322, with a total number N of 105.
Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Deviation
Skewness
Kurtosis
Statistic
Statistic
Statistic
Statistic
Statistic
Statistic
Std. Error
Statistic
Std. Error
quiz3
105
0
10
8.05
2.322
-1.177
.236
.805
.467
section
105
1
3
2.00
.797
.000
.236
-1.419
.467
Valid N
105
When looking at skewness, for a perfectly normal and symmetrical distribution, it has a value of zero (Warner, 20 ...
Overview of Multivariate Statistical MethodsThomasUttaro1
This is an overview of advanced multivariate statistical methods which have become very relevant in many domains over the last few decades. These methods are powerful and can exploit the massive datasets implemented today in meaningful ways. Typically analytics platforms do not deploy these statistical methods, in favor of straightforward metrics and machine learning, and thus they are often overlooked. Additional references are available as documented.
Similar to Section 1 Data File DescriptionThe fictional data represents a te.docx (20)
Issues Identify at least seven issues you see in the case1..docxbagotjesusa
Issues: Identify at least seven issues you see in the case
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
What is the Key issue you see in the case: __________________________
What facts pertain to the case: Identify at least three important facts that pertain to the case
1.
2.
3.
4.
5.
What assumptions do you plan to make in your analysis: None is an acceptable answer
1.
2.
3
What people and organizations may have an impact on the case: There should be at least five.
1.
2.
3.
4.
5.
6.
7.
8.
9.
You are writing the case from the perspective of which person or organization:______________
What tools of Analysis would you use in this case: You only need to identify them and explain what information each will give you that you feel is important.
Based upon the above information – provide three alternatives
Alternative 1 is the Status Quo or to do nothing different that the current situation.
Identify at least three arguments in favor and three against this approach
Pros
1.
2.
3.
4.
5.
Cons
1.
2.
3.
4.
5.
Alternative 2 ____________________________________________________
Identify at least three arguments in favor and three against this approach
Pros
1.
2.
3.
4.
5.
Cons
1.
2.
3.
4.
5.
Alternative 3 ______________________________________________
Identify at least three arguments in favor and three against this approach
Pros
1.
2.
3.
4.
5.
Cons
1.
2.
3.
4.
5.
Given the information above select your recommended alternative and explain why you feel it is the best alternative: This should take three to five paragraphs and be based upon the information presented in your case.
.
Issues and disagreements between management and employees lead.docxbagotjesusa
Issues and disagreements between management and employees lead to formation of labor unions. Over the decades, the role of labor unions has been interpreted in various ways by employees across the globe.
What are some of the reasons employees join labor unions?
Did you ever belong to a labor union? If you did, do you think union membership benefited you?
If you've never belonged to a union, do you think it would have benefited you in your current or past employment? Why or why not?
.
ISSA Journal September 2008Article Title Article Author.docxbagotjesusa
ISSA Journal | September 2008Article Title | Article Author
1�1�
ISSA The Global Voice of Information Security
Extending the McCumber Cube
to Model Network Defense
By Sean M. Price – ISSA member Northern Virginia, USA chapter
This article proposes an extension to the McCumber
Cube information security model to determine the best
countermeasures to achieve a desired security goal.
Confidentiality, integrity, and availability are the se-curity services of a system. In other words they are the security goals of system defense, intangible at-
tributes� providing assurances for the information protected.
Each service is realized when the appropriate countermea-
sures for a given information state are in place. But, it is not
enough to select countermeasures ad hoc. Countermeasures
should be selected to defend a system and its information
against specific types of attacks. When attacks against partic-
ular information states are considered, the necessary coun-
termeasures can be selected to achieve the desired security
service or goal. This article proposes an extension to the Mc-
Cumber Cube information security model as a way for the
security practitioner to consider the best countermeasures to
achieve the desired security goal.
Security models
Models are useful tools to help understand complex topics. A
well-developed model can often be represented graphically,
allowing a deeper understanding of the relationships of the
components that make the whole. A formal security model
is broadly applicable and rigorously developed using formal
methods.2 In contrast, an informal model is considered lack-
ing one or both of these qualities. There are a variety of in-
formal models in the information security world which are
regularly used by security practitioners to understand basic
information and concepts.
� Security goals often lack explicit definitions and are difficult to quantify. They are
usually based on policies with broad interpretations and tend to be qualitative. It is
true that security goals emerge from the confluence of information states and coun-
termeasures which have measurable attributes. But, the subjective nature of security
goals combined with informal modeling characterizes their attributes as intangible.
2 P. T. Devanbu and S. Stubblebine, “Software Engineering for Security: A Roadmap,”
Proceedings of the Conference on The Future of Software Engineering (2000), 227-239.
One such informal model is the generally accepted risk as-
sessment framework. This model is used to assess risk by
estimating asset values, vulnerabilities, threats with their
likelihood of exploiting a vulnerability, and losses. Figure �
illustrates this model. Note that this commonly used model
requires a substantial amount of estimating on the part of
the risk assessment participants. This is problematic when
reliable estimates cannot be obtained. Another problem with
this model is that it does not guide th.
ISOL 536Security Architecture and DesignThreat Modeling.docxbagotjesusa
ISOL 536
Security Architecture and Design
Threat Modeling
Session 6a
“Processing Threats”
Agenda
• When to find threats
• Playing chess
• How to approach software
• Tracking threats and assumptions
• Customer/vendor
• The API threat model
• Reading: Chapter 7
When to Find Threats
• Start at the beginning of your project
– Create a model of what you’re building
– Do a first pass for threats
• Dig deep as you work through features
– Think about how threats apply to your mitigations
• Check your design & model matches as you
get close to shipping
Attackers Respond to Your Defenses
Playing Chess
• The ideal attacker will follow the road you
defend
– Ideal attackers are like spherical cows — they’re a
useful model for some things
• Real attackers will go around your defenses
• Your defenses need to be broad and deep
“Orders of Mitigation”
Order Threat Mitigation
1st Window smashing Reinforced glass
2nd Window smashing Alarm
3rd Cut alarm wire Heartbeat signal
4th Fake heartbeat Cryptographic signal integrity
By Example:
• Thus window smashing is a first order threat, cutting
alarm wire, a third-order threat
• Easy to get stuck arguing about orders
• Are both stronger glass & alarms 1st order
mitigations? (Who cares?!)
• Focus on the concept of interplay between
mitigations & further attacks
How to Approach Software
• Depth first
– The most fun and “instinctual”
– Keep following threats to see where they go
– Can be useful skill development, promoting “flow”
• Breadth first
– The most conservative use of time
• Best when time is limited
– Most likely to result in good coverage
Tracking Threats and Assumptions
• There are an infinite number of ways to
structure this
• Use the one that works reliably for you
• (Hope doesn’t work reliably)
Example Threat Tracking Tables
Diagram Element Threat Type Threat Bug ID
Data flow #4, web
server to business
logic
Tampering Add orders without
payment checks
4553 “Need
integrity controls on
channel”
Info disclosure Payment
instruments sent in
clear
4554 “need crypto”
#PCI
Threat Type Diagram Element(s) Threat Bug ID
Tampering Web browser Attacker modifies
our JavaScript order
checking
4556 “Add order-
checking logic to
server”
Data flow #2 from
browser to server
Failure to
authenticate
4557 “Add enforce
HTTPS everywhere”
Both are fine, help you iterate over diagrams in different ways
Example Assumption Tracking
Assumption Impact if it’s
wrong
Who to talk
to
Who’s
following up
Follow-up
by date
Bug #
It’s ok to
ignore
denial of
service
within the
data center
Availability
will be
below spec
Alice Bob April 15 4555
• Impact is sometimes so obvious it’s not worth filling out
• Who to talk to is not always obvious, it’s ok to start out blank
• Tracking assumptions in bugs helps you not lose track
• Treat the assumption as a bug – you need to resolve it
The Customer/Vendor Boundary
• There is always.
ISOL 533 Project Part 1OverviewWrite paper in sections.docxbagotjesusa
ISOL 533 Project Part 1
Overview
Write paper in sections
Understand the company
Find similar situations
Research and apply possible solutions
Research and find other issues
Health network inc
You are an Information Technology (IT) intern
Health Network Inc.
Headquartered in Minneapolis, Minnesota
Two other locations
Portland Oregon
Arlington Virginia
Over 600 employees
$500 million USD annual revenue
Data centers
Each location is near a data center
Managed by a third-party vendor
Production centers located at the data centers
Health network’s Three products
HNetExchange
Handles secure electronic medical messages between
Large customers such as hospitals and
Small customers such as clinics
HNetPay
Web Portal to support secure payments
Accepts various payment methods
HNetConnect
Allows customers to find Doctors
Contains profiles of doctors, clinics and patients
Health networks IT network
Three corporate data centers
Over 1000 data severs
650 corporate laptops
Other mobile devices
Management request
Current risk assessment outdated
Your assignment is to create a new one
Additional threats may be found during re-evaluation
No budget has been set on the project
Threats identified
Loss of company data due to hardware being removed from production systems
Loss of company information on lost or stolen company-owned assets, such as mobile devices and laptops
Loss of customers due to production outages caused by various events, such as natural disasters, change management, unstable software, and so on
Internet threats due to company products being accessible on the Internet
Insider threats
Changes in regulatory landscape that may impact operations
Part 1 project assignment
Conduct a risk assessment based on the information from this presentation
Write a 5-page paper properly APA formatted
Your paper should include
The Scope of the risk assessment i.e. assets, people, processes, and technologies
Tools used to conduct the risk assessment
Risk assessment findings
Business Impact Analysis
.
Is the United States of America a democracyDetailed Outline.docxbagotjesusa
Is the United States of America a democracy?
Detailed Outline:
-Introduction (2-3 Paragraphs):
Define and discuss the criteria for democracy.
What does a country need to be democratic?
-Thesis Statement (1 Paragraph):
Clearly state whether or not you think America is a democracy. Briefly preview the three pieces of evidence you are going to use. Your thesis statement is your argument. It must be clear and strongly stated so I know what you are arguing.
-Supporting Evidence 1 (1-3 Paragraphs)
Using Freedom House’s 2021 (2020 if 21 is not available)analysis of the U.S., support your argument regarding democracy in the U.S analysis of the U.S., support your argument regarding democracy in the U.S.
Supporting Evidence 2 (1-3 Paragraphs)
Choose a news article and explain the event covered in the article and how it
supports your argument.
Supporting Evidence 3 (1-3 Paragraphs)
Choose another news article
-Conclusion (1-2 Paragraphs)
Summarize your supporting evidence and how it supports your overall argument. This should include a brief discussion about how the other argument could be right
Citations: You will need outside sources for this paper. All sources must be properly cited. This means that the sources need to be parenthetically cited in the text of the paper and need to be included in a bibliography page. You are not allowed to use any user edit web sites (Wikipedia, Yahoo Answers, Ask.com, etc.) or social media as sources
4-5 papers
.
Islamic Profession of Faith (There is no God but God and Muhammad is.docxbagotjesusa
Islamic Profession of Faith (There is no God but God and Muhammad is his prophet.)
1. [contextualize] How are they a reflection of the time and culture which produced them?
2. [evaluate] What were the implications of these beliefs and values during the Middle Ages?
3. [compare] How do the beliefs and values of these cultures compare to your own?
.
IS-365 Writing Rubric Last updated January 15, 2018 .docxbagotjesusa
IS-365 Writing Rubric
Last updated: January 15, 2018
Student:
Score (out of 50):
General Comments:
Other comments are embedded in the document.
Criterion <- Higher - Quality - Lower ->
Persuasiveness The reader is
compelled by solid
critical reasoning,
appropriate usage of
sources, and
consideration of
alternative
viewpoints.
The document is
logical and coherent
enough that the
reader can accept its
points and
conclusions
Gaps in logic and
uncritical review of
sources cause the
reader to have some
doubts about the
points made by the
document, or
whether they’re
relevant to the
question asked.
The reader is unsure
of what the document
is trying to
communicate, or is
wholly unconvinced
by its arguments
Not
applicable
Evidence and support Exceptional use of
authoritative and
relevant sources,
properly cited,
providing strong
support of the
document’s points
Sufficient relevant
and authoritative
sources give
confidence that the
document is based
on adequate
research
Sources are
insufficient in
number, not
authoritative, not
relevant, or
improperly cited
No sources are used,
undermining the
document’s
foundations
Not
applicable
Writing Word choices, flow
of logic, and
sentence and
paragraph structure
engage the reader,
making for a
pleasurable
experience
Writing is clear and
adequately fulfills
the document’s
purpose
Some issues with
word choice and
sentence and
paragraph structure
interfere with the
conveyance of the
document’s ideas
Frequent questionable
choices in writing
make it difficult to
read and understand
Not
applicable
Language Essentially free of
language errors
Minor errors in
grammar,
punctuation, or
spelling
Noticeable language
errors that detract
from the readability
of the document
Significant language
errors that call the
credibility of the
document into
question
Not
applicable
Formatting (heading
styles, fonts, margins,
white space, tables
and graphics)
Professional and
consistent formatting
that enhances
readability.
Appropriate use of
tables and graphics.
Generally acceptable
formatting choices.
Some missed
opportunities for
displaying data via
tables or graphics.
Inconsistent or
questionable
formatting choices
that detract from the
document’s
readability
Critical formatting
issues that make the
document
unprofessional-
looking
Not
applicable
Page 1
Page 1
Page 2
(Name deleted)
IS-365
Art Fifer
2/17/2017
Technical Documents for Varying Audiences
In this paper, I’ll be exploring the differences in presenting technical communications to audiences of varying knowledge. The topic of these two general summaries will be the manner in which computers connect to each other, including summaries of several communication protocols, how information traverses the network, and how it arrives at its destination and is read by th.
ISAS 600 – Database Project Phase III RubricAs the final ste.docxbagotjesusa
ISAS 600 – Database Project Phase III Rubric
As the final step to your proposed database, you submitted your Project Plan. This document should communicate how you intend to complete the project. Include timelines and resources required.
