- Janet Volen asked Dr. Alfonso Scandrett Jr to complete a descriptive analysis of her research project on using the Framingham Risk Score to initiate lifestyle changes and patient education by identifying individuals at risk of cardiovascular events.
- Dr. Scandrett analyzed demographic and clinical data from 50 participants, finding some significant relationships between variables. He created tables, graphs and conducted statistical tests like ANOVA, correlation matrices, and regression analysis.
- The results showed some relationships between variables but also areas that were not well explained, suggesting caution in fully accepting or rejecting the hypotheses. Dr. Scandrett provided analysis and interpretation of the results to Janet Volen.
Commonly Used Statistics in Medical Research Part IPat Barlow
This presentation covers a brief introduction to some of the more common statistical analyses we run into while working with medical residents. The point is to make the audience familiar with these statistics rather than calculate them, so it is well-suited for journal clubs or other EBM-related sessions. By the end of this presentation the students should be able to: Define parametric and descriptive statistics
• Compare and contrast three primary classes of parametric statistics: relationships, group differences, and repeated measures with regards to when and why to use each
• Link parametric statistics with their non-parametric equivalents
• Identify the benefits and risks associated with using multivariate statistics
• Match research scenarios with the appropriate parametric statistics
The presentation is accompanied with the following handout: http://slidesha.re/1178weg
Commonly Used Statistics in Medical Research Part IPat Barlow
This presentation covers a brief introduction to some of the more common statistical analyses we run into while working with medical residents. The point is to make the audience familiar with these statistics rather than calculate them, so it is well-suited for journal clubs or other EBM-related sessions. By the end of this presentation the students should be able to: Define parametric and descriptive statistics
• Compare and contrast three primary classes of parametric statistics: relationships, group differences, and repeated measures with regards to when and why to use each
• Link parametric statistics with their non-parametric equivalents
• Identify the benefits and risks associated with using multivariate statistics
• Match research scenarios with the appropriate parametric statistics
The presentation is accompanied with the following handout: http://slidesha.re/1178weg
Commonly used Statistics in Medical Research HandoutPat Barlow
We found this handout to be incredibly useful as a guide and resource for non-statistical professionals to make quick decisions about statistical methods. The handout accompanies the Commonly Used Statistics in Medical Research Part I Presentation
Commonly used Statistics in Medical Research HandoutPat Barlow
We found this handout to be incredibly useful as a guide and resource for non-statistical professionals to make quick decisions about statistical methods. The handout accompanies the Commonly Used Statistics in Medical Research Part I Presentation
Section 1 Data File DescriptionThe fictional data represents a te.docxbagotjesusa
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
Std. Error
GPA
Mean
2,78
,075
95% Confidence Interval for Mean
Lower Bound
2,63
Upper Bound
2,93
5% Trimmed Mean
2,80
Median
2,72
Variance
,583
Std. Deviation
,764
Minimum
1
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
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 th.
Journal of Personality and Social Psychology 1988, Vol, 54, MerrileeDelvalle969
Journal of Personality and Social Psychology
1988, Vol, 54, No. 6, 1063-1070
Copyright 1988 by the American Psychological Association, Inc.
0022-3514/88/Í00.75
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Development and Validation of Brief Measures of Positive
and Negative Affect: The PANAS Scales
David Watson and Lee Anna Clark
Southern Methodist University
Auke Tellegen
University of Minnesota
in recent studies of the structure of affect, positive and negative affect have consistently emerged as
two dominant and relatively independent dimensions. A number of mood scales have been created
to measure these factors; however, many existing measures are inadequate, showing low reliability
or poor convergent or discriminant validity. To fill the need for reliable and valid Positive Affect and
Negative Affect scales that are also brief and easy to administer, we developed two 10-item mood
scales that comprise the Positive and Negative Affect Schedule (PANAS). The scales are shown to be
highly internally consistent, largely uncorrelated, and stable at appropriate levels over a 2-month
time period. Normative data and factorial and external evidence of convergent and discriminant
validity for the scales are also presented.
Two dominant dimensions consistently emerge in studies of
affective structure, both in the United States and in a number
of other cultures. They appear as the first two factors in factor
analyses of self-rated mood and as the first two dimensions in
multidimensional scalings of facial expressions or mood terms
(Diener, Larsen, Levine, & Emmons, 1985; Russell, 1980,
1983; Stone, 1981; Watson, Clark, & Tellegen, 1984; Zevon &
Tellegen, 1982).
Watson and Tellegen (1985) have summarized the relevant
evidence and presented a basic, consensual two-factor model.
Whereas some investigators work with the unrotated dimen
sions (typically labeled pleasantness-unpleasantness and
arousal), the varimax-rotated factors—usually called Positive
Affect and Negative Affect—have been used more extensively in
the self-report mood literature; they are the focus of this article.
Although the terms Positive Affect and Negative Affect might
suggest that these two mood factors are opposites (that is,
strongly negatively correlated), they have in fact emerged as
highly distinctive dimensions that can be meaningfully repre
sented as orthogonal dimensions in factor analytic studies of
affect.
Briefly, Positive Affect (PA) reflects the extent to which a per
son feels enthusiastic, active, and alert. High PA is a state of
high energy, full concentration, and pleasurable engagement,
whereas low PA is chara ...
Data Analysis and Statistical inferenceShreyas G S
Analyzing relationship between two variables, whether they are dependent or independent variables in a General Social Survey data set using data analysis and inference techniques.
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.
Answer all questions individually and cite all work!!1. Provid.docxfestockton
Answer all questions individually and cite all work!!
