The document describes how to conduct and interpret a one-way repeated measures ANOVA in SPSS. It discusses the key assumptions, provides a worked example using data from a word memory experiment, and walks through interpreting the SPSS output. The example ANOVA found a significant effect of word list on words remembered, with participants remembering more words from unrelated lists compared to phonologically related lists.
How do I do a T test, correlation and ANOVA in SpssSolution .pdfamitseesldh
How do I do a T test, correlation and ANOVA in Spss?
Solution
One-way between-subjects ANOVA A one-way between-subjects ANOVA allows
you to determine if there is a relationship between a categorical independent variable (IV) and a
continuous dependent variable (DV), where each subject is only in one level of the IV. To
determine whether there is a relationship between the IV and the DV, a one-way between-
subjects ANOVA tests whether the means of all of the groups are the same. If there are any
differences among the means, we know that the value of the DV depends on the value of the IV.
The IV in an ANOVA is referred to as a factor, and the different groups composing the IV are
referred to as the levels of the factor. A one-way ANOVA is also sometimes called a single
factor ANOVA. A one-way ANOVA with two groups is analogous to an independent-samples t-
test. The pvalues of the two tests will be the same, and the F statistic from the ANOVA will be
equal to the square of the t statistic from the t-test. To perform a one-way between-subjects
ANOVA in SPSS • Choose Analyze General Linear Model Univariate. • Move the DV to the
Dependent Variable box. • Move the IV to the Fixed Factor(s) box. • Click the OK button. The
output from this analysis will contain the following sections. • Between-Subjects Factors. Lists
how many subjects are in each level of your factor. • Tests of Between-Subjects Effects. The row
next to the name of your factor reports a test of whether there is a significant relationship
between your IV and the DV. A significant F statistic means that at least two group means are
different from each other, indicating the presence of a relationship. You can ask SPSS to provide
you with the means within each level of your between-subjects factor by clicking the Options
button in the variable selection window and moving your withinsubjects variable to the Display
Means For box. This will add a section to your output titled Estimated Marginal Means
containing a table with a row for each level of your factor. The values within each row provide
the mean, standard error of the mean, and the boundaries for a 95% confidence interval around
the mean for observations within that cell. Post-hoc analyses for one-way between-subjects
ANOVA. A significant F statistic tells you that at least two of your means are different from
each other, but does not tell you where the differences may lie. Researchers commonly perform
post-hoc analyses following a significant ANOVA to help them understand the nature of the
relationship between the IV and the DV. The most commonly reported post-hoc tests are (in
order from most to least liberal): LSD (Least Significant Difference test), SNK (Student-
Newman-Keuls), Tukey, and Bonferroni. The more liberal a test is, the more likely it will find a
significant difference between your means, but the more likely it is that this difference is actually
just due to chance. 14 Although it is the most liberal, simulations ha.
I need this done ASAP, You have to have SPSS Software on your comput.docxanthonybrooks84958
I need this done ASAP, You have to have SPSS Software on your computer. Please do not request to do the assignment if you don't have the software or if you do not have the understanding to get this assignment complete.
Assignment 2: Tests of Significance
Throughout this assignment you will review mock studies.
You will needs to follow the directions outlined in the section using SPSS and decide whether there is significance between the variables.
You will need to list the five steps of hypothesis testing (as covered in the lesson for Week 6) to see how
every
question should be formatted.
You will complete all of the problems.
Be sure to cut and past the appropriate test result boxes from SPSS under each problem and explain what you will do with your research hypotheses.
All calculations should be coming from your SPSS
.
You will need to submit the SPSS output file to get credit for this assignment.
This file will save as a .spv file and will need to be in a single file.
In other words, you are not allowed to submit more than one output file for this assignment.
The five steps of hypothesis testing when using SPSS are as follows:
State your research hypothesis (H
1
) and null hypothesis (H
0
).
Identify your significance level (.05 or .01)
Conduct your analysis using SPSS.
Look for the valid score for comparison.
This score is usually under ‘Sig 2-tail’ or ‘Sig. 2’.
We will call this “p”.
Compare the two and apply the following rule:
If “p” is < or = significance level, than you reject the null.
Be sure to explain to the reader what this means in regards to your study.
(Ex: will you recommend counseling services?)
* Be sure that your answers are clearly distinguishable.
Perhaps you bold your font or use a different color.
This assignment is due no later than Sunday of Week 6 by 11:55 pm ET.
Save the file in the following format: [your last name_SOCI332_A2].
The file must be a word file.
t Tests
t Test for a Single Sample (20 points)
Open SPSS
Enter the number of activities of daily living performed by the depressed clients studied in #1 in the Data View window.
In the Variable View window, change the variable name to “ADL” and set the decimals to zero.
Click Analyze
Compare Means
One-Sample T test
the arrow to move “ADL” to the Variable(s) window.
Enter the population mean (17) in the “Test Value” box.
Click OK.
Researches are interested in whether depressed people undergoing group therapy will perform a different number of activities of daily living after group therapy. The researchers have randomly selected 12 depressed clients to undergo a 6-week group therapy program.
Use the five steps of hypothesis testing to determine whether the average number of activities of daily living (shown below) obtained after therapy is significantly different from a mean number of activities of 17 that is typical for depressed people. (Clearly indicate each step).
Test the difference at the .05 level of significance a.
Your Paper was well written, however; I need you to follow the frochellscroop
Your Paper was well written, however; I need you to follow the following Analysis Guidance for Intervention Data. I will give you a passing grade when you submit with these by the 26th of April at 1pm EST
This document is designed to provide a summary of the key steps for analysing intervention data. The main analysis is conducted using the general linear model function in SPSS. This document does not cover how to clean data for analysis. (Data for the PARS module has already been cleaned so students do not have to undertake this part of the analysis.) This document is written with the PARS assignment in mind, so please refer to statistical texts for details on how to check assumptions, and a broader overview of how to interpret the output of intervention analyses in SPSS.
Preparing Scales
When using scales, ensure you compute scale reliabilities (Cronbachs Alpha using the function Analyse>Scale>Reliability analysis). Make sure scales are recoded as required by the specific scale you’re using. If you find poor reliability, that might indicate scale items have not been coded as required (e.g. a scale item may need reverse coding). If scale reliability is poor, then you may want to exclude it from the analysis, remove a low-loading item, or report why you think the reliability is poor and justify why you decided to include it. Scale items should be aggregated or averaged using the compute variable function in SPSS (Transform>Compute variable) for the main analysis, as directed by the scale authors. (For the PARS assignment, scale reliability statistics can be reported in the appendix.)
Calculating Means and Standard Deviations
It is useful at this stage to calculate the means and standard deviations for the data using the function Analyse>Descriptive Statistics. For intervention data comparing more than one condition, you need to isolate a condition in the dataset before generating the means and standard deviations for that condition. The analyses testing the effect of an intervention with individuals in different conditions (i.e. between-subject) are essentially testing whether there is a significant difference in the means of groups in different conditions. The means for the different conditions show whether levels are increasing or decreasing, and this is useful for interpreting the results of the analysis.
Isolate study conditions using the function Data>Select cases, and use the function ‘If condition satisfied’. In the PARS data, use cohort as the variable in the rule (i.e. ‘Cohort = 1’ for the intervention group, or ‘Cohort = 2’ for the control group). When you have either of these rules applied, SPSS will only run the analysis on the cases selected by that rule. For example, if the rule applied is ‘Cohort = 1’ only cases with the value 1 in the cohort variable will be included in the analysis.