Area
Does not meet expectations
Meets expectations
Exceeds expectations
A. Analysis - how will you determine the needs of the database
Did not identify appropriate plan for analysis phase
Identified appropriate plan for analysis phase
Identified appropriate plan for analysis phase and included additional content
Design - what process will you use to design the database (tables, forms, queries, reports)
Did not sufficiently identify detail on the appropriate process for design phase
Identified appropriate process for design phase
Identified appropriate process for design phase and included additional detail
Prototype/End user feedback - Will you show users a prototype before building the system?
Did not sufficiently identify method for feedback and prototypes during building of the system
Identified method for feedback and prototypes during building of the system
Identified method for feedback and prototypes during building of the system and provided additional detail
Coding - what process will you use to build the database?
Did not sufficiently identify appropriate process for coding the database
Identified appropriate process for coding the database
Identified appropriate process for coding the database and provided additional detail.
Testing - How will you test it?
to build the database?
Did not sufficiently identify appropriate process for testing the database
Identified appropriate process for testing the database
Identified appropriate process for testing the database and provided additional detail.
User Acceptance - describe the final step of determining if you met the user's needs?
Did not sufficiently identify an appropriate process for User Acceptance phase - How to determine if the database meets user’s needs.
Identified appropriate process for User Acceptance phase - How to determine if the database meets user’s needs.
Identified appropriate process for User Acceptance phase - How to determine if the database meets user’s needs. Answer provided additional detail
Training - what is the plan for training end users?
Did not identify appropriate detail for training plan
Identified appropriate detail for training plan
Identified appropriate detail for a training plan and provided additional detail.
Project close out - what steps will you take to finalize the project?
Did not sufficiently identify appropriate steps for closing out the project
Identified appropriate steps for closing out the project
Identified appropriate steps for closing out the project and provided additional detail.
Entity Relationship Diagram1
ERD:
Normalization:
1NF:
For the 1st NF we will have to check the tables’ attributes, like there must not be any multivalued attribute, if there is any multivalued at.
Is teenage pregnancy a social problem How does this topic reflect.docxbagotjesusa
Is teenage pregnancy a social problem? How does this topic reflect the social construction of problems? How does social location impact if you view this as a social problem?
Explain why media representation of social problems is an important issue using the example of teenage pregnancy. What is an example of a problematic representation? Does this vary across race, ethnicity, religion, socioeconomic status and gender?
.
Is Texas so conservative- (at least for the time being)- as many pun.docxbagotjesusa
Is Texas so conservative- (at least for the time being)- as many pundits and observers claim? Or is that just an opinion not supported by analysis and facts? Not only does Texas vote Republican in many elections but has done so for many years. It is also the birthplace of the so-called Tea Party movement and of Ron Paul's campaigns for president. Texas also appears to espouse conservative approaches to government and to issues. You will need to define in a concrete and operational way what conservative means as conservative is more than voting behavior or party affiliation.
Texas is the 2nd largest state in population compared to California and.like California made up of many differing migrant and immigrant groups. Texas like California was also part of Northern Mexico. but Texas is very, very different from California in voting behavior and positions on social issues. Why? Texas and California are good comparisons or are they? Provide explanations of the differences and similarities in this ideological context
Texas was once "Democratic" but even that was not really the case in terms of either past or current Democratic ideals and goals but a historic reaction to the consequences of the civil war and the fact that Texas was on the losing side in that war and of the attempt to defend agrarian interests in the form of slavery.. Being Democratic from post civil war to the middle of the 20th century in part meant for decades being in favor of inequality for minorities and defenders in spirit, if not in fact, of slavery.net
So Texas was never "Democratic" and never a more liberal interpretation of reality but a reflection of conservative thought and a particular view of individualistic man.
Is Texas conservative and why? ( you will need a social, cultural, historical and economic analysis here
with supporting evidence)?
? Need much more than opinions here.
.
Irreplaceable Personal Objects and Cultural IdentityThink of .docxbagotjesusa
Irreplaceable: Personal Objects and Cultural Identity
Think of a
personal object
that is
irreplaceable
to you.
Please answer the following:
1. Describe the item and tell a brief story, memory, or ritual related to the item.
2. How does this possession influence your identity?
3. How does this item represent your cultural identity?
4. How is your selection of this item influenced by your identity and culture?
Instructions:
please answer all 4 questions accordingly. Each answer should have the question re-typed following the answer. A minimum of 500 words in all excluding the re-typed questions. No reference is needed.
.
IRB is an important step in research. State the required components .docxbagotjesusa
IRB is an important step in research. State the required components one should look for in a project to determine if IRB submission is needed. Discuss an example of a research study found in one of your literature review articles that needed IRB approval. Specifically, describe why IRB approval was needed in this instance.
.
irem.org/jpm | jpm® | 47
AND
REWARD
RISK
>>
BY KRISTIN GUNDERSON HUNT
THE FIGHT TO FILL VACANT COMMERCIAL REAL ESTATE SPACE IN RECENT YEARS
HAS FORCED REAL ESTATE OWNERS AND MANAGERS TO CONSIDER NEW USES
FOR THEIR PROPERTIES—EVEN IF THEY REQUIRE TAKING ADDITIONAL RISKS.
especially vacancies,” said Janice
Ochenkowski, managing director
for Jones Lang LaSalle and the com-
mercial real estate firm’s director of
global risk management in Chicago.
“But property owners and manag-
ers have been very creative in how
to use their existing facilities.”
Traditional retail stores have been
transformed into everything from
medical office space and churches
to fitness centers and breweries. In
addition, special events and pop-
up stores are more commonplace;
traditional office spaces have been
converted to daycare centers; in-
dustrial warehouses are being used
as practice facilities for youth base-
ball teams; and the list goes on.
“From a risk management per-
spective, these new uses can bring
new challenges,” Ochenkowski said.
“However, it is the primary goal
of the risk manager to support the
business, which means we need to
be more creative in the way we deal
with these risks.”
DOESN’T MEAN YOU HAVE TO WALK AWAY.”–JANICE OCHENKOWSKI, JONES
LANG LASAL
LE
DO THE ASSESSMENT HONESTLY. JUST BECAUSE THERE IS A HI
GHER RISK
“DON’T BE AFRAID TO THINK ABOUT WHAT THE RISKS ARE.
the tough economy has resulted in a lot of challenges—“
DUE DILIGENCE
The risks associated with new-use tenants are as varied as the tenants them-
selves.
First and foremost, certain tenants could present additional life safety
risks, said Jeffrey Shearman, a Pittsburgh-based senior risk engineering con-
sultant and real estate industry practice leader for commercial insurance
provider, Zurich.
For example, restaurant tenants create increased exposure to fire; church
and/or educational institutions might spur egress concerns because they en-
courage large gatherings in spaces formerly used for different occupancy;
and hazardous waste can be a risk with some medical tenants.
“You have to recognize that certain types of work are going to create cer-
tain types of hazards,” Shearman said.
Beyond life safety risks, certain tenants might be more susceptible than
previous tenants to codes and regulations imposed by state or federal laws,
such as licensing regulations for daycares or American Disabilities Act re-
quirements for medical tenants, said Pat Pollan, CPM, principal at Pollan
Hausman Real Estate Services in Houston.
New-use tenant risks don’t stop there: financial risks also exist. Replac-
ing a unique tenant with a similar occupant after the lease expires can be
difficult—a particular concern if a lot of money was spent customizing the
space for an alternative use.
“It’s not just the risk of liability, it’s the risk of the tenant going out of busi-
ness and losing any money you put into the tenant, or its space, .
IoT References:
https://www.techrepublic.com/article/how-to-secure-your-iot-devices-from-botnets-and-other-threats/
https://www.peerbits.com/blog/biggest-iot-security-challenges.html
https://www.bankinfosecurity.asia/securing-iot-devices-challenges-a-11138
https://www.sumologic.com/blog/iot-security/
https://news.ihsmarkit.com/press-release/number-connected-iot-devices-will-surge-125-billion-2030-ihs-markit-says
https://cdn.ihs.com/www/pdf/IoT_ebook.pdf
https://go.armis.com/hubfs/Buyers%E2%80%99%20Guide%20to%20IoT%20Security%20-Final.pdf
https://www.techrepublic.com/article/smart-farming-how-iot-robotics-and-ai-are-tackling-one-of-the-biggest-problems-of-the-century/
Video Resources:What is the Internet of Things (IoT) and how can we secure it?
https://www.youtube.com/watch?v=H_X6IP1-NDc
What is the problem with IoT security? - Gary explains
https://www.youtube.com/watch?v=D3yrk4TaIQQ
Classmate 1
The Rise of the Republican Party
The Republican Party was formed due to a split in the Whig Party. The anti-slavery
“Conscience Whigs” split from the pro-slavery “Cotton Whigs”. Some anti-slavery Whigs joined
the American “Know-Nothing” Party, while the remainder joined with independent Democrats
and Free-Soilers to form a new party, the Republicans. The initial members stood for one
principle: the exclusion of slavery from the western territories (Shi, p. 462). Knowing the
Republicans ideology, we will look at how the events leading up to the Kansas-Nebraska Act led
to greater political division that eventually caused the formation of the Republican Party and it’s
rise to the presidency in 1860.
In the 1850’s, America was becoming increasingly divided between those for and against
slavery. The Compromise of 1850 had temporarily appeased both sides by admitting California
as a free state, allowing no slavery restrictions in New Mexico and Utah, paying Texas,
abolishing slave trade but no slavery in the District of Columbia, establishing the Fugitive Slave
Act, and denying congress authority to interfere with interstate slave trade (Shi, p. 457). This
Fugitive Slave Act was highly contested, although very few slaves were returned to the south
under this Act. In fact, it ended up uniting anti-slavery people, more than aiding the South. It was
during this time that Uncle Tom’s Cabin was written, selling more than a million copies
worldwide and detailing the harsh brutality of slavery (Shi, p. 460-461).
In the mid-1850’s, the Kansas-Nebraska Act was passed. The main reason for it was to the
settle the vast territory west of Missouri and Iowa, and to create a transcontinental railroad to
capitalize on Asian markets and goods. New territories brought up questions of whether slavery
would be allowed, with many supporting “popular sovereignty” where voters chose whether they
would have slavery or not. The issue here was that the 1820 Missouri Compromise had said there
would be no new slaver.
In two paragraphs, respond to the prompt below. Journal entries .docxbagotjesusa
In two paragraphs, respond to the prompt below. Journal entries must contain proper grammar, spelling and capitalization.
Consider the communication pattern within your family of origin. How does your family's conversation orientation (how open your family is to discuss a range of topics) and conformity orientation (how strongly your family reinforces the uniformity of attitudes, values and beliefs) affect your interactions with your partner? If you don't think there is any effect, explain your reasoning.
.
Investigative Statement AnalysisInitial statement given by Ted K.docxbagotjesusa
Investigative Statement Analysis
Initial statement given by Ted Kennedy in reference to the accident that occurred on July 18, 1969 in Chappaquiddick, Massachusetts.
Date:
October 30, 2007
Analyst Comments:
Narrative Balance: The Prologue begins with sentence #1 and ends with sentence #3. The Central Issue begins with sentence #4 and ends with sentence #9. The Epilogue begins with sentence #10 and ends with sentence #14. Thus the breakdown is:
Prologue = 3 sentences
Central Issue = 6 sentences
Epilogue = 5 sentences
The narrative is somewhat unbalanced due to the short Prologue and thus can be considered to be possibly deceptive on its face. It is not unbalanced enough to say this conclusively.
Mean Length of Unit:
The narrative has 14 sentences and 237 words, thus giving a MLU of 16.9 rounded to 17. Thus any sentences 23 words or longer and any sentences 11 words or less can be considered deceptive on their face.
Structure of Analysis:
The actual sentences from the narrative are in bold italicized type. After each sentence are the number of words in the sentence, whether or not it is deceptive on its face, and the analyst’s comments. All of these will be in normal type.
1.
On July 18th, 1969, at approximately 11:15 P.M. in Chappaquiddick, Martha’s Vineyard, Massachusetts, I was driving my car on Main Street on my way to get the ferry back to Edgartown.
30 words – Deceptive on its face. There is no mention of the passenger in this sentence. All of the pronouns are singular. It is “my car” “on my way”, etc. When the passenger is mentioned later, it is almost an afterthought. The deception in this sentence may be the last part of the sentence where he relates why he was driving the car. He very well may have been driving for some reason other than to get the ferry. This would be an area to be further explored in an interview.
2.
I was unfamiliar with the road and turned right onto Dike Road, instead of bearing hard left on Main Street.
20 words. “I was unfamiliar with the road” is an explanatory phrase telling us why he ended up on Dike Road. The phrase “instead of bearing hard left on Main Street” is a strange way of phrasing. Most people would say something like “instead of staying on Main Street.”
3.
After proceeding for approximately one-half mile on Dike Road I descended a hill and came upon a narrow bridge.
20 words. There is nothing particularly deceptive about this sentence. The phrasing of the sentence is very formal. The phrasing is almost like a police type report or a legal/lawyer way of phrasing. It also appears that the phrase “came upon a narrow bridge” is almost a passive way of phrasing that indicates he was taken by surprise and had no control over what he was doing.
4.
The car went off the side of the bridge.
9 words – This sentence is deceptive on its face. This is the very first sentence of the Central Issue. It is interesting to note that four of the six s.
Investigating Happiness at College SNAPSHOT T.docxbagotjesusa
Investigating Happiness at College
SNAPSHOT:
TOPIC Either a specific group related to college or a factor within
college life that possibly affects a specified group of college
students or students in general.
PITCH Present your topic and your research question to the class—
shark tank! Sound too scary? How about guppy tank ?).
Tentative due date: 2/5 & 2/7
ESSAY 1 The prospectus and the annotated bibliography.
Tentative due date: 2/21
ESSAY 2 Change in your topic or conducting your own study
Tentative due date: 3/16
ESSAY 3 Argument about a specific controversy within your topic
Tentative due date: 4/6
ESSAY 4 Answers and argues your refined research question about the
importance of your topic.
Tentative due date: 4/24
♥ Rough drafts with reflections about what is working and not working and
WHY will be required for the prospectus and essays 2 and 3. The work
on the rough draft and the reflections will count toward your essay grade.
♥ Final reflections submitted the class period after you submit your final
draft for essays 2-4 will also count as part of your essay grade.
♥ You will upload your drafts on Moodle. You will be asked to identify the
portions of the sources you used and submit hard copies of your sources
in a folder or files of your sources online.
Investigating Happiness at College:
Some questions that will help you form your own research
questions:
● Is happiness a necessity or a perk in college life?
● What do the expectations of happiness and the pursuit of
happiness reveal about a specific college group, college
students in general, or another college-related group?
● Considering both on-campus factors and off-campus factors
(at least at first), what most influences your group’s
happiness (or unhappiness)?
● Is there one major factor (on campus or off campus) you
would want to investigate that affects students’ happiness?
● How do the expectations about happiness that society has in
general or a certain specific segment of society (for
instance, parents) has, relate to college or college students?
● How much do preconceived notions and expectations about
college life affect student happiness?
● Hard work is hard to enjoy. So how do students balance that
hard work with the .
Investigate Development Case Death with Dignity Physician-Assiste.docxbagotjesusa
Investigate Development Case: Death with Dignity / Physician-Assisted Suicide
MAKE A DECISION: Is Ben's decision making being affected by his depression?
Yes
No
Why? Give reasons for why you chose the way you did. Consider the following factors in your reasons:
The effects of depression on decision making
Other stresses in Ben's life contributing to his state of mind
Ben's current quality of life
The family's values and beliefs
Your own values and beliefs
Please see attachment
.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Section 1 Data File DescriptionThe fictional data represents a te.docx
1. Section 1: Data File Description
The fictional data represents a teacher's recording of student
demographics and performance on quizzes and a final exam
across three sections of the course. Each section consists of 35
students which totals to 105 students (sample size, N = 105).
The dataset has 21 variables but in this case only two variables
will be analyzed. These are: gender and gpa variables. The
gender variable is categorized as nominal since the numbers are
arbitrarily assigned to represent group membership. The ‘gpa’
variable belongs to the interval data group since it has a true
zero which is meaningful. Alternatively, gender could be
categorized as a categorical variable while ‘gpa’ ‘as a
continuous variable.
Section 2: Testing Assumptions
1. Articulate the assumptions of the statistical test.
Paste SPSS output that tests those assumptions and interpret
them. Properly integrate SPSS output wher1e appropriate. Do
not string all output together at the beginning of the section.
All statistical tests operate under a set of assumptions. For the t
test, there are three assumptions:
· The first assumption is independence of observations.
· The outcome variable Y is normally distributed.
· The variance of Y scores is approximately equal across groups
(homogeneity of variance assumption)
Figure 1: histogram of GPA
The histogram above shows that the variable is probably not
normally distributed. The bell shape is absent and two peaks are
evident.
Table 1: descriptives
Descriptives
Statistic
3. Maximum
4
Range
3
Interquartile Range
1
Skewness
-,052
,236
Kurtosis
-,811
,467
With reference to the table 1 above, the ‘GPA’ variable is in the
ideal range for skewness due to the fact that its absolute value
for skewness are is less than .50 (approximately symmetric).
The GPA variable is not ideal but acceptable since its kurtosis
value is greater than .50 but less than 1. This new information
gives mixed signals about the data being normal and only a
normality test could iron out the differences.
Table 2: Normality test
Tests of Normality
Kolmogorov-Smirnova
Shapiro-Wilk
4. Statistic
df
Sig.
Statistic
df
Sig.
GPA
,091
105
,033
,956
105
,001
a. Lilliefors Significance Correction
Looking at the table above, the p-value is less than 0.05.
Therefore, the null hypothesis is rejected and thus it can be
concluded that the variable is not normally distributed.
However, since the sample size is sufficiently large, one does
not need to worry about this violation. On the other hand,
Levene’s test provides a p =0.566 (table 3) meaning that the
null hypothesis should not be rejected. Thus the homogeneity of
variances assumption is not violated. It’s also assumed that
proper research procedures that maintain independence of
observations were followed. Two of the three assumptions are
met.Section 3: Research Question, Hypotheses, and Alpha Level
The research question for this study is whether a relationship
exists between the ‘Gender’ variable and the ‘GPA’ variable.
Does one particular gender dominate the other in terms of
academic performance (gpa)? The null hypothesis states that the
means of the ‘GPA’ based on gender groups do not differ
significantly whereas the alternative hypothesis states that there
is a significant difference in the two ‘GPA’ groups based on the
gender of the participants. The alternative hypothesis is non-
directional meaning that the GPA for males could be higher than
5. that for females or the GPA for females could be higher than
that for males. The alpha level to be used is the standard .05
level of significance.
Section 4: Interpretation
1. Paste SPSS output for an inferential statistic. Properly
integrate SPSS output where appropriate. Do not string all
output together at the beginning of the section.
2.
Table 3: Independent samples t test results
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
GPA
Equal variances assumed
6. ,331
,566
2,004
103
,048
,302
,151
,003
,601
Equal variances not assumed
1,994
83,974
,049
,302
,151
,001
,603
The test was carried out and indicated a p value 0f 0.048. This
means that we shall have to reject the null hypothesis. This
means that the two means are not equal.
The mean GPA for males and females differed significantly,
t(103) =2.004, p = .048 (two-tailed). Mean GPA for the gender
1 group ( M = 2.90, SD = 0.748) was about 0.302 different from
the mean GPA for the gender 2 ( M = 2.59, SD = 0.763). It
could be said that the GPA for gender 2 is lower than that of
GPA for gender 1. The effect size, as indexed by η^2 , was ;
this is a small effect. The 95% CI for the difference between
sample means, M 1 − M 2, had a lower bound of 0.003 and an
upper bound of 0.601. This technically means that the
difference between the two means cannot be zero since zero is
not included in the confidence interval. This is in line with
previous finding where the null hypothesis was rejected and it
7. had been concluded that a significant statistical difference
between the means was present.
Section 5: Conclusion
In conclusion, it is evident that a student’s previous
performance between gender 1 and gender 2 is different. Gender
1 had higher GPA than Gender 2. A major limitation of this test
is that one cannot compare more than two groups at a go.
Another test has to be used in conjunction or instead of the
independent samples t test.
Chapter 12 - DUMMY PREDICTOR VARIABLES IN
MULTIPLE REGRESSION
12.1 Research Situations Where Dummy Predictor Variables
Can Be Used
Previous examples of regression analysis have used scores on
quantitative X variables to predict scores on a quantitative Y
variable. However, it is possible to include group membership
or categorical predictor variables as predictors in regression
analysis. This can be done by creating dummy (dichotomous)
predictor variables to represent information about group
membership. A dummy or dichotomous predictor variable
provides yes/no information for questions about group
membership. For example, a simple dummy variable to
represent gender corresponds to the following question: Is the
participant female (0) or male (1)? Gender is an example of a
two-group categorical variable that can be represented by a
single dummy variable.
When we have more than two groups, we can use a set of
dummy variables to provide information about group
membership. For example, suppose that a study includes
members of k = 3 political party groups. The categorical
variable political party has the following scores: 1 = Democrat,
2 = Republican, and 3 = Independent. We might want to find out
whether mean scores on a quantitative measure of political
conservatism (Y) differ across these three groups. One way to
8. answer this question is to perform a one-way analysis of
variance (ANOVA) that compares mean conservatism (Y) across
the three political party groups. In this chapter, we will see that
we can also use regression analysis to evaluate how political
party membership is related to scores on political conservatism.
However, we should not set up a regression to predict scores on
conservatism from the multiple-group categorical variable
political party, with party membership coded 1 = Democrat, 2 =
Republican, and 3 = Independent. Multiple-group categorical
variables usually do not work well as predictors in regression,
because scores on a quantitative outcome variable, such as
“conservatism,” will not necessarily increase linearly with the
score on the categorical variable that provides information
about political party. The score values that represent political
party membership may not be rank ordered in a way that is
monotonically associated with changes in conservatism; as we
move from Group 1 = Democrat to Group 2 = Republican,
scores on conservatism may increase, but as we move from
Group 2 = Republican to Group 3 = Independent, conservatism
may decrease. Even if the scores that represent political party
membership are rank ordered in a way that is monotonically
associated with level of conservatism, the amount of change in
conservatism between Groups 1 and 2 may not be equal to the
amount of change in conservatism between Groups 2 and 3. In
other words, scores on a multiple-group categorical predictor
variable (such as political party coded 1 = Democrat, 2 =
Republican, and 3 = Independent) are not necessarily linearly
related to scores on quantitative variables.
If we want to use the categorical variable political party to
predict scores on a quantitative variable such as conservatism,
we need to represent the information about political party
membership in a different way. Instead of using one categorical
predictor variable with codes 1 = Democrat, 2 = Republican,
and 3 = Independent, we can create two dummy or dichotomous
predictor variables to represent information about political party
membership, and we can then use these two dummy variables as
9. predictors of conservatism scores in a regression. Political party
membership can be assessed by creating dummy variables
(denoted by D1 and D2) that correspond to two yes/no
questions. In this example, the first dummy variable D1
corresponds to the following question: Is the participant a
member of the Democratic Party? Coded 1 = yes, 0 = no. The
second dummy variable D2 corresponds to the following
question: Is the participant a member of the Republican Party?
Coded 1 = yes, 0 = no. We assume that group memberships for
individuals are mutually exclusive and exhaustive—that is, each
case belongs to only one of the three groups, and every case
belongs to one of the three groups identified by the categorical
variable. When these conditions are met, a third dummy
variable is not needed to identify the members of the third
group because, for Independents, the answers to the first two
questions that correspond to the dummy variables D1 and D2
would be no. In general, when we have k groups or categories, a
set of k − 1 dummy variables is sufficient to provide complete
information about group membership. Once we have represented
political party group membership by creating scores on two
dummy variables, we can set up a regression to predict scores
on conservatism (Y) from the scores on the two dummy
predictor variables D1 and D2:
In this chapter, we will see that the information about the
association between group membership (represented by D1 and
D2) and scores on the quantitative Y variable that can be
obtained from the regression analysis in Equation 12.1 is
equivalent to the information that can be obtained from a one-
way ANOVA that compares means on Y across groups or
categories. It is acceptable to use dichotomous predictor
variables in regression and correlation analysis. This works
because (as discussed in Chapter 8) a dichotomous categorical
variable has only two possible score values, and the only
possible relationship between scores on a dichotomous predictor
variable and a quantitative outcome variable is a linear one.
10. That is, as you move from a score of 0 = female on gender to a
score of 1 = male on gender, mean height or mean annual salary
may increase, decrease, or stay the same; any change that can be
observed across just two groups can be represented as linear.
Similarly, if we represent political party membership using two
dummy variables, each dummy variable represents a contrast
between the means of two groups; for example, the D1 dummy
variable can represent the difference in mean conservatism
between Democrats and Independents, and the D2 dummy
variable can represent the mean difference in conservatism
between Republicans and Independents.
This chapter uses empirical examples to demonstrate that
regression analyses that use dummy predictor variables (similar
to Equation 12.1) provide information that is equivalent to the
results of more familiar analyses for comparison of group means
(such as ANOVA). There are several reasons why it is useful to
consider dummy predictor variables as predictors in regression
analysis. First, the use of dummy variables as predictors in
regression provides a simple demonstration of the fundamental
equivalence between ANOVA and multiple regression; ANOVA
and regression are both special cases of a more general analysis
called the general linear model (GLM). Second, researchers
often want to include group membership variables (such as
gender) along with other predictors in a multiple regression.
Therefore, it is useful to examine examples of regression that
include dummy variables along with quantitative predictors.
The computational procedures for regression remain the same
when we include one or more dummy predictor variables. The
most striking difference between dummy variables and
quantitative variables is that the scores on dummy variables
usually have small integer values (such as 1, 0, and −1). The use
of small integers as codes simplifies the interpretation of the
regression coefficients associated with dummy variables. When
dummy variables are used as predictors in a multiple regression,
the b raw score slope coefficients provide information about
differences between group means. The specific group means
11. that are compared differ depending on the method of coding that
is used for dummy variables, as explained in the following
sections. Except for this difference in the interpretation of
regression coefficients, regression analysis remains essentially
the same when dummy predictor variables are included.
12.2 Empirical Example
The hypothetical data for this example are provided by a study
of predictors of annual salary in dollars for a group of N = 50
college faculty members; the complete data appear in Table
12.1. Predictor variables include the following: gender, coded 0
= female and 1 = male; years of job experience; college, coded
1 = Liberal Arts, 2 = Sciences, 3 = Business; and an overall
merit evaluation. Additional columns in the SPSS data
worksheet in Figure 12.1, such as D1, D2, E1, and E2, represent
alternative ways of coding group membership, which are
discussed later in this chapter. All subsequent analyses in this
chapter are based on the data in Table 12.1.
The first research question that can be asked using these data is
whether there is a significant difference in mean salary between
males and females (ignoring all other predictor variables). This
question could be addressed by conducting an independent
samples t test to compare male and female means on salary. In
this chapter, a one-way ANOVA is performed to compare mean
salary for male versus female faculty; then, salary is predicted
from gender by doing a regression analysis to predict salary
scores from a dummy variable that represents gender. The
examples presented in this chapter demonstrate that ANOVA
and regression analysis provide equivalent information about
gender differences in mean salary. Examples or demonstrations
such as the ones presented in this chapter do not constitute
formal mathematical proof. Mathematical statistics textbooks
provide formal mathematical proof of the equivalence of
ANOVA and regression analysis.