Bivariate Correlations
As part the analysis, you need to run bivariate correlations. Use the function Analyse>Correlate>Bivariate. (For ...
Chapter 18 – Pricing Setting in the Business WorldThere are few .docxrobert345678
Chapter 18 – Pricing Setting in the Business World
There are few Methods for setting pricing – costs methods vs demand methods
Formulas considering costs and mark up will help you to do the Problem set assignment:
1. Markup for setting prices (Mark up $ = SP-CP); MARK UP % = (MU $/SP) X100)
Formula for setting price with the markup method
SP = Cost/(1- Markup %)
Example - retailer buys A hat for $15 and wants a 40% markup, his selling price would be….
SP = 15/(1-.40)=.60
= $25.00
2. Understand Role of different costs – fixed, variable, total costs and average costs
3. What is the breakeven point? Formula for calculating the Break Even point.
BE = Total Fixed Cost/Fixed cost contribution
Fixed Cost Contribution=Price – variable cost
4. Average Cost = when there are many flavors/types of the same product, producer determines average cost and then add the mark up to set a common selling price.
.ANOVA
Analysis of Variance is a method of testing the equality of three or more population
means by analyzing sample variance.
One-Way ANOVA
The one-way ANOVA is used to compare three or more population means when there is
one factor of interest.
Requirements
The populations have distributions that are approximately normal.
The populations have the same variance.
The samples are simple random samples of quantitative data.
The samples are independent of each other.
The different samples are from populations that are categorized in only one.
way
One-Way ANOVA is a hypothesis test. There are seven steps for a hypothesis test.
Example
A professor at a local University believes there is a relationship between head size and
the major of the students in her biostatistics classes. She takes a random sample of 20
students from each of three classes and records their major and head circumference.
The data are shown in the following table.
Step 1: State the null hypothesis.
Mean 1 equals mean 2 equals mean 3 equals mean 4.
Step 2: State the Alternative hypothesis.
At least one mean is different.
Step 3: State the Level of Significance.
The level of significance is 0.05.
Step 4: State the test statistic.
variance between samples
variance within samples
F
The test statistic follows the F distribution which has two degrees of freedom, one for
the numerator and one for the denominator.
The calculations for the test statistic are complicated, so a software program is
generally used for the calculations. We will be using Microsoft Excel for this example.
Step 5: Calculate
The calculations are done in Microsoft Excel using the data analysis toolpak. Enter the
data into the spread sheet as shown here. Click on data and the data analysis tookpak
button is on the right.
When you click on the button a dialogue box appears.
Choose ANOVA One Factor. Then another dialogue box appears.
Input range is where the data is in the table. Be sure to put a check in the box for labels
in.
How do I do a T test, correlation and ANOVA in SpssSolution .pdfamitseesldh
How do I do a T test, correlation and ANOVA in Spss?
Solution
One-way between-subjects ANOVA A one-way between-subjects ANOVA allows
you to determine if there is a relationship between a categorical independent variable (IV) and a
continuous dependent variable (DV), where each subject is only in one level of the IV. To
determine whether there is a relationship between the IV and the DV, a one-way between-
subjects ANOVA tests whether the means of all of the groups are the same. If there are any
differences among the means, we know that the value of the DV depends on the value of the IV.
The IV in an ANOVA is referred to as a factor, and the different groups composing the IV are
referred to as the levels of the factor. A one-way ANOVA is also sometimes called a single
factor ANOVA. A one-way ANOVA with two groups is analogous to an independent-samples t-
test. The pvalues of the two tests will be the same, and the F statistic from the ANOVA will be
equal to the square of the t statistic from the t-test. To perform a one-way between-subjects
ANOVA in SPSS • Choose Analyze General Linear Model Univariate. • Move the DV to the
Dependent Variable box. • Move the IV to the Fixed Factor(s) box. • Click the OK button. The
output from this analysis will contain the following sections. • Between-Subjects Factors. Lists
how many subjects are in each level of your factor. • Tests of Between-Subjects Effects. The row
next to the name of your factor reports a test of whether there is a significant relationship
between your IV and the DV. A significant F statistic means that at least two group means are
different from each other, indicating the presence of a relationship. You can ask SPSS to provide
you with the means within each level of your between-subjects factor by clicking the Options
button in the variable selection window and moving your withinsubjects variable to the Display
Means For box. This will add a section to your output titled Estimated Marginal Means
containing a table with a row for each level of your factor. The values within each row provide
the mean, standard error of the mean, and the boundaries for a 95% confidence interval around
the mean for observations within that cell. Post-hoc analyses for one-way between-subjects
ANOVA. A significant F statistic tells you that at least two of your means are different from
each other, but does not tell you where the differences may lie. Researchers commonly perform
post-hoc analyses following a significant ANOVA to help them understand the nature of the
relationship between the IV and the DV. The most commonly reported post-hoc tests are (in
order from most to least liberal): LSD (Least Significant Difference test), SNK (Student-
Newman-Keuls), Tukey, and Bonferroni. The more liberal a test is, the more likely it will find a
significant difference between your means, but the more likely it is that this difference is actually
just due to chance. 14 Although it is the most liberal, simulations ha.
I need this done ASAP, You have to have SPSS Software on your comput.docxanthonybrooks84958
I need this done ASAP, You have to have SPSS Software on your computer. Please do not request to do the assignment if you don't have the software or if you do not have the understanding to get this assignment complete.
Assignment 2: Tests of Significance
Throughout this assignment you will review mock studies.
You will needs to follow the directions outlined in the section using SPSS and decide whether there is significance between the variables.
You will need to list the five steps of hypothesis testing (as covered in the lesson for Week 6) to see how
every
question should be formatted.
You will complete all of the problems.
Be sure to cut and past the appropriate test result boxes from SPSS under each problem and explain what you will do with your research hypotheses.
All calculations should be coming from your SPSS
.
You will need to submit the SPSS output file to get credit for this assignment.
This file will save as a .spv file and will need to be in a single file.
In other words, you are not allowed to submit more than one output file for this assignment.
The five steps of hypothesis testing when using SPSS are as follows:
State your research hypothesis (H
1
) and null hypothesis (H
0
).
Identify your significance level (.05 or .01)
Conduct your analysis using SPSS.
Look for the valid score for comparison.
This score is usually under ‘Sig 2-tail’ or ‘Sig. 2’.
We will call this “p”.
Compare the two and apply the following rule:
If “p” is < or = significance level, than you reject the null.
Be sure to explain to the reader what this means in regards to your study.
(Ex: will you recommend counseling services?)
* Be sure that your answers are clearly distinguishable.
Perhaps you bold your font or use a different color.
This assignment is due no later than Sunday of Week 6 by 11:55 pm ET.
Save the file in the following format: [your last name_SOCI332_A2].
The file must be a word file.
t Tests
t Test for a Single Sample (20 points)
Open SPSS
Enter the number of activities of daily living performed by the depressed clients studied in #1 in the Data View window.
In the Variable View window, change the variable name to “ADL” and set the decimals to zero.
Click Analyze
Compare Means
One-Sample T test
the arrow to move “ADL” to the Variable(s) window.
Enter the population mean (17) in the “Test Value” box.
Click OK.