Table 12.1 Hypothetical Data for Salary and Predictors of
Salary for N = 50 College Faculty
12. NOTES: Salary, annual salary in thousands of dollars; years,
years of job experience; gender, dummy-coded gender (0 =
female and 1 = male); college, membership coded 1 = Liberal
Arts, 2 = Sciences, 3 = Business.
Figure 12.1 SPSS Data Worksheet for Hypothetical Faculty
Salary Study
The second question that will be addressed is whether there are
significant differences in salary across the three colleges. This
question will be addressed by doing a one-way ANOVA to
compare mean salary across the three college groups and by
using dummy variables that represent college group membership
as predictors in a regression. This example demonstrates that
membership in k groups can be represented by a set of (k − 1)
dummy variables.
12.3 Screening for Violations of Assumptions
When we use one or more dummy variables as predictors in
regression, the assumptions are essentially the same as for any
other regression analysis (and the assumptions for a one-way
ANOVA). As in other applications of ANOVA and regression,
scores on the outcome variable Y should be quantitative and
approximately normally distributed. If the Y outcome variable
is categorical, logistic regression analysis should be used
instead of linear regression; a brief introduction to binary
logistic regression is presented in Chapter 21. Potential
violations of the assumption of an approximately normal
distribution shape for the Y outcome variable can be assessed
by examining a histogram of scores on Y; the shape of this
distribution should be reasonably close to normal. As described
in Chapter 4, if there are extreme outliers or if the distribution
shape is drastically different from normal, it may be appropriate
to drop a few extreme scores, modify the value of a few extreme
scores, or, by using a data transformation such as the logarithm
13. of Y, make the distribution of Y more nearly normal.
The variance of Y scores should be fairly homogeneous across
groups—that is, across levels of the dummy variables. The F
tests used in ANOVA are fairly robust to violations of this
assumption, unless the numbers of scores in the groups are
small and/or unequal. When comparisons of means on Y across
multiple groups are made using the SPSS t test procedure, one-
way ANOVA, or GLM, the Levene test can be requested to
assess whether the homogeneity of variance is seriously
violated. SPSS multiple regression does not provide a formal
test of the assumption of homogeneity of variance across
groups. It is helpful to examine a graph of the distribution of
scores within each group (such as a boxplot) to assess visually
whether the scores of the Y outcome variable appear to have
fairly homogeneous variances across levels of each X dummy
predictor variable.
The issue of group size should also be considered in preliminary
data screening (i.e., How many people are there in each of the
groups represented by codes on the dummy variables?). For
optimum statistical power and greater robustness to violations
of assumptions, such as the homogeneity of variance
assumption, it is preferred that there are equal numbers of
scores in each group.1 The minimum number of scores within
each group should be large enough to provide a reasonably
accurate estimate of group means. For any groups that include
fewer than 10 or 20 scores, estimates of the group means may
have confidence intervals that are quite wide. The guidelines
about the minimum number of scores per group from Chapter 5
(on the independent samples t test) and Chapter 6 (between-
subjects [between-S] one-way ANOVA) can be used to judge
whether the numbers in each group that correspond to a dummy
variable, such as gender, are sufficiently large to yield
reasonable statistical power and reasonable robustness against
violations of assumptions.
For the hypothetical faculty salary data in Table 12.1, the
numbers of cases within the groups are (barely) adequate. There
14. were 20 female and 30 male faculty in the sample, of whom 22
were Liberal Arts faculty, 17 were Sciences faculty, and 11
were Business faculty. Larger group sizes are desirable in real-
world applications of dummy variable analysis; relatively small
numbers of cases were used in this example to make it easy for
students to verify computations, such as group means, by hand.
All the SPSS procedures that are used in this chapter, including
boxplot, scatter plot, Pearson correlation, one-way ANOVA,
and linear regression, have been introduced and discussed in
more detail in earlier chapters. Only the output from these
procedures appears in this chapter. For a review of the menu
selections and the SPSS dialog windows for any of these
procedures, refer to the chapter in which each analysis was first
introduced.
To assess possible violations of assumptions, the following
preliminary data screening was performed. A histogram was set
up to assess whether scores on the quantitative outcome variable
salary were reasonably normally distributed; this histogram
appears in Figure 12.2. Although the distribution of salary
values was multimodal, the salary scores did not show a
distribution shape that was drastically different from a normal
distribution. In real-life research situations, salary distributions
are often positively skewed, such that there is a long tail at the
upper end of the distribution (because there is usually no fixed
upper limit for salary) and a truncated tail at the lower end of
the distribution (because salary values cannot be lower than 0).
Skewed distributions of scores can sometimes be made more
nearly normal in shape by taking the base 10 or natural log of
the salary scores. Logarithmic transformation was judged
unnecessary for the artificial salary data that are used in the
example in this chapter. Also, there were no extreme outliers in
salary, as can be seen in Figure 12.2.
Figure 12.2 Histogram of Salary Scores
A boxplot of salary scores was set up to examine the
distribution of outcomes for the female and male groups (Figure
15. 12.3). A visual examination of this boxplot did not reveal any
extreme outliers within either group; median salary appeared to
be lower for females than for males. The boxplot suggested that
the variance of salary scores might be larger for males than for
females. This is a possible violation of the homogeneity of
variance assumption; in the one-way ANOVA presented in a
later section, the Levene test was requested to evaluate whether
this difference between salary variances for the female and male
groups was statistically significant (and the Levene test was not
significant).
Figure 12.4 shows a scatter plot of salary (on the Y axis) as a
function of years of job experience (on the X axis) with
different case markers for female and male faculty. The relation
between salary and years appears to be linear, and the slope that
predicts salary from years appears to be similar for the female
and male groups, although there appears to be a tendency for
females to have slightly lower salary scores than males at each
level of years of job experience; also, the range for years of
experience was smaller for females (0–15) than for males (0–
22). Furthermore, the variance of salary appears to be
reasonably homogeneous across years in the scatter plot that
appears in Figure 12.4, and there were no extreme bivariate
outliers. Subsequent regression analyses will provide us with
more specific information about gender and years as predictors
of salary. We will use a regression analysis that includes the
following predictors: years, a dummy variable to represent
gender, and a product of gender and years. The results of this
regression will help answer the following questions: Is there a
significant increase in predicted salary associated with years of
experience (when gender is statistically controlled)? Is there a
significant difference in predicted salary between males and
females (when years of experience is statistically controlled)?
Figure 12.3 Boxplot of Salary Scores for Female and Male
Groups
Figure 12.4 Scatter Plot of Salary by Years With Case Markers
16. for Gender
12.4 Issues in Planning a Study
Essentially, when we use a dummy variable as a predictor in a
regression, we have the same research situation as when we do a
t test or ANOVA (both analyses predict scores on the Y
outcome variable for two groups). When we use several dummy
variables as predictors in a regression, we have the same
research situation as in a one-way ANOVA (both analyses
compare means across several groups). Therefore, the issues
reviewed in planning studies that use t tests (in Chapter 5) and
studies that use one-way ANOVA (in Chapter 6) are also
relevant when we use a regression analysis as the method of
data analysis. To put it briefly, if the groups that are compared
received different “dosage” levels of some treatment variable,
the dosage levels need to be far enough apart to produce
detectable differences in outcomes. If the groups are formed on
the basis of participant characteristics (such as age), the groups
need to be far enough apart on these characteristics to yield
detectable differences in outcome. Other variables that might
create within-group variability in scores may need to be
experimentally or statistically controlled to reduce the
magnitude of error variance, as described in Chapters 5 and 6.
It is important to check that every group has a reasonable
minimum number of cases. If any group has fewer than 10
cases, the researcher may decide to combine that group with one
or more other groups (if it makes sense to do so) or exclude that
group from the analysis. The statistical power tables that
appeared in Chapters 5 and 6 can be used to assess the minimum
sample size needed per group to achieve reasonable levels of
statistical power.
12.5 Parameter Estimates and Significance Tests for
Regressions With Dummy Variables
The use of one or more dummy predictor variables in regression
17. analysis does not change any of the computations for multiple
regression described in Chapter 11. The estimates of b
coefficients, the t tests for the significance of individual b
coefficients, and the overall F test for the significance of the
entire regression equation are all computed using the methods
described in Chapter 11. Similarly, sr2 (the squared semipartial
correlation), an estimate of the proportion of variance uniquely
predictable from each dummy predictor variable, can be
calculated and interpreted for dummy predictors in a manner
similar to that described in Chapter 11. Confidence intervals for
each b coefficient are obtained using the methods described in
Chapter 11.
However, the interpretation of b coefficients when they are
associated with dummy-coded variables is slightly different
from the interpretation when they are associated with
continuous predictor variables. Depending on the method of
coding that is used, the b0 (intercept) coefficient may
correspond to the mean of one of the groups or to the grand
mean of Y. The bi coefficients for each dummy-coded predictor
variable may correspond to contrasts between group means or to
differences between group means and the grand mean.
12.6 Group Mean Comparisons Using One-Way Between-S
ANOVA12.6.1 Gender Differences in Mean Salary
A one-way between-S ANOVA was performed to assess whether
mean salary differed significantly between female and male
faculty. No other variables were taken into account in this
analysis. A test of the homogeneity of variance assumption was
requested (the Levene test).
The Levene test was not statistically significant: F(1, 48) =
2.81, p = .1. Thus, there was no statistically significant
difference between the variances of salary for the female and
male groups; the homogeneity of variance assumption was not
violated. The mean salary for males (Mmale) was 49.9 thousand
dollars per year; the mean salary for females (Mfemale) was
40.8 thousand dollars per year. The difference between mean
18. annual salary for males and females was statistically significant
at the conventional α = .05 level: F(1, 48) = 13.02, p = .001.
The difference between the means (Mmale − Mfemale) was +9.1
thousand dollars (i.e., on average, male faculty earned about 9.1
thousand dollars more per year than female faculty). The eta-
squared effect size for this gender difference was .21; in other
words, about 21% of the variance in salaries could be predicted
from gender. This corresponds to a large effect size (refer to
Table 5.2 for suggested verbal labels for values of η2 and R2).
This initial finding of a gender difference in mean salary is not
necessarily evidence of gender bias in salary levels. Within this
sample, there was a tendency for female faculty to have fewer
years of experience and for male faculty to have more years of
experience, as well as salary increases as a function of years of
experience. It is possible that the gender difference we see in
this ANOVA is partly or completely accounted for by
differences between males and females in years of experience.
Subsequent analyses will address this by examining whether
gender still predicts different levels of salary when “years of
experience” is statistically controlled by including it as a
second predictor of salary.
Note that when the numbers of cases in the groups are unequal,
there are two different ways in which a grand mean can be
calculated. An unweighted grand mean of salary can be obtained
by simply averaging male and female mean salary, ignoring
sample size. The unweighted grand mean is (40.8 + 49.9)/2 =
45.35. However, in many statistical analyses, the estimate of the
grand mean is weighted by sample size. The weighted grand
mean in this example is found as follows:
Figure 12.5 One-Way Between-S ANOVA: Mean Salary for
Females and Males
The grand mean for salary reported in the one-way ANOVA in
Figure 12.5 corresponds to the weighted grand mean. (This
weighted grand mean is equivalent to the sum of the 50
19. individual salary scores divided by the number of scores in the
sample.) In the regression analyses reported later in this
chapter, the version of the grand mean that appears in the
results corresponds to the unweighted grand mean.12.6.2
College Differences in Mean Salary
In a research situation that involves a categorical predictor
variable with more than two levels or groups and a quantitative
outcome variable, the most familiar approach to data analysis is
a one-way ANOVA. To evaluate whether mean salary level
differs for faculty across the three different colleges in the
hypothetical dataset in Table 12.1, we can conduct a between-S
one-way ANOVA using the SPSS ONEWAY procedure. The
variable college is coded 1 = Liberal Arts, 2 = Sciences, and 3 =
Business. The outcome variable, as in previous analyses, is
annual salary in thousands of dollars. Orthogonal contrasts
between colleges were also requested by entering custom
contrast coefficients. The results of this one-way ANOVA
appear in Figure 12.6.
The means on salary were as follows: 44.8 for faculty in Liberal
Arts, 44.7 in Sciences, and 51.6 in Business. The overall F for
this one-way ANOVA was not statistically significant: F(2, 47)
= 2.18, p = .125. The effect size, eta squared, was obtained by
taking the ratio SSbetween/SStotal; for this ANOVA, η2 = .085.
This is a medium effect. In this situation, if we want to use this
sample to make inferences about some larger population of
faculty, we would not have evidence that the proportion of
variance in salary that is predictable from college is
significantly different from 0. However, if we just want to
describe the strength of the association between college and
salary within this sample, we could say that, for this sample,
about 8.5% of the variance in salary was predictable from
college. The orthogonal contrasts that were requested made the
following comparison. For Contrast 1, the custom contrast
coefficients were +1, −1, and 0; this corresponds to a
comparison of the mean salaries between College 1 (Liberal
Arts) and College 2 (Sciences); this contrast was not
20. statistically significant: t(47) = .037, p = .97. For Contrast 2,
the custom contrast coefficients were +1, +1, and −2; this
corresponds to a comparison of the mean salary for Liberal Arts
and Sciences faculty combined, compared with the Business
faculty. This contrast was statistically significant: t(47) =
−2.082, p = .043. Business faculty had a significantly higher
mean salary than the two other colleges (Liberal Arts and
Sciences) combined.
Figure 12.6 One-Way Between-S ANOVA: Mean Salary Across
Colleges
The next sections show that regression analyses with dummy
predictor variables can be used to obtain the same information
about the differences between group means. Dummy variables
that represent group membership (such as female and male, or
Liberal Arts, Sciences, and Business colleges) can be used to
predict salary in regression analyses. We will see that the
information about differences among group means that can be
obtained by doing regression with dummy predictor variables is
equivalent to the information that we can obtain from one-way
ANOVA. In future chapters, both dummy variables and
quantitative variables are used as predictors in regression.
12.7 Three Methods of Coding for Dummy Variables
The three coding methods that are most often used for dummy
variables are given below (the details regarding these methods
are presented in subsequent sections along with empirical
examples):
1. Dummy coding of dummy variables
2. Effect coding of dummy variables
3. Orthogonal coding of dummy variables
In general, when we have k groups, we need only (k − 1)
dummy variables to represent information about group
21. membership. Most dummy variable codes can be understood as
answers to yes/no questions about group membership. For
example, to represent college group membership, we might
include a dummy variable D1 that corresponds to the question,
“Is this faculty member in Liberal Arts?” and code the
responses as 1 = yes, 0 = no. If there are k groups, a set of (k −
1) yes/no questions provides complete information about group
membership. For example, if a faculty member reports
responses of “no” to the questions, “Are you in Liberal Arts?”
and “Are you in Science?” and there are only three groups, then
that person must belong to the third group (in this example,
Group 3 is the Business college).
The difference between dummy coding and effect coding is in
the way in which codes are assigned for members of the last
group—that is, the group that does not correspond to an explicit
yes/no question about group membership. In dummy coding of
dummy variables, members of the last group receive a score of 0
on all the dummy variables. (In effect coding of dummy
variables, members of the last group are assigned scores of −1
on all the dummy variables.) This difference in codes results in
slightly different interpretations of the b coefficients in the
regression equation, as described in the subsequent sections of
this chapter.12.7.1 Regression With Dummy-Coded Dummy
Predictor Variables12.7.1.1 Two-Group Example With a
Dummy-Coded Dummy Variable
Suppose we want to predict salary (Y) from gender; gender is a
dummy-coded dummy variable with codes of 0 for female and 1
for male participants in the study. In a previous section, this
difference was evaluated by doing a one-way between-S
ANOVA to compare mean salary across female and male
groups. We can obtain equivalent information about the
magnitude of gender differences in salary from a regression
analysis that uses a dummy-coded variable to predict salary. For
this simple two-group case (prediction of salary from dummy-
coded gender), we can write a regression equation using gender
to predict salary (Y) as follows:
22. From Equation 12.2, we can work out two separate prediction
equations: one that makes predictions of Y for females and one
that makes predictions of Y for males. To do this, we substitute
the values of 0 (for females) and 1 (for males) into Equation
12.2 and simplify the expression to obtain these two different
equations:
These two equations tell us that the constant value b0 is the best
prediction of salary for females, and the constant value (b0 +
b1) is the best prediction of salary for males. This implies that
b0 = mean salary for females, b0 + b1 = mean salary for males,
and b1 = the difference between mean salary for the male versus
female groups. The slope coefficient b1 corresponds to the
difference in mean salary for males and females. If the b1 slope
is significantly different from 0, it implies that there is a
statistically significant difference in mean salary for males and
females.
The results of the regression in Figure 12.7 provide the
numerical estimates for the raw score regression coefficients
(b0 and b1) for this set of data:
Salary′ = 40.8 + 9.1 × Gender.
For females, with gender = 0, the predicted mean salary given
by this equation is 40.8 + 9.1 × 0 = 40.8. Note that this is the
same as the mean salary for females in the one-way ANOVA
output in Figure 12.5. For males, with gender = 1, the predicted
salary given by this equation is 40.8 + 9.1 = 49.9. Note that this
value is equal to the mean salary for males in the one-way
ANOVA in Figure 12.5.
The b1 coefficient in this regression was statistically
significant: t(48) = 3.61, p = .001. The F test reported in Figure
12.6 is equivalent to the square of the t test value for the null
hypothesis that b1 = 0 in Figure 12.7 (t = +3.608, t2 = 13.02).
Note also that the eta-squared effect size associated with the
ANOVA (η2 = .21) and the R2 effect size associated with the
23. regression were equal; in both analyses, about 21% of the
variance in salaries was predictable from gender.
When we use a dummy variable with codes of 0 and 1 to
represent membership in two groups, the value of the b0
intercept term in the regression equation is equivalent to the
mean of the group for which the dummy variable has a value of
0. The b1 “slope” coefficient represents the difference (or
contrast) between the means of the two groups. The slope, in
this case, represents the change in the mean level of Y when
you move from a code of 0 (female) to a code of 1 (male) on the
dummy predictor variable.
Figure 12.7 Regression to Predict Salary From Dummy-Coded
Gender
Figure 12.8 shows a scatter plot of salary scores (on the Y axis)
as a function of gender code (on the X axis). In this graph, the
intercept b0 corresponds to the mean on the dependent variable
(salary) for the group that had a dummy variable score of 0
(females). The slope, b1, corresponds to the difference between
the means for the two groups—that is, the change in the
predicted mean when you move from a code of 0 to a code of 1
on the dummy variable. That is, b1 = Mmale − Mfemale, the
change in salary when you move from a score of 0 (female) to a
score of 1 (male). The test of the statistical significance of b1 is
equivalent to the t test of the difference between the mean Y
values for the two groups represented by the dummy variable in
the regression.12.7.1.2 Multiple-Group Example With Dummy-
Coded Dummy Variables
When there are multiple groups (number of groups = k), group
membership can be represented by scores on a set of (k − 1)
dummy variables. Each dummy variable essentially represents a
yes/no question about group membership. In the preceding
example, there are k = 3 college groups in the faculty data. In
this example, we will use two dummy variables, denoted by D1
and D2, to represent information about college membership in a
regression analysis. D1 corresponds to the following question:
24. Is the faculty member from the Liberal Arts college? 1 = yes, 0
= no. D2 corresponds to the following question: Is the faculty
member from the Sciences college? 1 = yes, 0 = no. For dummy
coding, members of the last group receive a score of 0 on all the
dummy variables. In this example, faculty from the Business
college received scores of 0 on both the D1 and D2 dummy-
coded dummy variable. For the set of three college groups, the
dummy-coded dummy variables that provide information about
college group membership were coded as follows:
Figure 12.8 Graph for Regression to Predict Salary From
Dummy-Coded Gender (0 = Female, 1 = Male)
Now that we have created dummy variables that represent
information about membership in the three college groups as
scores on a set of dummy variables, mean salary can be
predicted from college groups by a regression analysis that uses
the dummy-coded dummy variables shown above as predictors:
The results of the regression (using dummy-coded dummy
variables to represent career group membership) are shown in
Figure 12.9. Note that the overall F reported for the regression
analysis in Figure 12.9 is identical to the overall F reported for
the one-way ANOVA in Figure 12.6, F (2, 47) = 2.17, p = .125,
and that the η2 for the one-way ANOVA is identical to the R2
for the regression (η2 = R2 = .085). Note also that the b0
coefficient in the regression results in Figure 12.9 (b0 =
constant = 51.55) equals the mean salary for the group that was
assigned score values of 0 on all the dummy variables (the mean
salary for the Business faculty was 51.55). Note also that the bi
coefficients for each of the two dummy variables represent the
difference between the mean of the corresponding group and the
mean of the comparison group whose codes were all 0; for
example, b1 = −6.73, which corresponds to the difference
between the mean salary of Group 1, Liberal Arts (M1 = 44.82)
and mean salary of the comparison group, Business (M3 =
25. 51.55); the value of the b2 coefficient (b2 = −6.84) corresponds
to the difference between mean salary for the Science (M =
44.71) and Business (M = 51.55) groups. This regression
analysis with dummy variables to represent college membership
provided information equivalent to a one-way ANOVA to
predict salary from college.
Figure 12.9 Regression to Predict Salary From Dummy-Coded
College Membership
12.7.2 Regression With Effect-Coded Dummy Predictor
Variables12.7.2.1 Two-Group Example With an Effect-Coded
Dummy Variable
We will now code the scores for gender slightly differently,
using a method called “effect coding of dummy variables.” In
effect coding of dummy variables, a score value of 1 is used to
represent a “yes” answer to a question about group membership;
membership in the group that does not correspond to a “yes”
answer on any of the group membership questions is represented
by a score of −1. In the following example, the effect-coded
dummy variable “geneff” (Is the participant male, yes or no?) is
coded +1 for males and −1 for females. The variable geneff is
called an effect-coded dummy variable because we used −1
(rather than 0) as the value that represents membership in the
last group. Our overall model for the prediction of salary (Y)
from gender, represented by the effect-coded dummy variable
geneff, can be written as follows:
Substituting the values of +1 for males and −1 for females, the
predictive equations for males and females become
From earlier discussions on t tests and ANOVA, we know that
the best predicted value of Y for males is equivalent to the
mean on Y for males, Mmale; similarly, the best predicted value
of Y for females is equal to the mean on Y for females,
Mfemale. The two equations above, therefore, tell us that
Mmale = b0 + b1 and Mfemale = b0 − b1. What does this imply
26. for the values of b0 and b1? The mean for males is b1 units
above b0; the mean for females is b1 units below b0. With a
little thought, you will see that the intercept b0 must equal the
grand mean on salary for both genders combined.
Note that when we calculate a grand mean by combining group
means, there are two different possible ways to calculate the
grand mean. If the groups have the same numbers of scores,
these two methods yield the same result, but when the groups
have unequal numbers of cases, these two methods for
computation of the grand mean yield different results.
Whenever you do analyses with unequal numbers in the groups,
you need to decide whether the unweighted or the weighted
grand mean is a more appropriate value to report. In some
situations, it may not be clear what default decision a computer
program uses (i.e., whether the program reports the weighted or
the unweighted grand mean), but it is possible to calculate both
the weighted and unweighted grand means by hand from the
group means; when you do this, you will be able to determine
which version of the grand mean was reported on the SPSS
printout.
The unweighted grand mean for salary for males and females is
obtained by ignoring the number of cases in the groups and
averaging the group means together for males and females. For
the male and female salary data that appeared in Table 12.1, the
unweighted mean is (Mmale + Mfemale)/2 = (40.80 + 49.90)/2 =
45.35. Note that the b0 constant or intercept term in the
regression in Figure 12.10 that uses effect-coded gender to
predict salary corresponds to this unweighted grand mean of
45.35. When you run a regression to predict scores on a
quantitative outcome variable from effect-coded dummy
predictor variables, and the default methods of computation are
used in SPSS, the b0 coefficient in the regression equation
corresponds to the unweighted grand mean, and effects (or
differences between group means and grand means) are reported
relative to this unweighted grand mean as a reference point.
This differs slightly from the one-way ANOVA output in Figure
27. 12.5, which reported the weighted grand mean for salary
(46.26).
When effect-coded dummy predictor variables are used, the
slope coefficient b1 corresponds to the “effect” of gender; that
is, +b1 is the distance between the male mean and the grand
mean, and −b1 is the distance between the female mean and the
grand mean. In Chapter 6 on one-way ANOVA, the terminology
used for these distances (group mean minus grand mean) was
effect. The effect of membership in Group i in a one-way
ANOVA is represented by αi, where αi = Mi − MY, the mean of
Group i minus the grand mean of Y across all groups.
This method of coding (+1 vs. −1) is called “effect coding,”
because the intercept b0 in Equation 12.5 equals the unweighted
grand mean for salary, MY, and the slope coefficient bi for each
effect-coded dummy variable E represents the effect for the
group that has a code of 1 on that variable—that is, the
difference between that group’s mean on Y and the unweighted
grand mean. Thus, when effect-coded dummy variables are used
to represent group membership, the b0 intercept term equals the
grand mean for Y, the outcome variable, and each bi coefficient
represents a contrast between the mean of one group versus the
unweighted grand mean (or the “effect” for that group). The
significance of b1 for the effect-coded variable geneff is a test
of the significance of the difference between the mean of the
corresponding group (in this example, males) and the
unweighted grand mean. Given that geneff is coded −1 for
females and +1 for males and given that the value of b1 is
significant and positive, the mean salary of males is
significantly higher than the grand mean (and the mean salary
for females is significantly lower than the grand mean).
Note that we do not have to use a code of +1 for the group with
the higher mean and a code of −1 for the group with the lower
mean on Y. The sign of the b coefficient can be either positive
or negative; it is the combination of signs (on the code for the
dummy variable and the b coefficient) that tells us which group
had a mean that was lower than the grand mean.
28. The overall F result for the regression analysis that predicts
salary from effect-coded gender (geneff) in Figure 12.10 is
identical to the F value in the earlier analyses of gender and
salary reported in Figures 12.5 and 12.7: F(1, 48) = 13.02, p =
.001. The effect size given by η2 and R2 is also identical across
these three analyses (R2 = .21). The only difference between the
regression that uses a dummy-coded dummy variable (Figure
12.7) and the regression that uses an effect-coded dummy
variable to represent gender (Figure 12.10) is in the way in
which the b0 and b1 coefficients are related to the grand mean
and group means.12.7.2.2 Multiple-Group Example With Effect-
Coded Dummy Variables
If we used effect coding instead of dummy coding to represent
membership in the three college groups used as an example
earlier, group membership could be coded as follows:
That is, E1 and E2 still represent yes/no questions about group
membership. E1 corresponds to the following question: Is the
faculty member in Liberal Arts? Coded 1 = yes, 0 = no. E2
corresponds to the following question: Is the faculty member in
Sciences? Coded 1 = yes, 0 = no. The only change when effect
coding (instead of dummy coding) is used is that members of
the Business college (the one group that does not correspond
directly to a yes/no question) now receive codes of −1 on both
the variables E1 and E2.
We can run a regression to predict scores on salary from the two
effect-coded dummy variables E1 and E2:
Figure 12.10 Regression to Predict Salary From Effect-Coded
Gender
The results of this regression analysis are shown in Figure
12.11.