Researches are interested in whether depressed people undergoing group therapy will perform a different number of activities of daily living after group therapy. The researchers have randomly selected 12 depressed clients to undergo a 6-week group therapy program.
Use the five steps of hypothesis testing to determine whether the average number of activities of daily living (shown below) obtained after therapy is significantly different from a mean number of activities of 17 that is typical for depressed people. (Clearly indicate each step).
Test the difference at the .05 level of significance a.
Your Paper was well written, however; I need you to follow the frochellscroop
Your Paper was well written, however; I need you to follow the following Analysis Guidance for Intervention Data. I will give you a passing grade when you submit with these by the 26th of April at 1pm EST
This document is designed to provide a summary of the key steps for analysing intervention data. The main analysis is conducted using the general linear model function in SPSS. This document does not cover how to clean data for analysis. (Data for the PARS module has already been cleaned so students do not have to undertake this part of the analysis.) This document is written with the PARS assignment in mind, so please refer to statistical texts for details on how to check assumptions, and a broader overview of how to interpret the output of intervention analyses in SPSS.
Preparing Scales
When using scales, ensure you compute scale reliabilities (Cronbachs Alpha using the function Analyse>Scale>Reliability analysis). Make sure scales are recoded as required by the specific scale you’re using. If you find poor reliability, that might indicate scale items have not been coded as required (e.g. a scale item may need reverse coding). If scale reliability is poor, then you may want to exclude it from the analysis, remove a low-loading item, or report why you think the reliability is poor and justify why you decided to include it. Scale items should be aggregated or averaged using the compute variable function in SPSS (Transform>Compute variable) for the main analysis, as directed by the scale authors. (For the PARS assignment, scale reliability statistics can be reported in the appendix.)
Calculating Means and Standard Deviations
It is useful at this stage to calculate the means and standard deviations for the data using the function Analyse>Descriptive Statistics. For intervention data comparing more than one condition, you need to isolate a condition in the dataset before generating the means and standard deviations for that condition. The analyses testing the effect of an intervention with individuals in different conditions (i.e. between-subject) are essentially testing whether there is a significant difference in the means of groups in different conditions. The means for the different conditions show whether levels are increasing or decreasing, and this is useful for interpreting the results of the analysis.
Isolate study conditions using the function Data>Select cases, and use the function ‘If condition satisfied’. In the PARS data, use cohort as the variable in the rule (i.e. ‘Cohort = 1’ for the intervention group, or ‘Cohort = 2’ for the control group). When you have either of these rules applied, SPSS will only run the analysis on the cases selected by that rule. For example, if the rule applied is ‘Cohort = 1’ only cases with the value 1 in the cohort variable will be included in the analysis.
Bivariate Correlations
As part the analysis, you need to run bivariate correlations. Use the function Analyse>Correlate>Bivariate. (For ...
Chapter 18 – Pricing Setting in the Business WorldThere are few .docxrobert345678
Chapter 18 – Pricing Setting in the Business World
There are few Methods for setting pricing – costs methods vs demand methods
Formulas considering costs and mark up will help you to do the Problem set assignment:
1. Markup for setting prices (Mark up $ = SP-CP); MARK UP % = (MU $/SP) X100)
Formula for setting price with the markup method
SP = Cost/(1- Markup %)
Example - retailer buys A hat for $15 and wants a 40% markup, his selling price would be….
SP = 15/(1-.40)=.60
= $25.00
2. Understand Role of different costs – fixed, variable, total costs and average costs
3. What is the breakeven point? Formula for calculating the Break Even point.
BE = Total Fixed Cost/Fixed cost contribution
Fixed Cost Contribution=Price – variable cost
4. Average Cost = when there are many flavors/types of the same product, producer determines average cost and then add the mark up to set a common selling price.
.ANOVA
Analysis of Variance is a method of testing the equality of three or more population
means by analyzing sample variance.
One-Way ANOVA
The one-way ANOVA is used to compare three or more population means when there is
one factor of interest.
Requirements
The populations have distributions that are approximately normal.
The populations have the same variance.
The samples are simple random samples of quantitative data.
The samples are independent of each other.
The different samples are from populations that are categorized in only one.
way
One-Way ANOVA is a hypothesis test. There are seven steps for a hypothesis test.
Example
A professor at a local University believes there is a relationship between head size and
the major of the students in her biostatistics classes. She takes a random sample of 20
students from each of three classes and records their major and head circumference.
The data are shown in the following table.
Step 1: State the null hypothesis.
Mean 1 equals mean 2 equals mean 3 equals mean 4.
Step 2: State the Alternative hypothesis.
At least one mean is different.
Step 3: State the Level of Significance.
The level of significance is 0.05.
Step 4: State the test statistic.
variance between samples
variance within samples
F
The test statistic follows the F distribution which has two degrees of freedom, one for
the numerator and one for the denominator.
The calculations for the test statistic are complicated, so a software program is
generally used for the calculations. We will be using Microsoft Excel for this example.
Step 5: Calculate
The calculations are done in Microsoft Excel using the data analysis toolpak. Enter the
data into the spread sheet as shown here. Click on data and the data analysis tookpak
button is on the right.
When you click on the button a dialogue box appears.
Choose ANOVA One Factor. Then another dialogue box appears.
Input range is where the data is in the table. Be sure to put a check in the box for labels
in.
Print CopyExport Output InstructionsSPSS output can be selectiv.docxstilliegeorgiana
Print Copy/Export Output Instructions
SPSS output can be selectively copied and pasted into Word by using the Copy command:
1. Click on the SPSS output in the Viewer window.
2. Right-click for options.
3. Click the Copy command.
4. Paste the output into a Microsoft Word document.
The Copy command will preserve the formatting of the SPSS tables and charts when pasting into Microsoft Word.
An alternative method is to use the Export command:
1. Click on the SPSS output in the Viewer window.
2. Right-click for options.
3. Click the Export command.
4. Save the file as Word/RTF (.doc) to your computer.
5. Open the .doc file.
IBM SPSS Step-by-Step Guide: Histograms and Descriptive Statistics
Note: This guide is an example of creating histograms and descriptive statistics in SPSS with the grades.sav file. The variables shown in this guide do not correspond with the actual variables assigned in Assessment 1. Carefully follow the Assessment 1 instructions for a list of assigned variables. Screen shots were created with SPSS 21.0.
Section 1: Histograms and Visual Interpretation
Refer to your Assessment 1 instructions for a list of assigned Section 1 variables. The example variable final is shown below.
Step 1. Open grades.sav in SPSS.
Step 2. On the Graphs menu, point to Legacy Dialogs and click Histogram…
Step 3. In the Histogram dialog box:
First, select your assigned variable for Assessment 1. Move it to the Variable box. The exampleof final appears below.
Second, select Display normal curve.
Third, move gender to the Rows box.
Fourth, click OK to generate the histogram output.
Step 4. The histogram output appears in SPSS. Copy your SPSS output. To do this, right-click on the image and then click Copy.
Step 5. Open a new Word document and right-click for the paste options. Paste the histogram output into the Word document. Below the histograms, write up your visual interpretations as described in your Assessment 1.
Section 2: Calculate and Interpret Measures of Central Tendency and Dispersion
Refer to the Assessment 1 instructions for a list of assigned Section 2 variables. The examplevariable final is shown below.
Step 1. On the Analyze menu, point to Descriptive Statistics and click Descriptives…
Step 2. In the Descriptives dialog box:
First, select your Assessment 1 variables. Move them to the Variable(s) box. The examplevariable final is shown below.
Second, click the Options button.
Third, select Mean, Std. deviation, Kurtosis, and Skewness. For Display Order, select Variable list.
Fourth, click Continue and then click OK. The descriptives output is generated in SPSS for your Assessment 1 variables.
Step 3. Copy the descriptive statistics output and paste it into your Word document. Below the output, write up your analyses as described in the Assessment 1 instructions.
1
Remove or Replace: Header Is Not Doc Title
Assessment 1 ContextTransitioning from Descriptive Statistics to Inferential Statistics
In this assessment ...
Assignment 2 Tests of SignificanceThroughout this assignmen.docxkarenahmanny4c
Assignment 2: Tests of Significance
Throughout this assignment you will review mock studies. You will needs to follow the directions outlined in the section using SPSS and decide whether there is significance between the variables. You will need to list the five steps of hypothesis testing (as covered in the lesson for Week 6) to see how
every
question should be formatted. You will complete all of the problems. Be sure to cut and past the appropriate test result boxes from SPSS under each problem and explain what you will do with your research hypotheses.
All calculations should be coming from your SPSS
. You will need to submit the SPSS output file to get credit for this assignment. This file will save as a .spv file and will need to be in a single file. In other words, you are not allowed to submit more than one output file for this assignment.
The five steps of hypothesis testing when using SPSS are as follows:
State your research hypothesis (H
1
) and null hypothesis (H
0
).
Identify your significance level (.05 or .01)
Conduct your analysis using SPSS.
Look for the valid score for comparison. This score is usually under ‘Sig 2-tail’ or ‘Sig. 2’. We will call this “p”.
Compare the two and apply the following rule:
If “p” is < or = significance level, than you reject the null.
Be sure to explain to the reader what this means in regards to your study. (Ex: will you recommend counseling services?)
* Be sure that your answers are clearly distinguishable. Perhaps you bold your font or use a different color.
This assignment is due no later than Sunday of Week 6 by 11:55 pm ET. Save the file in the following format: [your last name_SOCI332_A2]. The file must be a word file.
t Tests
t Test for a Single Sample (20 points)
Open SPSS
Enter the number of activities of daily living performed by the depressed clients studied in #1 in the Data View window.
In the Variable View window, change the variable name to “ADL” and set the decimals to zero.
Click Analyze
à
Compare Means
à
One-Sample T test
à
the arrow to move “ADL” to the Variable(s) window.
Enter the population mean (17) in the “Test Value” box.
Click OK.
1.
Researches are interested in whether depressed people undergoing group therapy will perform a different number of activities of daily living after group therapy. The researchers have randomly selected 12 depressed clients to undergo a 6-week group therapy program.
Use the five steps of hypothesis testing to determine whether the average number of activities of daily living (shown below) obtained after therapy is significantly different from a mean number of activities of 17 that is typical for depressed people. (Clearly indicate each step).
Test the difference at the .05 level of significance and at the .01 level (in SPSS this means you change the “confidence level” from 95% to 99%).
As part of Step 5, indicate whether the behavioral scientists should recommend group therapy for all depressed people based.
This assignment tests your ability to correctly identify and apply.docxterirasco
This assignment tests your ability to correctly identify and apply statistical techniques, describe and interpret results, and present managerial recommendations. You will be graded on each of these concepts.
Always create your own tables to present the results. Simple copying and pasting the SPSS tables will
reduce your grade
(you may copy/paste histograms from SPSS).
First copying/pasting into Excel (or other) than copying/pasting into SPSS is the same thing. Be sure to create presentation quality tables from the raw data tables produced by SPSS.
Keep in mind that a table may not be necessary for every question. Assume any data errors you discover are double-entries which should be corrected to single-entries (e.g., 44 should be 4). Not finding and correcting these errors means your other answers will probably be wrong. Be sure to properly list results (e.g., sort means in descending order, etc.) rather than a random (i.e., lack of meaning) order.
Download the SPSS file “Avery Fitness Center MR 2015.sav” from Blackboard>Assignments. Refer to both the survey (p. 357) and codebook (p. 358) for guidance.
The class handouts and Chapters 17 and 18 provide examples of the needed techniques and interpretations. The class handouts and Chapters 19 and 20 provide guidance on how to best present your results. Unless specified otherwise, use an alpha of .05. Be sure to report all relevant values (e.g., chi-square, et cetera). Also use a 95% confidence level where appropriate unless told otherwise. The
Guide to Using SPSS
, the textbook, and the class handouts will help you use SPSS.
When done, submit a single Word document with your “lastname” in the filename on Blackboard>Assignments>SPSS Homework Submission.
NOTE
: The results you get will be DIFFERENT from the exhibits in the book!
Remember to use relevant data to thoroughly explain and analyze your answers. In addition to the correct answers, you will be graded on the clarity of communication including the appearance of exhibits (e.g. tables). Presenting the SPSS tables will lower your grade. The tables you submit should be created in either Word or Excel (or similar).
This is an
INDIVIDUAL
assignment. Sharing of answers, data, tables, analysis, et cetera is strictly prohibited. While it is acceptable to ask a classmate on how to use SPSS, you cannot work together on the analysis of the data, creation of the tables, or any other component of this project. If you do then you are violating the Academic Integrity policy and subject to receiving a failing grade for the
course
. Your papers will be submitted through turnitin.com (via Blackboard) and checked for originality. Furthermore each file will be inspected for evidence of collaboration.
Working side-by-side or sharing of files is a violation of the academic integrity policy and all parties involved will be reported to the Associate Provost. DO NOT allow someone else to have access to your files. You will be held responsible if s.
Analysis of variance (ANOVA) everything you need to knowStat Analytica
Most of the students may struggle with the analysis of variance (ANOVA). Here in this presentation you can clear all your doubts in analysis of variance with suitable examples.
Business Email Rubric Subject Line Subject line clea.docxjasoninnes20
Business Email Rubric
Subject Line
Subject line
clearly states the
main point of the
email
5points
Subject line is a
bit long (5+
words) or a bit
short (1 word) but
states the point of
the email
3points
Subject line does
not correspond to
the main point of
the email
2points
Subject line is
missing
0points
Greeting
Email includes a
professional
greeting that is
appropriate for the
audience; uses
the person's first
name
5points
Email includes a
professional
greeting that is
adequate for the
audience but uses
the person's first
and last name or
just the last name
3points
Email includes a
greeting but it is
not personalized
2points
Email lacks a
greeting
0points
Introductory
Comment
Email includes an
introductory
positive, relevant
comment
5points
it Email includes
an introductory,
positive comment
but may be very
general
3points
Email includes an
introduction only
2points
Email lacks an
introductory
comment
0points
Content
Purpose of the
email is clear, as
is the outcome;
content is succinct
and well organized
and does not
include
unnecessary
information
5points
Purpose of the
email is clear, as
is the outcome;
content could be
better organized
3points
Purpose of the
email is not clear
and/or content is
poorly organized;
it took more than
one reading to
understand what
the email is about
2points
Email seems to be
a collection of
unrelated
statements; it is
difficult to figure
out what the
purpose is
0points
Closing and
Signature
Email includes a
complementary
closing and
signature with all
required items
(name,
title/position,
company name,
phone number)
5points
Email includes a
complementary
closing and
signature but the
signature is
incomplete
3points
Email may be
missing either a
complementary
closing or
signature
2points
Email lacks a
closing and
signature
0points
Writing
Conventions
Email looks
professional and
does not have any
formatting or
writing errors
5points
Email is well
presented with
minimal (<3)
formatting or
writing errors
3points
Email includes
several (3+)
formatting or
writing errors
2points
Email is poorly
presented and has
an accumulation
of writing errors
that interfere with
readability
0points
BUS308 Week 3 Lecture 2
Examining Differences – ANOVA and Chi Square
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Conducting hypothesis tests with the ANVOA and Chi Square tests
2. How to interpret the Analysis of Variance test output
3. How to interpret Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
This week we introduced the ANOVA test for multiple mean equality and the Chi Square
tests for distrib ...