When effect coding is used, the intercept or b0 coefficient is
interpreted as an estimate of the (unweighted) grand mean for
the Y outcome variable, and each bi coefficient represents the
29. effect for one of the groups—that is, the contrast between a
particular group mean and the grand mean. (Recall that when
dummy coding was used, the intercept b0 was interpreted as the
mean of the “last” group—namely, the group that did not
correspond to a “yes” answer on any of the dummy variables—
and each bi coefficient corresponded to the difference between
one of the group means and the mean of the “last” group, the
group that is used as the reference group for all comparisons.)
In Figure 12.11, the overall F value and the overall R2 are the
same as in the two previous analyses that compared mean salary
across college (in Figures 12.6 and 12.9): F(2, 47) = 2.12, p =
.125; R2 = .085. The b coefficients for Equation 12.9, from
Figure 12.11, are as follows:
Salary′ = 47.03 − 2.205 × E1 − 2.317 × E2.
The interpretation is as follows: The (unweighted) grand mean
of salary is 47.03 thousand dollars per year. Members of the
Liberal Arts faculty have a predicted annual salary that is 2.205
thousand dollars less than this grand mean; members of the
Sciences faculty have a predicted salary that is 2.317 thousand
dollars less than this grand mean. Neither of these differences
between a group mean and the grand mean is statistically
significant at the α = .05 level.
Figure 12.11 Regression to Predict Salary From Effect-Coded
College Membership
Because members of the Business faculty have scores of −1 on
both E1 and E2, the predicted mean salary for Business faculty
is
Salary′= 47.03 + 2.205 + 2.317 = 51.5 thousand dollars per
year.
We do not have a significance test to evaluate whether the mean
salary for Business faculty is significantly higher than the grand
mean. If we wanted to include a significance test for this
contrast, we could do so by rearranging the dummy variable
codes associated with group membership, such that membership
in the Business college group corresponded to an answer of
30. “yes” on either E1 or E2.12.7.3 Orthogonal Coding of Dummy
Predictor Variables
We can set up contrasts among group means in such a way that
the former are orthogonal (the term orthogonal is equivalent to
uncorrelated or independent). One method of creating
orthogonal contrasts is to set up one contrast that compares
Group 1 versus Group 2 and a second contrast that compares
Groups 1 and 2 combined versus Group 3, as in the example
below:
The codes across each row should sum to 0. For each
orthogonally coded dummy variable, the groups for which the
code has a positive sign are contrasted with the groups for
which the code has a negative sign; groups with a code of 0 are
ignored. Thus, O1 compares the mean of Group 1 (Liberal Arts)
with the mean of Group 2 (Sciences).
To figure out which formal null hypothesis is tested by each
contrast, we form a weighted linear composite that uses these
codes. That is, we multiply the population mean μk for Group k
by the contrast coefficient for Group k and sum these products
across the k groups; we set that weighted linear combination of
population means equal to 0 as our null hypothesis.
In this instance, the null hypotheses that correspond to the
contrast specified by the two Oi orthogonally coded dummy
variables are as follows:
H0 for O1: (+1)μ1 + (−1)μ2 + (0)μ3 = 0.
That is,
H0 for O1: μ1 − μ2 = 0 (or μ1 = μ2).
The O2 effect-coded dummy variable compares the average of
the first two group means (i.e., the mean for Liberal Arts and
Sciences combined) with the mean for the third group
(Business):
or
We can assess whether the contrasts are orthogonal by taking
31. the cross products and summing the corresponding coefficients.
Recall that products between sets of scores provide information
about covariation or correlation; see Chapter 7 for details.
Because each of the two variables O1 and O2 has a sum (and,
therefore, a mean) of 0, each code represents a deviation from a
mean. When we compute the sum of cross products between
corresponding values of these two variables, we are, in effect,
calculating the numerator of the correlation between O1 and O2.
In this example, we can assess whether O1 and O2 are
orthogonal or uncorrelated by calculating the following sum of
cross products. For O1 and O2, the sum of cross products of the
corresponding coefficients is
(+1)(+1) + (−1)(+1) + (0)(−2) = 0.
Because this sum of the products of corresponding coefficients
is 0, we know that the contrasts specified by O1 and O2 are
orthogonal.
Of course, as an alternate way to see whether the O1 and O2
predictor variables are orthogonal or uncorrelated, SPSS can
also be used to calculate a correlation between O1 and O2; if
the contrasts are orthogonal, Pearson’s r between O1 and O2
will equal 0 (provided that the numbers in the groups are equal).
For each contrast specified by a set of codes (e.g., the O1 set of
codes), any group with a 0 coefficient is ignored, and groups
with opposite signs are contrasted. The direction of the signs
for these codes does not matter; the contrast represented by the
codes (+1, −1, and 0) represents the same comparison as the
contrast represented by (−1, +1, 0). The b coefficients obtained
for these two sets of codes would be opposite in sign, but the
significance of the difference between the means of Group 1 and
Group 2 would be the same whether Group 1 was assigned a
code of +1 or −1.
Figure 12.12 shows the results of a regression in which the
dummy predictor variables O1 and O2 are coded to represent the
same orthogonal contrasts. Note that the t tests for significance
of each contrast are the same in both Figure 12.6, where the
contrasts were requested as an optional output from the
32. ONEWAY procedure, and Figure 12.12, where the contrasts
were obtained by using orthogonally coded dummy variables as
predictors of salary. Only one of the two contrasts, the contrast
that compares the mean salary for Liberal Arts and Sciences
faculty with the mean salary for Business faculty, was
statistically significant.
Figure 12.12 Regression to Predict Salary From Dummy
Variables That Represent Orthogonal Contrasts
Orthogonal coding of dummy variables can also be used to
perform a trend analysis—for example, when the groups being
compared represent equally spaced dosage levels along a
continuum. The following example shows orthogonal coding to
represent linear versus quadratic trends for a study in which the
groups receive three different dosage levels of caffeine.
Note that the sum of cross products is again 0 [(−1)(+1) +
(0)(−2) + (+1)(+1)], so these contrasts are orthogonal. A simple
way to understand what type of trend is represented by each line
of codes is to visualize the list of codes for each dummy
variable as a template or graph. If you place values of −1, 0,
and +1 from left to right on a graph, it is clear that these codes
represent a linear trend. The set of coefficients +1, −2, and +1
or, equivalently, −1, +2, and −1 represent a quadratic trend. So,
if b1 (the coefficient for O1) is significant with this set of
codes, the linear trend is significant and b1 is the amount of
change in the dependent variable Y from 0 to 150 mg or from
150 to 300 mg. If b2 is significant, there is a quadratic
(curvilinear) trend.
12.8 Regression Models That Include Both Dummy and
Quantitative Predictor Variables
We can do a regression analysis that includes one (or more)
dummy variables and one (or more) continuous predictors, as in
the following example:
33. How is the b1 coefficient for the dummy variable, D,
interpreted in the context of this multiple regression with
another predictor variable? The b1 coefficient still represents
the estimated difference between the means of the two groups;
if gender was coded 0, 1 as in the first example, b1 still
represents the difference between mean salary Y for males and
females. However, in the context of this regression analysis,
this difference between means on the Y outcome variable for the
two groups is assessed while statistically controlling for any
differences in the quantitative X variable (such as years of job
experience).
Numerical results for this regression analysis appear in Figure
12.13. From these results we can conclude that both gender and
years are significantly predictive of salary. The coefficient to
predict salary from gender (controlling for years of experience)
was b2 = 3.36, with t(47) = 2.29, p = .026. The corresponding
squared semipartial (or part) correlation for gender as a
predictor of salary was sr2 = (.159)2 = .03. The coefficient to
predict salary from years of experience, controlling for gender,
was b = 1.44, t(47) = 10.83, p < .001; the corresponding sr2
effect size for years was .7492 = .56. This analysis suggests that
controlling for years of experience partly accounts for the
observed gender differences in salary, but it does not
completely account for gender differences; even after years of
experience is taken into account, males still have an average
salary that is about 3.36 thousand dollars higher than females at
each level of years of experience. However, this gender
difference is relatively small in terms of the proportion of
explained variance. About 56% of the variance in salaries is
uniquely predictable from years of experience (this is a very
strong effect). About 3% of the variance in salary is predictable
from gender (this is a medium-sized effect). Within this sample,
for each level of years of experience, females are paid about
3.35 thousand dollars less than males who have the same
number of years of experience; this is evidence of possible
gender bias. Of course, it is possible that this remaining gender
34. difference in salary might be accounted for by other variables.
Perhaps more women are in the college of Liberal Arts, which
has lower salaries, and more men are in the Business and
Science colleges, which tend to receive higher salaries.
Controlling for other variables such as college might help us to
account for part of the gender difference in mean salary levels.
The raw score b coefficient associated with gender in the
regression in Figure 12.13 had a value of b = 3.355; this
corresponds to the difference between the intercepts of the
regression lines for males and females in Figure 12.14. For this
set of data, it appears that gender differences in salary may be
due to a difference in starting salaries (i.e., the salaries paid to
faculty with 0 years of experience) and not due to differences
between the annual raises in salary given to male and female
faculty. The model Results section at the end of the chapter
provides a more detailed discussion of the results that appear in
Figures 12.13 and 12.14.
The best interpretation for the salary data in Table 12.1, based
on the analyses that have been performed so far, appears to be
the following: Salary significantly increases as a function of
years of experience; there is also a gender difference in salary
(such that males are paid significantly higher salaries than
females) even when the effect of years of experience is
statistically controlled. However, keep in mind that statistically
controlling for additional predictor variables (such as college
and merit) in later analyses could substantially change the
apparent magnitude of gender differences in salary.
Figure 12.13 Regression to Predict Salary From Gender and
Years
Figure 12.14 Graph of the Regression Lines to Predict Salary
From Years of Experience Separately for Males and Females
NOTE: This is based on the regression analysis in Figure 12.13:
Salary′ = 34.2 + 3.36 × Gender + 1.44 × Years; gender coded 0
35. = female and 1 = male.
12.9 Effect Size and Statistical Power
As discussed in Chapter 11, we can represent the effect size for
the regression as a whole (i.e., the proportion of variance in Y
is predictable from a set of variables that may include dummy-
coded and/or continuous variables) by reporting multiple R and
multiple R2 as our overall effect-size measures. We can
represent the strength of the unique predictive contribution of
any particular variable by reporting the estimate of sr2unique
for each predictor variable, as in Chapter 11. The proportion of
variance given by sr2 can be used to describe the proportion of
variance uniquely predictable from the contrast specified by a
dummy variable, in the same manner in which it describes the
proportion of variance uniquely predictable from a continuous
predictor variable.
When only dummy variables are included in regression analysis,
the regression is essentially equivalent to a one-way ANOVA
(or a t test, if only two groups are being compared). Therefore,
the tables presented in Chapter 5 (independent samples t test)
and Chapter 6 (one-way ANOVA) can be used to look up
reasonable minimum sample sizes per group for anticipated
effect sizes that are small, medium, or large. Whether the
method used to make predictions and compare means across
groups is ANOVA or regression, none of the groups should have
a very small n. If n < 20 per group, nonparametric analyses may
be more appropriate.
12.10 Nature of the Relationship and/or Follow-Up Tests
When dummy-coded group membership predictors are included
in a regression analysis, the information that individual
coefficients provide is equivalent to the information obtained
from planned contrasts between group means in an ANOVA.
The choice of the method of coding (dummy, effect, orthogonal)
and the decision as to which group to code as the “last” group
determine which set of contrasts the regression will include.
36. Whether we compare multiple groups by performing a one-way
ANOVA or by using a regression equation with dummy-coded
group membership variables as predictors, the written results
should include the following: means and standard deviations for
scores in each group, confidence intervals for each group mean,
an overall F test to report whether there were significant
differences in group means, planned contrasts or post hoc tests
to identify which specific pairs of group means differed
significantly, and a discussion of the direction of differences
between group means. Effect-size information about the
proportion of explained variance (in the form of an η2 or sr2)
should also be included.
12.11 Results
The hypothetical data showing salary scores for faculty (in
Table 12.1) were analyzed in several different ways to
demonstrate the equivalence between ANOVA and regression
with dummy variables and to illustrate the interpretation of b
coefficients for dummy variables in regression. The text below
reports the regression analysis for prediction of salary from
years of experience and gender (as shown in Figure 12.13).
Results
To assess whether gender and years of experience significantly
predict faculty salary, a regression analysis was performed to
predict faculty annual salary in thousands of dollars from
gender (dummy-coded 0 = female, 1 = male) and years of
experience. The distribution of salary was roughly normal, the
variances of salary scores were not significantly different for
males and females, and scatter plots did not indicate nonlinear
relations or bivariate outliers. No data transformations were
applied to scores on salary and years, and all 50 cases were
included in the regression analysis.
The results of this regression analysis (SPSS output in Figure
12.13) indicated that the overall regression equation was
significantly predictive of salary; R = .88, R2 = .78, adjusted
37. R2 = .77, F(2, 47) = 80.93, p < .001. Salary could be predicted
almost perfectly from gender and years of job experience. Each
of the two individual predictor variables was statistically
significant. The raw score coefficients for the predictive
equation were as follows:
Salary′ = 34.19 + 3.36 × Gender + 1.44 × Years.
When controlling for the effect of years of experience on salary,
the magnitude of the gender difference in salary was 3.36
thousand dollars. That is, at each level of years of experience,
male annual salary was about 3.36 thousand dollars higher than
female salary. This difference was statistically significant: t(47)
= 2.29, p = .026.
For each 1-year increase in experience, the salary increase was
approximately 1.44 thousand dollars for both females and
males. This slope for the prediction of salary from years of
experience was statistically significant: t(47) = 10.83, p < .001.
The graph in Figure 12.14 illustrates the regression lines to
predict salary for males and females separately. The intercept
(i.e., predicted salary for 0 years of experience) was
significantly higher for males than for females.