WEEK 6 – EXERCISES Enter your answers in the spaces pr.docxwendolynhalbert
WEEK 6 – EXERCISES
Enter your answers in the spaces provided. Save the file using your last name as the beginning of the file name (e.g., ruf_week6_exercises) and submit via “Assignments.” When appropriate,
show your work
. You can do the work by hand, scan/take a digital picture, and attach that file with your work.
1
.
A psychotherapist studied whether his clients self-disclosed more while sitting in an easy chair or lying down on a couch. All clients had previously agreed to allow the sessions to be videotaped for research purposes. The therapist randomly assigned 10 clients to each condition. The third session for each client was videotaped and an independent observer counted the clients’ disclosures. The therapist reported that “clients made more disclosures when sitting in easy chairs (
M
= 18.20) than when lying down on a couch (
M
= 14.31),
t
(18) = 2.84,
p
< .05, two-tailed.” Explain these results to a person who understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
2.
A researcher compared the adjustment of adolescents who had been raised in homes that were either very structured or unstructured. Thirty adolescents from each type of family completed an adjustment inventory. The results are reported in the table below. Explain these results to a person who understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
Means on Four Adjustment Scales for
Adolescents from Structured versus Unstructured Homes
Scale
Structured Homes
Unstructured Homes
t
Social Maturity
106.82
113.94
–1.07
School Adjustment
116.31
107.22
2.03*
Identity Development
89.48
94.32
1.93*
Intimacy Development
102.25
104.33
.32
______________________
*
p
< .05
3.
Do men with higher levels of a particular hormone show higher levels of assertiveness? Levels of this hormone were tested in 100 men. The top 10 and the bottom 10 were selected for the study. All participants took part in a laboratory simulation in which they were asked to role-play a person picking his car up from a mechanic’s shop. The simulation was videotaped and later judged by independent raters on each of four types of assertive statements made by the participant. The results are shown in the table below. Explain these results to a person who fully understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
Mean Number of Assertive Statements
Type of Assertive Statement
Group
1
2
3
4
Men with High Levels
2.14
1.16
3.83
0.14
Men with Low Levels
1.21
1.32
2.33
0.38
t
3.81**
0.89
2.03*
0.58
______________________
*
p
< .05;
**
p
< 0.1
4.
A manager of a small store wanted to discourage shoplifters by putting signs around the store saying “Shoplifting is a crime!” However, he wanted to make sure this would not result in customers buying less. To test this, he displayed the signs every other W.
Print CopyExport Output InstructionsSPSS output can be selectiv.docxstilliegeorgiana
Print Copy/Export Output Instructions
SPSS output can be selectively copied and pasted into Word by using the Copy command:
1. Click on the SPSS output in the Viewer window.
2. Right-click for options.
3. Click the Copy command.
4. Paste the output into a Microsoft Word document.
The Copy command will preserve the formatting of the SPSS tables and charts when pasting into Microsoft Word.
An alternative method is to use the Export command:
1. Click on the SPSS output in the Viewer window.
2. Right-click for options.
3. Click the Export command.
4. Save the file as Word/RTF (.doc) to your computer.
5. Open the .doc file.
IBM SPSS Step-by-Step Guide: Histograms and Descriptive Statistics
Note: This guide is an example of creating histograms and descriptive statistics in SPSS with the grades.sav file. The variables shown in this guide do not correspond with the actual variables assigned in Assessment 1. Carefully follow the Assessment 1 instructions for a list of assigned variables. Screen shots were created with SPSS 21.0.
Section 1: Histograms and Visual Interpretation
Refer to your Assessment 1 instructions for a list of assigned Section 1 variables. The example variable final is shown below.
Step 1. Open grades.sav in SPSS.
Step 2. On the Graphs menu, point to Legacy Dialogs and click Histogram…
Step 3. In the Histogram dialog box:
First, select your assigned variable for Assessment 1. Move it to the Variable box. The exampleof final appears below.
Second, select Display normal curve.
Third, move gender to the Rows box.
Fourth, click OK to generate the histogram output.
Step 4. The histogram output appears in SPSS. Copy your SPSS output. To do this, right-click on the image and then click Copy.
Step 5. Open a new Word document and right-click for the paste options. Paste the histogram output into the Word document. Below the histograms, write up your visual interpretations as described in your Assessment 1.
Section 2: Calculate and Interpret Measures of Central Tendency and Dispersion
Refer to the Assessment 1 instructions for a list of assigned Section 2 variables. The examplevariable final is shown below.
Step 1. On the Analyze menu, point to Descriptive Statistics and click Descriptives…
Step 2. In the Descriptives dialog box:
First, select your Assessment 1 variables. Move them to the Variable(s) box. The examplevariable final is shown below.
Second, click the Options button.
Third, select Mean, Std. deviation, Kurtosis, and Skewness. For Display Order, select Variable list.
Fourth, click Continue and then click OK. The descriptives output is generated in SPSS for your Assessment 1 variables.
Step 3. Copy the descriptive statistics output and paste it into your Word document. Below the output, write up your analyses as described in the Assessment 1 instructions.
1
Remove or Replace: Header Is Not Doc Title
Assessment 1 ContextTransitioning from Descriptive Statistics to Inferential Statistics
In this assessment ...
Assignment 2 Tests of SignificanceThroughout this assignmen.docxkarenahmanny4c
Assignment 2: Tests of Significance
Throughout this assignment you will review mock studies. You will needs to follow the directions outlined in the section using SPSS and decide whether there is significance between the variables. You will need to list the five steps of hypothesis testing (as covered in the lesson for Week 6) to see how
every
question should be formatted. You will complete all of the problems. Be sure to cut and past the appropriate test result boxes from SPSS under each problem and explain what you will do with your research hypotheses.
All calculations should be coming from your SPSS
. You will need to submit the SPSS output file to get credit for this assignment. This file will save as a .spv file and will need to be in a single file. In other words, you are not allowed to submit more than one output file for this assignment.
The five steps of hypothesis testing when using SPSS are as follows:
State your research hypothesis (H
1
) and null hypothesis (H
0
).
Identify your significance level (.05 or .01)
Conduct your analysis using SPSS.
Look for the valid score for comparison. This score is usually under ‘Sig 2-tail’ or ‘Sig. 2’. We will call this “p”.
Compare the two and apply the following rule:
If “p” is < or = significance level, than you reject the null.
Be sure to explain to the reader what this means in regards to your study. (Ex: will you recommend counseling services?)
* Be sure that your answers are clearly distinguishable. Perhaps you bold your font or use a different color.
This assignment is due no later than Sunday of Week 6 by 11:55 pm ET. Save the file in the following format: [your last name_SOCI332_A2]. The file must be a word file.
t Tests
t Test for a Single Sample (20 points)
Open SPSS
Enter the number of activities of daily living performed by the depressed clients studied in #1 in the Data View window.
In the Variable View window, change the variable name to “ADL” and set the decimals to zero.
Click Analyze
à
Compare Means
à
One-Sample T test
à
the arrow to move “ADL” to the Variable(s) window.
Enter the population mean (17) in the “Test Value” box.
Click OK.
1.
Researches are interested in whether depressed people undergoing group therapy will perform a different number of activities of daily living after group therapy. The researchers have randomly selected 12 depressed clients to undergo a 6-week group therapy program.
Use the five steps of hypothesis testing to determine whether the average number of activities of daily living (shown below) obtained after therapy is significantly different from a mean number of activities of 17 that is typical for depressed people. (Clearly indicate each step).
Test the difference at the .05 level of significance and at the .01 level (in SPSS this means you change the “confidence level” from 95% to 99%).
As part of Step 5, indicate whether the behavioral scientists should recommend group therapy for all depressed people based.
This assignment tests your ability to correctly identify and apply.docxterirasco
This assignment tests your ability to correctly identify and apply statistical techniques, describe and interpret results, and present managerial recommendations. You will be graded on each of these concepts.
Always create your own tables to present the results. Simple copying and pasting the SPSS tables will
reduce your grade
(you may copy/paste histograms from SPSS).
First copying/pasting into Excel (or other) than copying/pasting into SPSS is the same thing. Be sure to create presentation quality tables from the raw data tables produced by SPSS.
Keep in mind that a table may not be necessary for every question. Assume any data errors you discover are double-entries which should be corrected to single-entries (e.g., 44 should be 4). Not finding and correcting these errors means your other answers will probably be wrong. Be sure to properly list results (e.g., sort means in descending order, etc.) rather than a random (i.e., lack of meaning) order.
Download the SPSS file “Avery Fitness Center MR 2015.sav” from Blackboard>Assignments. Refer to both the survey (p. 357) and codebook (p. 358) for guidance.
The class handouts and Chapters 17 and 18 provide examples of the needed techniques and interpretations. The class handouts and Chapters 19 and 20 provide guidance on how to best present your results. Unless specified otherwise, use an alpha of .05. Be sure to report all relevant values (e.g., chi-square, et cetera). Also use a 95% confidence level where appropriate unless told otherwise. The
Guide to Using SPSS
, the textbook, and the class handouts will help you use SPSS.
When done, submit a single Word document with your “lastname” in the filename on Blackboard>Assignments>SPSS Homework Submission.
NOTE
: The results you get will be DIFFERENT from the exhibits in the book!
Remember to use relevant data to thoroughly explain and analyze your answers. In addition to the correct answers, you will be graded on the clarity of communication including the appearance of exhibits (e.g. tables). Presenting the SPSS tables will lower your grade. The tables you submit should be created in either Word or Excel (or similar).
This is an
INDIVIDUAL
assignment. Sharing of answers, data, tables, analysis, et cetera is strictly prohibited. While it is acceptable to ask a classmate on how to use SPSS, you cannot work together on the analysis of the data, creation of the tables, or any other component of this project. If you do then you are violating the Academic Integrity policy and subject to receiving a failing grade for the
course
. Your papers will be submitted through turnitin.com (via Blackboard) and checked for originality. Furthermore each file will be inspected for evidence of collaboration.
Working side-by-side or sharing of files is a violation of the academic integrity policy and all parties involved will be reported to the Associate Provost. DO NOT allow someone else to have access to your files. You will be held responsible if s.
Analysis of variance (ANOVA) everything you need to knowStat Analytica
Most of the students may struggle with the analysis of variance (ANOVA). Here in this presentation you can clear all your doubts in analysis of variance with suitable examples.
Business Email Rubric Subject Line Subject line clea.docxjasoninnes20
Business Email Rubric
Subject Line
Subject line
clearly states the
main point of the
email
5points
Subject line is a
bit long (5+
words) or a bit
short (1 word) but
states the point of
the email
3points
Subject line does
not correspond to
the main point of
the email
2points
Subject line is
missing
0points
Greeting
Email includes a
professional
greeting that is
appropriate for the
audience; uses
the person's first
name
5points
Email includes a
professional
greeting that is
adequate for the
audience but uses
the person's first
and last name or
just the last name
3points
Email includes a
greeting but it is
not personalized
2points
Email lacks a
greeting
0points
Introductory
Comment
Email includes an
introductory
positive, relevant
comment
5points
it Email includes
an introductory,
positive comment
but may be very
general
3points
Email includes an
introduction only
2points
Email lacks an
introductory
comment
0points
Content
Purpose of the
email is clear, as
is the outcome;
content is succinct
and well organized
and does not
include
unnecessary
information
5points
Purpose of the
email is clear, as
is the outcome;
content could be
better organized
3points
Purpose of the
email is not clear
and/or content is
poorly organized;
it took more than
one reading to
understand what
the email is about
2points
Email seems to be
a collection of
unrelated
statements; it is
difficult to figure
out what the
purpose is
0points
Closing and
Signature
Email includes a
complementary
closing and
signature with all
required items
(name,
title/position,
company name,
phone number)
5points
Email includes a
complementary
closing and
signature but the
signature is
incomplete
3points
Email may be
missing either a
complementary
closing or
signature
2points
Email lacks a
closing and
signature
0points
Writing
Conventions
Email looks
professional and
does not have any
formatting or
writing errors
5points
Email is well
presented with
minimal (<3)
formatting or
writing errors
3points
Email includes
several (3+)
formatting or
writing errors
2points
Email is poorly
presented and has
an accumulation
of writing errors
that interfere with
readability
0points
BUS308 Week 3 Lecture 2
Examining Differences – ANOVA and Chi Square
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Conducting hypothesis tests with the ANVOA and Chi Square tests
2. How to interpret the Analysis of Variance test output
3. How to interpret Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
This week we introduced the ANOVA test for multiple mean equality and the Chi Square
tests for distrib ...
WEEK 6 – EXERCISES Enter your answers in the spaces pr.docxwendolynhalbert
WEEK 6 – EXERCISES
Enter your answers in the spaces provided. Save the file using your last name as the beginning of the file name (e.g., ruf_week6_exercises) and submit via “Assignments.” When appropriate,
show your work
. You can do the work by hand, scan/take a digital picture, and attach that file with your work.
1
.
A psychotherapist studied whether his clients self-disclosed more while sitting in an easy chair or lying down on a couch. All clients had previously agreed to allow the sessions to be videotaped for research purposes. The therapist randomly assigned 10 clients to each condition. The third session for each client was videotaped and an independent observer counted the clients’ disclosures. The therapist reported that “clients made more disclosures when sitting in easy chairs (
M
= 18.20) than when lying down on a couch (
M
= 14.31),
t
(18) = 2.84,
p
< .05, two-tailed.” Explain these results to a person who understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
2.
A researcher compared the adjustment of adolescents who had been raised in homes that were either very structured or unstructured. Thirty adolescents from each type of family completed an adjustment inventory. The results are reported in the table below. Explain these results to a person who understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
Means on Four Adjustment Scales for
Adolescents from Structured versus Unstructured Homes
Scale
Structured Homes
Unstructured Homes
t
Social Maturity
106.82
113.94
–1.07
School Adjustment
116.31
107.22
2.03*
Identity Development
89.48
94.32
1.93*
Intimacy Development
102.25
104.33
.32
______________________
*
p
< .05
3.