The squared semipartial correlation for years as a predictor of
salary was sr2 = .56; thus, years of experience uniquely
predicted about 56% of the variance in salary (when gender was
statistically controlled). The squared semipartial correlation for
gender as a predictor of salary was sr2 = .03; thus, gender
uniquely predicted about 3% of the variance in salary (when
years of experience was statistically controlled). The results of
this analysis suggest that there was a systematic difference
between salaries for male and female faculty and that this
difference was approximately the same at all levels of years of
experience. Statistically controlling for years of job experience,
by including it as a predictor of salary in a regression that also
used gender to predict salary, yielded results that suggest that
the overall gender difference in mean salary was partly, but not
completely, accounted for by gender differences in years of job
experience.
38. 12.12 Summary
This chapter presented examples that demonstrated the
equivalence of ANOVA and regression analyses that use dummy
variables to represent membership in multiple groups. This
discussion has presented demonstrations and examples rather
than formal proofs; mathematical statistics textbooks provide
formal proofs of equivalence between ANOVA and regression.
ANOVA and regression are different special cases of the GLM.
If duplication of ANOVA using regression were the only
application of dummy variables, it would not be worth spending
so much time on them. However, dummy variables have
important practical applications. Researchers often want to
include group membership variables (such as gender) among the
predictors that they use in multiple regression, and it is
important to understand how the coefficients for dummy
variables are interpreted.
An advantage of choosing ANOVA as the method for comparing
group means is that the SPSS procedures provide a wider range
of options for follow-up analysis—for example, post hoc
protected tests. Also, when ANOVA is used, interaction terms
are generated automatically for all pairs of (categorical)
predictor variables or factors, so it is less likely that a
researcher will fail to notice an interaction when the analysis is
performed as a factorial ANOVA (as discussed in Chapter 13)
than when a comparison of group means is performed using
dummy variables as predictors in a regression. ANOVA does
not assume a linear relationship between scores on categorical
predictor variables and scores on quantitative outcome
variables. A quantitative predictor can be added to an ANOVA
model (this type of analysis, called analysis of covariance or
ANCOVA, is discussed in Chapter 15).
On the other hand, an advantage of choosing regression as the
method for comparing group means is that it is easy to use
quantitative predictor variables along with group membership
39. predictor variables to predict scores on a quantitative outcome
variable. Regression analysis yields equations that can be used
to generate different predicted scores for cases with different
score values on both categorical and dummy predictor variables.
A possible disadvantage of the regression approach is that
interaction terms are not automatically included in a regression;
the data analyst must specifically create a new variable (the
product of the two variables involved in the interaction) and add
that new variable as a predictor. Thus, unless they specifically
include interaction terms in their models (as discussed in
Chapter 15), data analysts who use regression analysis may fail
to notice interactions between predictors. A data analyst who is
careless may also set up a regression model that is “nonsense”;
for example, it would not make sense to predict political
conservatism (Y) from scores on a categorical X1 predictor
variable that has codes 1 for Democrat, 2 for Republican, and 3
for Independent. Regression assumes a linear relationship
between predictor and outcome variables; political party
represented by just one categorical variable with three possible
score values probably would not be linearly related to an
outcome variable such as conservatism. To compare group
means using regression in situations where there are more than
two groups, the data analyst needs to create dummy variables to
represent information about group membership. In some
situations, it may be less convenient to create new dummy
variables (and run a regression) than to run an ANOVA.
Ultimately, however, ANOVA and regression with dummy
predictor variables yield essentially the same information about
predicted scores for different groups. In many research
situations, ANOVA may be a more convenient method to assess
differences among group means. However, regression with
dummy variables provides a viable alternative, and in some
research situations (where predictor variables include both
categorical and quantitative variables), a regression analysis
may be a more convenient way of setting up the analysis.
40. Note
1. Unequal numbers in groups make the interpretation of b
coefficients for dummy variables more complex. For additional
information about issues that should be considered when using
dummy or effect codes to represent groups of unequal sizes, see
Hardy (1993).
Comprehension Questions
1.
Suppose that a researcher wants to do a study to assess how
scores on the dependent variable heart rate (HR) differ across
groups that have been exposed to various types of stress. Stress
group membership was coded as follows:
Group 1, no stress/baseline
Group 2, mental arithmetic
Group 3, pain induction
Group 4, stressful social role play
The basic research questions are whether these four types of
stress elicited significantly different HRs overall and which
specific pairs of groups differed significantly.
a.
Set up dummy-coded dummy variables that could be used to
predict HR in a regression.
Note that it might make more sense to use the “no-stress”
41. group as the one that all other group means are compared with,
rather than the group that happens to be listed last in the list
above (stressful role play). Before working out the contrast
coefficients, it may be helpful to list the groups in a different
order:
Group 1, mental arithmetic
Group 2, pain induction
Group 3, stressful social role play
Group 4, no stress/baseline
Set up dummy-coded dummy variables to predict scores on HR
from group membership for this set of four groups.
Write out in words which contrast between group means
each dummy variable that you have created represents.
b.
Set up effect-coded dummy variables that could be used to
predict HR in a regression.
Describe how the numerical results for these effect-coded
42. dummy variables (in 1b) differ from the numerical results
obtained using dummy-coded dummy variables (in 1a). Which
parts of the numerical results will be the same for these two
analyses?
c.
Set up the coding for orthogonally coded dummy variables that
would represent these orthogonal contrasts:
Group 1 versus 2
Groups 1 and 2 versus 3
Groups 1, 2, and 3 versus 4
2.
Suppose that a researcher does a study to see how level of
anxiety (A1 = low, A2 = medium, A3 = high) is used to predict
exam performance (Y). Here are hypothetical data for this
research situation. Each column represents scores on Y (exam
scores).
a.Would it be appropriate to do a Pearson correlation (and/or
linear regression) between anxiety, coded 1, 2, 3 for (low,
medium, high), and exam score? Justify your answer. b.Set up
orthogonally coded dummy variables (O1, O2) to represent
linear and quadratic trends, and run a regression analysis to
predict exam scores from O1 and O2. What conclusions can you
draw about the nature of the relationship between anxiety and
exam performance? c.Set up dummy-coded dummy variables to
contrast each of the other groups with Group 2, medium
anxiety; run a regression to predict exam performance (Y) from
43. these dummy-coded dummy variables. d.Run a one-way
ANOVA on these scores; request contrasts between Group 2,
medium anxiety, and each of the other groups. Do a point-by-
point comparison of the numerical results for your ANOVA
printout with the numerical results for the regression in (2c),
pointing out where the results are equivalent.3.Why is it
acceptable to use a dichotomous predictor variable in a
regression when it is not usually acceptable to use a categorical
variable that has more than two values as a predictor in
regression?4.Why are values such as +1, 0, and −1 generally
used to code dummy variables?5.How does the interpretation of
regression coefficients differ for dummy coding of dummy
variables versus effect coding of dummy variables? (Hint: in
one type of coding, b0 corresponds to the grand mean; in the
other, b0 corresponds to the mean of one of the groups.)6.If you
have k groups, why do you only need k − 1 dummy variables to
represent group membership? Why is it impossible to include k
dummy variables as predictors in a regression when you have k
groups?7.How does orthogonal coding of dummy variables
differ from dummy and effect coding?8.Write out equations to
show how regression can be used to duplicate a t test or a one-
way ANOVA.
(Warner 469-500)
Warner, Rebecca (Becky) (Margaret). Applied Statistics: From
Bivariate Through Multivariate Techniques, 2nd Edition. SAGE
Publications, Inc, 04/2012. VitalBook file.
The citation provided is a guideline. Please check each citation
for accuracy before use.
IBM SPSS Step-by-Step Guide: One-Way ANOVA
Note: This guide is an example of creating ANOVA output in
44. SPSS with the grades.sav file. The variables shown in this guide
do not correspond with the actual variables assigned in Unit 10
Assignment 1. Carefully follow the instructions in the
assignment for a list of assigned variables. Screen shots were
created with SPSS 21.0.
Creating One-Way ANOVA Output
To complete Section 2 of the DAA for Unit 10 Assignment 1,
you will generate SPSS output for a histogram, descriptive
statistics, and the Shapiro-Wilk test, which are covered in
previous step-by-step guides. The Levene test (homogeneity of
variance) is covered in the steps below.
Refer to the Unit 10 assignment instructions for a list of
assigned variables. The examplevariables year and final are
shown below.
Step 1. Open grades.sav in SPSS.
Step 2. On the Analyze menu, point to Compare Means and
click One-Way ANOVA…
Step 3. In the One-Way ANOVA dialog box:
· Move the assigned dependent variable into the Dependent List
box.
· Move the assigned independent variable into the Factor box.
The examples of final and year are shown below.
· Click the Options button.
Step 4. In the One-Way ANOVA: Options dialog box:
· Select Homogeneity of variance test (for the Levene test for
Section 2 of the DAA).
45. · Select Descriptive and Means Plot (for Section 4 of the DAA).
· Click Continue.
· Return to the One-Way ANOVA dialog box and select the Post
Hoc button.
Step 5. In the One-Way ANOVA: Post Hoc Multiple
Comparisons dialog box:
· Check the Tukey option for multiple comparisons.
· Click Continue and OK.
Interpreting One-Way ANOVA Output
A string of ANOVA output will appear in SPSS. (The output
below is for the example variable final.)
Step 1. Copy the Levene test output from SPSS and paste it into
Section 2 of the DAA Template. Then interpret it for the
homogeneity of variance assumption.
Test of Homogeneity of Variances
final
Levene Statistic
df1
df2
Sig.
.866
3
101
.462
Step 2. Copy the means plot, paste it into Section 4 of the DAA
Template, and interpret it.
Step 3. Copy the descriptives output. Paste it into Section 4
46. along with the report of means and standard deviations of the
dependent variable at each level of the independent variable.
Descriptives
final
N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean
Minimum
Maximum
Lower Bound
Upper Bound
Frosh
3
59.33
5.859
3.383
44.78
73.89
55
66
Soph
19
62.42
6.628
1.520
48. Sum of Squares
df
Mean Square
F
Sig.
Between Groups
37.165
3
12.388
.192
.902
Within Groups
6525.025
101
64.604
Total
6562.190
104
Step 5. Finally, if the overall ANOVA is significant, copy the
post hoc output, paste it into Section 4, and interpret it.
Multiple Comparisons
Dependent Variable: final
Tukey HSD
(I) Year in school
(J) Year in school
Mean Difference (I-J)
Std. Error
Sig.
95% Confidence Interval
51. 1.561
4.993
.989
-11.48
14.61
Soph
-1.526
2.608
.936
-8.34
5.29
Junior
-.574
2.100
.993
-6.06
4.91
5
In Unit 10, we will apply our understanding of the one-way
ANOVA to the SPSS data set.
Proper Reporting of the One-Way ANOVA
Reporting a one-way ANOVA in proper APA style requires an
understanding of several elements. To provide
context for the F test, provide a means plot, as well as the
means and standard deviations for each level of a
given factor. The following elements are included in reporting
the F test:
52. • The statistical notation for a one-way ANOVA ( F).
• The degrees of freedom.
• The F value.
• The probability value ( p).
• The effect size.
If the omnibus F test is significant, follow with a discussion of
post-hoc tests. Consider the following example
from Warner (2013, p. 254):
The overall F for the one-way ANOVA was statistically
significant, F(3, 24) = 11.94, p < .001. This
corresponded to an effect size of η2 = .60; that is, about 60% of
the variance in anxiety scores was
predictable from the type of stress intervention. This is a large
effect. . . .
In addition, all possible pairwise comparisons were made using
the Tukey HSD test. Based on this
test. . . .
F, Degrees of Freedom, and F Value
The statistical notation for a one-way ANOVA is F, and
following it is the degrees of freedom for this statistical
test, such as (3, 24) reported above. Unlike correlation or a t
test, there are two degrees of freedom reported
for a one-way ANOVA. The first reported df is the between-
groups df, or dfbetween, which is the number of
groups (or levels) minus one ( k − 1). In the example above, the
factor consists of k = 4 levels (4 − 1 = 3). The
second reported df is the within-groups df, or dfwithin, which is
the sample size minus the number of groups or
53. levels ( N − k). In the example above, N = 28, so 28 − 4 = 24.
The F value is calculated as a ratio of mean
squares, which are both positive. Therefore, any non-zero F
value is always positive.
Probability Value
Appendix C (pp.1058–1061) of the Warner text provides critical
values of F for rejecting the null hypothesis. In
the example above, with (3, 24) degrees of freedom and alpha
level set to .05 (one-tailed versus two-tailed is
not relevant to ANOVA), the table indicates a critical value of ±
3.01 to reject the null hypothesis. The obtained
F value above is 11.94, which exceeds the critical value
required to reject the null hypothesis. SPSS determined
the exact p value to be .000, which is reported as p < .001.
(Remember that SPSS only calculates a p value out
to three decimal places.) This p value is less than .05, which
indicates that the null hypothesis should be rejected
for the alternative hypothesis—that is, at least one of the four
group means is significantly different from the
other group means.
Unit 10 - One-Way ANOVA: Application
INTRODUCTION
Effect Size
The effect size for a one-way ANOVA is eta squared (η2). The
effect size is not provided in SPSS output. It is
calculated by hand by dividing SSbetween by SStotal from the
SPSS ANOVA output. In the example above,
54. SSbetween = 182.107 and SStotal = 304.107, which means that
182.107 ÷ 304.107 = .60. The effect size is
interpreted using Table 5.2 in the Warner text (p. 208).
Post-Hoc Tests
When the omnibus F is significant, it does not indicate exactly
which pairwise comparisons are significant. A
Tukey's honestly significant difference (HSD) test is one of
many post-hoc tests used. The SPSS output for the
Tukey's HSD indicates which pairwise comparisons are
statistically significant, and this information can be
reported in narrative form (that is, without p values or other
specific statistical notation) as shown in the fourth
paragraph of the "Results" section in the Warner text (p. 254).