Do men with higher levels of a particular hormone show higher levels of assertiveness? Levels of this hormone were tested in 100 men. The top 10 and the bottom 10 were selected for the study. All participants took part in a laboratory simulation in which they were asked to role-play a person picking his car up from a mechanic’s shop. The simulation was videotaped and later judged by independent raters on each of four types of assertive statements made by the participant. The results are shown in the table below. Explain these results to a person who fully understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
Mean Number of Assertive Statements
Type of Assertive Statement
Group
1
2
3
4
Men with High Levels
2.14
1.16
3.83
0.14
Men with Low Levels
1.21
1.32
2.33
0.38
t
3.81**
0.89
2.03*
0.58
______________________
*
p
< .05;
**
p
< 0.1
4.
A manager of a small store wanted to discourage shoplifters by putting signs around the store saying “Shoplifting is a crime!” However, he wanted to make sure this would not result in customers buying less. To test this, he displayed the signs every other W.
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
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”.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
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.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
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.
2024.06.01 Introducing a competency framework for languag learning materials ...
one-way-rm-anova-DE300.pdf
1. One-Way Repeated-Measures ANOVA
Analysis of Variance (ANOVA) is a common and robust statistical test that you can use to
compare the mean scores collected from different conditions or groups in an experiment.
There are many different types of ANOVA, but this tutorial will introduce you to One-Way
Repeated-Measures ANOVA.
A repeated-measures (or within-participants) test is what you use when you want to compare
the performance of the same group of participants in different experimental conditions. That
is, when the same participants take part in all of the conditions in your study.
The term one-way simply to refers to the number of independent variables you have; in this
case, one.
You would use this test when you have the following:
one dependent variable
one independent variable (with 3 or more conditions)
each participant takes part all conditions
This tutorial will show you how to run and interpret the output of a one-way repeated-
measures ANOVA using SPSS.
2. Worked Example
Let’s consider a simple memory experiment. Imagine that you are a researcher who is
interested in factors that influence people’s ability to remember lists of words. Specifically, you
want to know whether the relatedness of the words on the list influence people’s
ability to remember them.
As part of this study, participants could be given several different word lists to learn. The word
lists would need to be matched for length and frequency of use, but would vary according to
how the words related to one another
(the independent variable).
Participants would have to learn all three versions of the lists:
Unrelated words
Semantically related words
Phonemically related words
The number of words the remembered would be taken as the dependent variable.
This what the data looks like in SPSS. It can also be found in the SPSS file: ‘Week 2 Words
Data.sav’.
3. As a general rule in SPSS, each row in the spreadsheet should contain all of the data provided
by one participant. In a repeated measures design, this means that separate columns need to
represent each of the conditions of the experiment. In this example, we have one IV with three
levels, which means we need to have at least three columns of data.
Note that this differs from a between-participants design, where the conditions of the IV are
coded in a separate column.
In this example, the different columns display the following data:
id : This just refers to the ID number assigned to the participants. We use numbers as
identifiers instead of participant names, as this allows us to collect data while keeping the
participants anonymous.
unrelated: This column displays the number of words that participants successfully
remembered from the list made up from unrelated words
semantic: This column displays the number of words that participants successfully
remembered from the list made up from semantically related words
phonological: This column displays the number of words that participants successfully
remembered from the list made up from phonologically (or phonemically) related words
To start the analysis, begin by CLICKING on the Analyze menu, select the General Linear
Models, and then the Repeated Measures sub-option.
The “Repeated Measures Define Factor(s)” box should now appear. This is where we tell SPSS
what our repeated measures IV is, and how many levels it has. In this case, let’s call our IV
mouth_visibility by entering this into the Within-Subject Factor Name box. We can then tell
SPSS that there are 3 levels to this IV, using the Number of Levels box.
4. Once you have told SPSS your variable name, and how many levels (or conditions) it has, CLICK
on Add to add it to your analysis (see image above). Then CLICK on Define to continue.
This opens the Repeated Measures dialog box. In the Within-Subjects Variables window you
can see a series of question marks with bracketed numbers. SPSS will use these numbers to
represent the different levels of our IV. Your task is to replace the question marks with the
names of the conditions that match the variable levels.
To do this, highlight all of the condition names in the box, as is shown above. When doing this
yourself, remember that if you hold down the Ctrl key so you can highlight them all in one go.
Now CLICK on the top blue arrow to move them across.
5. By moving the conditions across in this way you are basically telling SPSS to code your variable
conditions as follows:
1 = unrelated
2 = semantic
3 = phonological
Now we have told SPSS what is it that we want to analyse, we are almost ready to run the
ANOVA. But first, we need to ask SPSS to produce some other information for us, to help us
better understand our data.
First, it’s often a good idea to illustrate your data by producing a graph. To do this, CLICK on the
Plots button on the right of the Univariate dialog box.
This opens the Profile Plots box.
The variable Word_List should already be selected for you. CLICK on the arrow to the left of
the Horizontal Axis box to add this variable to the plot. Then CLICK Add. Once this variable is
added to the Plots window (as above), CLICK Continue.
Next, we need to tell SPSS exactly what information we want it to produce as part
of the ANOVA. CLICK on the Options button in the Univariate box to do this.
6. To produce information for the
independent variable and its conditions,
SELECT Word_List in the Factors(s) box and
move it across to the Display Means for
box by CLICKING on the blue arrow.
Next, SELECT the Compare main effects
box. By doing this you can now choose
some Post Hoc tests1 to carry out by
CLICKING on the Confidence interval
adjustment drop down menu. This gives
you three tests to choose from. In this
case, SELECT Bonferroni.
In the bottom half of the Options dialog
box, there are a number of tick box options
that you can choose from. In this example
we are just going to select two.
1. First, we want SPSS to produce some descriptive statistics for us (like means and
standard deviations), so CLICK on this option.
2. It is good practice to report the effect size of your analysis in your write up, so SELECT
this option too
Unlike with Independent ANOVAs, we don’t need to select the homogeneity tests option here,
as these are only used for between-participants variables.
Once you’ve selected these two options, CLICK on continue.
We are now ready to run the analysis! CLICK OK in the Univariate dialog box to
continue.
You can now view the results in the Output window. Here, SPSS produces all of the statistics
that you asked for. There is quite a lot of output for Analysis of Variance, but don’t worry - this
tutorial will talk you through the output you need, box by box.
1
Post Hoc tests cannot be carried out using the Post Hoc… button in the Univariate dialog box for repeated
measures variables. They have to be done this way; but this method can also be used for Independent tests.
7. Within-Subjects Factors
This box is here to remind you which numbers SPSS has assigned
to the different levels of your independent variable. You may find
it useful to refer back to this when interpreting your output.
From looking at this box you should be able to see that there are
three levels to the factor Word_List, where:
1 = unrelated
2 = semantically related
3 = phonologically related
8. Descriptive Statistics
This table gives you your descriptive statistics. Looking at these will show you the patterns in
your data. You will also need to report them in your write up.
From this table we can see that on average, the highest number of words were correctly
remembered when the words on the list were unrelated (mean = 9.03; SD = 2.27), followed by
the semantically related list (mean = 8.40; SD = 1.91), and fewest were recalled from the
phonologically related word list (mean = 7.47; SD = 2.60).