The Warner text provides a "Results" example at the end of
each chapter for all statistics studied in this course.
You are encouraged to review these examples and follow their
structure when writing up Section 4,
"Interpretation," of the DAA Template.
Reference
Warner, R. M. (2013). Applied statistics: From bivariate
through multivariate techniques (2nd ed.). Thousand
Oaks, CA: Sage.
OBJECTIVES
To successfully complete this learning unit, you will be
expected to:
1. Interpret the one-way ANOVA output.
55. 2. Apply the appropriate SPSS procedures to check assumptions
and calculate the one-way ANOVA to
generate relevant output.
3. Analyze the assumptions of the one-way ANOVA.
4. Articulate a research question, null hypothesis, alternative
hypothesis, and alpha level.
[u10s1] Unit 10 Study 1- Readings
Use your IBM SPSS Statistics Step by Step text to complete the
following:
• Read Chapter 12, "The One-Way ANOVA Procedure." This
reading addresses the following topics:
◦ Introduction to one-way ANOVA.
◦ SPSS commands.
◦ Post-hoc tests in SPSS.
◦ Planned contrasts in SPSS.
◦ Reporting and interpreting SPSS output.
[u10a1] Unit 10 Assignment 1 - One-Way ANOVA
As with your previous assignments, you will complete this
assignment with the DAA Template. Links to
additional resources are available in the Resources area.
Reminder: The format of this SPSS assignment should be
narrative with supporting statistical output
(table and graphs) integrated into the text in the appropriate
places (not all at the end of the document).
You will analyze the following variables in the grades.sav data
set:
• section
• quiz3
56. Step 1: Write Section 1 of the DAA.
• Provide the context of the grades.sav data set.
• Include a definition of the specified variables (predictor,
outcome) and corresponding scales of
measurement.
• Specify the sample size of the data set.
Step 2: Write Section 2 of the DAA.
• Analyze the assumptions of the one-way ANOVA.
• Paste the SPSS histogram output for quiz3 and discuss your
visual interpretations.
• Paste SPSS descriptives output showing skewness and kurtosis
values for quiz3 and interpret them.
• Paste SPSS output for the Shapiro-Wilk test of quiz3 and
interpret it.
• Report the results of the Levene test and interpret it.
• Summarize whether or not the assumptions of the one-way
ANOVA are met.
Step 3: Write Section 3 of the DAA.
• Specify a research question related to the one-way ANOVA.
• Articulate the null hypothesis and alternative hypothesis.
• Specify the alpha level.
Step 4: Write Section 4 of the DAA.
• Begin by pasting SPSS output of the means plot and providing
an interpretation.
• Also report the means and standard deviations of quiz3 for
57. each level of the section variable.
• Next, paste the SPSS ANOVA output and report the results of
the F test, including:
◦ Degrees of freedom.
◦ F value.
◦ p value.
◦ Calculated effect size.
◦ Interpretation of the effect size.
• Finally, if the omnibus F is significant, provide the SPSS post-
hoc (Tukey HSD) output.
◦ Interpret the post-hoc tests.
Step 5: Write Section 5 of the DAA.
• Discuss the conclusions of the one-way ANOVA as it relates
to the research question.
• Conclude with an analysis of the strengths and limitations of
one-way ANOVA.
Submit your DAA Template as an attached Word document in
the assignment area.
Resources
One-Way ANOVA Scoring Guide.
DAA Template.
SPSS Data Analysis Report Guidelines.
IBM SPSS Step-by-Step Guide: One-Way ANOVA.
Copy/Export Output Instructions.
58. APA Style and Format.
Journal Article Summary Scoring Guide
Due Date: End of Unit 9.
Percentage of Course Grade: 12%.
CRITERIA NON-PERFORMANCE BASIC PROFICIENT
DISTINGUISHED
Locate a scholarly
journal article in a
career specialization
that reports a
correlation, a t test,
a one-way ANOVA,
or some
combination.
20%
Does not locate a
scholarly journal
article in a career
specialization that
reports a correlation,
a t test, a one-way
ANOVA, or some
combination.
Locates a scholarly
journal article in a
career specialization,
59. but the article does not
report a correlation, a t
test, a one-way
ANOVA, or some
combination.
Locates a scholarly
journal article in a
career specialization
that reports a
correlation, a t test, a
one-way ANOVA, or
some combination.
Locates an exemplary
scholarly journal
article in a career
specialization that
reports a correlation, a
t test, a one-way
ANOVA, or some
combination.
Provide context for
the research study,
including a
definition of
variables and scales
of measurement.
15%
Does not provide
context for the
research study.
Provides partial
60. context for the
research study that
may or may not
include a definition of
variables and scales of
measurement.
Provides context for
the research study,
including a definition
of variables and
scales of
measurement.
Provide exemplary
context for the
research study,
including a definition
of variables and
scales of
measurement.
Identify
assumptions of the
statistic reported in
the journal article.
10%
Does not identify
assumptions of the
statistic reported in
the journal article.
Partially identifies
assumptions of the
statistic reported in the
61. journal article.
Identifies
assumptions of the
statistic reported in
the journal article.
Interprets assumptions
of the statistic reported
in the journal article.
Articulate the
research question,
null hypothesis,
alternative
hypothesis, and
alpha level.
15%
Does not articulate
the research
question, null
hypothesis,
alternative
hypothesis, and
alpha level.
Articulates some
combination of, but not
all of, the research
question, null
hypothesis, alternative
hypothesis, and alpha
level.
Articulates the
62. research question,
null hypothesis,
alternative
hypothesis, and alpha
level.
Articulates the
research question, null
hypothesis, alternative
hypothesis, and alpha
level in an exemplary
manner.
Report the results of
the journal article,
interpreting the
statistic against the
null hypothesis.
20%
Does not report the
results of the journal
article.
Reports the results of
the journal article, but
does not interpret the
statistic against the
null hypothesis.
Reports the results of
the journal article,
interpreting the
statistic against the
null hypothesis.
63. Reports the results of
the journal article in an
exemplary manner,
and interprets the
statistic against the
null hypothesis.
Generate a
conclusion that
includes strengths
and limitations of
the journal article.
10%
Does not generate a
conclusion.
Generates a
conclusion that
includes a partial list of
strengths and
limitations of the
journal article.
Generates a
conclusion that
includes strengths
and limitations of the
journal article.
Generaes an
exemplary conclusion
that includes strengths
and limitations of the
journal article.
64. Communicate in a
manner that is
concise and
professional and
that makes proper
use of APA
formatting.
10%
Does not
communicate in a
manner that is
concise and
professional; does
not make proper use
of APA formatting.
Communicates in a
manner that may or
may not be concise or
professional; often
makes proper use of
APA formatting.
Communicates in a
manner that is
concise and
professional and that
makes proper use of
APA formatting.
Communicates in a
manner that is concise
and professional and
that always makes
proper use of APA
65. formatting.
For the SPSS data analysis report assignments in Units 6, 8, and
10, you will use the Data Analysis and
Application (DAA) Template with the five sections described
below. As shown in the IBM SPSS step-
by-step guides, label all tables and graphs in a manner
consistent with Capella's APA Style and Format
guidelines. Citations, if needed, should be included in the text
and references included in a reference
section at the end of the report. The organization of the report
should include the following five sections:
Section 1: Data File Description (One Paragraph)
1. Describe the context of the data set. Cite a previous
description if the same data set is used from a
previous assignment. To increase the formal tone of the DAA,
avoid first-person perspective "I."
For example, do not write, "I ran a scatter plot shown in Figure
1." Instead, write, "Figure 1
shows. . . ."
2. Specify the variables used in this DAA and the scale of
measurement of each variable.
3. Specify sample size (N).
Section 2: Testing Assumptions (Multiple Paragraphs)
1. Articulate the assumptions of the statistical test.
2. Paste SPSS output that tests those assumptions and interpret
them. Properly embed SPSS output
66. where appropriate. Do not string all output together at the
beginning of the section. In other
words, interpretations of figures and tables should be near (that
is, immediately above or below)
where the output appears. Format figures and tables per APA
formatting. Refer to the examples in
the IBM SPSS step-by-step guides.
3. Summarize whether or not the assumptions are met. If
assumptions are not met, discuss how to
ameliorate violations of the assumptions.
Section 3: Research Question, Hypotheses, and Alpha Level
(One Paragraph)
1. Articulate a research question relevant to the statistical test.
2. Articulate the null hypothesis and alternative hypothesis for
the research question.
3. Specify the alpha level (.05 unless otherwise specified).
Section 4: Interpretation (Multiple Paragraphs)
1. Paste SPSS output for an inferential statistic and report it.
Properly embed SPSS output where
appropriate. Do not string all output together at the beginning
of the section. In other words,
interpretations of figures and tables should be near (that is,
immediately above or below) where
the output appears. Format figures and tables per APA
formatting.
2. Report the test statistics. For guidance, refer to the "Results"
examples at the end of the
appropriate chapter of your Warner text.
3. Interpret statistical results against the null hypothesis.
67. Print
SPSS Data Analysis Report Guidelines
Section 5: Conclusion (Two Paragraphs)
1. Provide a brief summary (one paragraph) of the DAA
conclusions.
2. Analyze strengths and limitations of the statistical test.
One-Way ANOVA Scoring Guide
Due Date: End of Unit 10.
Percentage of Course Grade: 12%.
CRITERIA NON-PERFORMANCE BASIC PROFICIENT
DISTINGUISHED
Apply the
appropriate SPSS
procedures to check
assumptions and
calculate the one-
way ANOVA to
generate relevant
output.
20%
No SPSS output is
provided.
68. Includes some, but
not all, of required the
output. Numerous
errors in SPSS output.
Includes all relevant
output, but also
includes irrelevant
output. One or two
errors in SPSS
output.
Includes all relevant
output; no irrelevant
output is included. No
errors in SPSS output.
Provide context for
the data set,
including a
definition of
required variables
and scales of
measurement.
10%
Does not provide
context for the data
set.
Provides partial
context for the data
set. May or may not
include a definition of
required variables and
69. scales of
measurement.
Provides context for
the data set, including
a definition of required
variables and scales
of measurement.
Provides exemplary
context for the data
set, including a
definition of required
variables and scales of
measurement.
Analyze the
assumptions of the
one-way ANOVA.
20%
Does not analyze the
assumptions of the
one-way ANOVA.
Identifies, but does
not analyze, the
assumptions of the
one-way ANOVA.
Analyzes the
assumptions of the
one-way ANOVA.
Evaluates the
assumptions of the
70. one-way ANOVA.
Articulate a research
question, null
hypothesis,
alternative
hypothesis, and
alpha level.
10%
Does not articulate a
research question,
null hypothesis,
alternative
hypothesis, and alpha
level.
Partially articulates a
research question,
null hypothesis,
alternative hypothesis,
and alpha level.
Articulates a research
question, null
hypothesis,
alternative
hypothesis, and alpha
level.
Articulates a research
question, null
hypothesis, alternative
hypothesis, and alpha
level in an exemplary
manner.
71. Interpret the one-
way ANOVA output.
20%
Does not interpret the
one-way ANOVA
output.
Partially interprets the
one-way ANOVA
output.
Interprets the one-
way ANOVA output.
Interprets the one-way
ANOVA output in an
exemplary manner.
Generate a
conclusion that
includes strengths
and limitations of
the one-way ANOVA.
10%
Does not generate a
conclusion.
Generates a
conclusion that
includes a partial list
of strengths and
limitations of the one-
way ANOVA.
72. Generates a
conclusion that
includes strengths
and limitations of the
one-way ANOVA.
Generates an
exemplary conclusion
that includes strengths
and limitations of the
one-way ANOVA.
Communicate in a
manner that is
scholarly,
professional, and
consistent with the
expectations for
members in the
identified field of
study.
10%
Does not
communicate in a
manner that is
scholarly,
professional, and
consistent with the
expectations for
members in the
identified field of
study.
Inconsistently
73. communicates in a
manner that is
scholarly,
professional, and
consistent with the
expectations for
members in the
identified field of
study.
Communicates in a
manner that is
scholarly,
professional, and
consistent with the
expectations for
members in the
identified field of
study.
Without exception,
communicates in a
manner that is
scholarly, professional,
and consistent with the
expectations for
members in the
identified field of study.
Running head: DATA ANALYSIS AND APPLICATION
TEMPLATE 1
DATA ANALYSIS AND APPLICATION TEMPLATE 2
74. Data Analysis and Application (DAA) TemplateLearner
NameCapella University
Data Analysis and Application (DAA) Template
Use this file for all assignments that require the DAA Template.
Although the statistical tests will change from week to week,
the basic organization and structure of the DAA remains the
same. Update the title of the template. Remove this text and
provide a brief introduction.Section 1: Data File Description
Describe the context of the data set. You may cite your previous
description if the same data set is used from a previous
assignment.
Specify the variables used in this DAA and the scale of
measurement of each variable.
Specify sample size (N).Section 2: Testing Assumptions
1. Articulate the assumptions of the statistical test.
Paste SPSS output that tests those assumptions and interpret
them. Properly integrate SPSS output where appropriate. Do not
string all output together at the beginning of the section.
Summarize whether or not the assumptions are met. If
assumptions are not met, discuss how to ameliorate violations
of the assumptions.Section 3: Research Question, Hypotheses,
and Alpha Level
1. Articulate a research question relevant to the statistical test.
2. Articulate the null hypothesis and alternative hypothesis.
3. Specify the alpha level.Section 4: Interpretation
1. Paste SPSS output for an inferential statistic. Properly
integrate SPSS output where appropriate. Do not string all
output together at the beginning of the section.
2. Report the test statistics.
3. Interpret statistical results against the null hypothesis.Section
5: Conclusion
1. State your conclusions.
2. Analyze strengths and limitations of the statistical test.