But to find out whether these observed differences are significant,
we need to look at the inferential statistics.
Multivariate Tests
This next table is not necessary for the interpretation of the ANOVA results, so feel free to
ignore it at this point!
Mauchly's Test of Sphericity
This table tests whether the assumption of sphericity has been met. This is a bit like the
assumption of homogeneity of variance for independent tests, but in this case it tests the
assumption that the relationship between the different pairs of conditions is similar. As it only
9. deals with pairs, you only need to look at this table when you have a variable with more than
two levels (i.e. more than one pair) - which we have in this case.
Like Levene's test for independent tests, we do not want Mauchly's test to be significant. In
this case, the assumption of sphericity has been met, as the p-value is more than .05. To
formally report this, we need the Chi-Square, df and p-values. Here we can say:
Mauchly’s test of sphericity showed that this assumption was met, χ2(2) = 4.24, p = .12
Tests of Within-Subjects Effects
This is the most important table in the output, as it gives you the results of your ANOVA. The
key columns you need to interpret your analysis are:
df stands for degrees of freedom. Degrees of freedom are crucial in calculating
statistical significance, so you need to report them. We use them to represent the size
of the sample, or samples used in the test. Don’t worry too much about the stats
involved in this though, as SPSS automatically controls the calculations for you.
With Repeated-Measures ANOVA, you need to report two of the df values:
1. One for the to the IV itself (in the row labelled Word_List)
2. And one to represent the error, which can be found in the Error row.
F stands for F-Ratio. This is the test statistic calculated by the ANOVA. You need to
report the F-value for your variable, which can be found in the Word_List row. It is
calculated by dividing the mean squares for the variable by its error mean squares.
Essentially, this is the systematic variance (i.e. the variation in your data that can be
explained by your experimental manipulation) divided by the unexpected, unsystematic
variance.
The larger your F-Ratio, the more likely it is that your experimental manipulation (your
IV) will have had a significant effect on the DV. If F < 1, your result will never be
10. significant, as this means there is more unexplained variance in your data than can be
accounted for by your IV.
Sig stands for Significance Level. This column gives you the probability that your results
could have occurred by chance, if the null hypothesis were true.
The convention is that the p-value should be smaller than 0.05 for the F-ratio to be
significant. If this is the case (i.e. p < 0.05) we reject the null hypothesis, inferring that
the results didn’t occur by chance (or as the result of sampling error) but are instead
due to the effect of the independent variable. However, if the p-value is larger than
0.05, then we have to retain the null hypothesis; that there is no difference between the
conditions.
Partial Eta Squared. While the p-value can tell you whether the difference between
conditions is statistically significant, partial eta squared (ηp
2) gives you an idea of how
different your samples are. In other words, it tells you about the magnitude of your
effect. As such, we refer to this as a measure of effect size.
To determine how much of an effect your IV has had on the DV, you can use the
following cut-offs to interpret your results:
o 0.14 or more are large effects
o 0.06 or more are medium effects
o 0.01 or more are small effects
So we know we need to look at the df, F, Sig and Partial Eta Squared columns... the question is
which numbers do we use?
Which rows you choose depends on whether your assumption of Sphericity has been met. If
your Mauchley’s test was non-significant (i.e. the assumption had been met), or if you had less
than 3 levels in your IV, then you would read across from the Sphericity Assumed rows. This is
what we need to do in this example.
However, if Mauchley’s test had been significant, then you would need to use one of the other
rows (usually Greenhouse-Geisser).
As the sphericity assumption was met in this example, we can use the Sphericity Assumed row.
As such, we can report the results as:
F (IV df, error df) = F-ratio, p= Sig, ηp
2= Partial Eta Squared
...along with a sentence, explaining what we have found. In this case you might say:
There was a significant main effect of Word_List on the number of words that participants
remembered (F(2, 58) = 6.96, p = .002, ηp
2=.19).
11. But what does this effect mean? To explain this we need to refer back to our Descriptive
Statistics to see what is happening in each of our different conditions.
To do this, skip over the next two output tables and go straight to your Estimated Marginal
Means.
Estimated Marginal Means
This box shows you the breakdown
of the means for the different levels
of your IVs. For a one-way ANOVA,
the mean values are the same as
those displayed the Descriptive
Statistics box at the beginning of
your output.
This box also gives you the Standard Error rather than the Standard Deviation for your different
conditions. Sometimes you need to report this… but not in this example.
As a quick reminder of what the means tells us, we can see that the fewest number of words
were remembered from the phonologically related list, followed by the semantically related
and the unrelated words.
Pairwise Comparisons
The Pairwise Comparisons box essentially does what the title implies. It carries out multiple
comparisons between every possible combination of pairs for your conditions.
The Pairwise Comparisons table essentially does what the title implies. It carries out multiple
comparisons between every possible combination of pairs for your conditions. While your
ANOVA result tells you that there is a significant effect of word list, this table tells you exactly
which pairs of conditions are significantly different from one another.
12. To remember which numbers refer to which condition, look back at your first output box. In
this case: 1 = unrelated, 2 = semantically and 3 = phonologically related.
Reading this table is actually very simple. In the first column we can see the list of our three
conditions. For each of the conditions, we can then read across each of the rows to see which
groups are being compared.
To find out whether these comparisons are significant, we need to look at the Sig column (and
the asterisks in the Mean Difference column). Remember, the p-value needs to be less than
.05 to be significant.
Here we can see that there is only one significant difference between the pairs:
between the unrelated (1) and phonologically related (3) word lists.
Multivariate Tests
You do not need this box to interpret your output. Skip over this and go straight to your plot.
Profile Plots
This is the final part of
your output. The
Means Plot graph can
often be useful in
helping you to visualise
your results and it is a
good idea to include it
in your write up.
Here we can see that
the most words were
remembered in the
unrelated condition (1),
followed by the
semantically related (2)
and phonologically
related (3) conditions.
13. How do we write up our results?
When writing up the findings from your analysis in APA format, you need to include
ALL of the relevant information from the output.
What were the inferential statistics for your IV?
o i.e. what was the ANOVA result
If your finding was significant, where do the significant differences lie?
o i.e. what were the results of your post hoc tests
What was the pattern and direction of these differences?
o i.e. what were the means and descriptive statistics for your conditions
For this example, you might end up writing a results section that looks a bit like this:
The results of the one-way repeated-measures ANOVA showed that there was a
significant main effect of Word List on the average number of words participants
successfully remembered (F(2, 58) = 6.96, p = .002, ηp
2=.19).
Bonferroni post hoc tests showed that participants remembered significantly more
words from the unrelated word list (mean = 9.03; SD = 2.27) compared to the
phonologically related word list (mean = 7.47; SD = 2.60; p=.003). However, neither
condition significantly differed from participants’ performance with the semantically
related word list (mean = 8.40; SD = 1.91).
This evidence supports the hypothesis that the relatedness of words on a list affects
participants’ ability to remember them. Specifically, these findings suggest that it is
harder to remember phonologically related words than unrelated words.
It can also be helpful to both you and the reader to include a table of the means and standard
deviations for all of the conditions in your results sections. Alternatively, you might want to
include a graph of your results. You can alter your graph by double clicking on it in the output
in SPSS.
This brings us to the end of the tutorial. Why not download the dataset used in this tutorial and
see if you can produce the same output on your own.