1. Provide an example of an idea, creativity, and innovation and argue why it best fits that category.
2. Identify three catalysts to enable innovativeness. Explain how they would enable innovation in your organization.
3. Why is it significant that an organization allow for failure? What are some significant ways an organization can allow for failure and still find success?
4. Making a pivot has saved organizations from completely deteriorating. Research an organization of your choice that has made an impactful pivot. Write an 8-10 sentence summary of the organization and the monumental pivot.
iStockphoto/Thinkstock
chapter 11
Nominal Data and the
Chi-Square Tests: What Occurs
Versus What Is Expected
Learning Objectives
After reading this chapter, you will be able to. . .
1. describe nominal data.
2. complete and explain the chi-square goodness-of-fit test.
3. complete and explain the chi-square test of independence.
4. present and interpret the results of the two types of chi-square test in proper APA format.
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suk85842_11_c11.indd 407 10/23/13 1:45 PM
CHAPTER 11Section 11.1 Nominal Data
When there was an important development in statistical analysis in the early part of the 20th century, more often than not Karl Pearson was associated with it. Many of those
who made important contributions were members of the department that Pearson founded
at University College London. William Sealy Gosset, who developed the t-tests; Ronald A.
Fisher, who developed analysis of variance; and Charles Spearman, who developed factor
analysis, all gravitated to Pearson’s department at some point. Although social relations
among these men were not always harmonious, they were enormously productive schol-
ars, and this was particularly true of Pearson. Besides the correlation coefficient named for
him, Pearson also developed an analytical approach related to Spearman’s factor analysis
called principal components analysis, and he developed the procedures that are the sub-
jects of this chapter, the chi-square tests. The Greek letter chi (x) is pronounced “kie” like
“pie.” Chi is the equivalent of the letter c, rather than the letter x, which it resembles.
11.1 Nominal Data
With the exception of Spearman’s rho in Chapter 9, the attention in Chapters 1 through 10 has been directed at procedures designed for interval or ratio data. Sometimes
the data is not interval scale, nor is it the ordinal-scale data that Spearman’s rho accom-
modates. When the data is nominal scale, often one of the chi-square (x2) tests is used.
It will be helpful to review what makes data nominal scale. Nominal data either fits into a
category or it does not, which is why nominal data is sometimes called categorical or clas-
sification data. Because the analysis is based on counting the data, it is also called count or
frequency data. Compared to ratio, interva ...
Chapter 12Choosing an Appropriate Statistical TestiStockph.docxmccormicknadine86
Chapter 12
Choosing an Appropriate Statistical Test
iStockphoto/ThinkstockLearning Objectives
After reading this chapter, you will be able to. . .
· understand the importance of using the proper statistical analysis.
· identify the type of analysis based on four critical questions.
· use the decision tree to identify the correct statistical test.
Here we are in the final chapter that will pull all prior chapters together. Chapters 1 to 3 discussed descriptive statistics while the latterchapters, 4 to 11, discussed inferential statistics. Each of the inferential chapters presented a statistical concept then conducted the appropriateanalysis to be able to test a hypothesis. The big question for students learning statistics is, "How do I know if I'm using the correct statisticaltest?" For experienced statisticians this question is easy to answer as it is based on a few criteria. However, to a student just learning statisticsor to the novice researcher, this question is a legitimate one. Many statistical reference texts include a guide that asks specific questionsregarding the type of research question, design, number and scales of measurement of variables, and statistical assumption of the data thatallows you to use an elegant chart known as a decision tree. Based on the answers to these questions, the decision tree is used to helpdetermine the type of analysis to be used for the research, thereby helping you answer this big question.
12.1 Considerations
To make the correct decisions based on the use of a decision tree, there are four specific questions that must be answered. These questions areas follows:
· What is your overarching research question?
· How many independent, dependent, and covariate variables are used in the study?
· What are the scales of measurement of each of your variables?
· Are there violations of statistical assumptions?
If you are able to answer these specific questions, then you will be able to determine the proper analysis for your study. These questions arecritically important, and if they cannot be answered, then not enough thought has gone into the research. That said, let us discuss each ofthese questions so that they can be considered and answered in the use of the decision tree.
What Is Your Overarching Research Question?Try It!
Derive your ownresearch question foryour Master's Thesisor DoctoralDissertation. Have a colleague orprofessor read it. What are theirthoughts or suggestions forimprovements?
Answering this question seems simple enough as all research has an overarching research questionthat drives the study, especially since this dictates the type of quantitative methodology. There arekey words in every research question that help determine the appropriate type of analysis. Forinstance, if the research question states, "What are the effects of job satisfaction on employeeproductivity?" the keyword is "effects" as in the cause and effect of job satisfaction (theindependent variable) on productivity (th ...
Assessing Mediation in HIV Intervention Studiesfhardnett
This presentation describes the use of asymmetric confidence limits to test for mediation when the direct effect was not significant and effect suppression was present.
6
ONE-WAY BETWEEN-
SUBJECTS ANALYSIS OF
VARIANCE
6.1 Research Situations Where One-Way Between-Subjects
Analysis of Variance (ANOVA) Is Used
A one-way between-subjects (between-S) analysis of variance (ANOVA) is
used in research situations where the researcher wants to compare means on a
quantitative Y outcome variable across two or more groups. Group
membership is identified by each participant’s score on a categorical X
predictor variable. ANOVA is a generalization of the t test; a t test provides
information about the distance between the means on a quantitative outcome
variable for just two groups, whereas a one-way ANOVA compares means
on a quantitative variable across any number of groups. The categorical
predictor variable in an ANOVA may represent either naturally occurring
groups or groups formed by a researcher and then exposed to different
interventions. When the means of naturally occurring groups are compared
(e.g., a one-way ANOVA to compare mean scores on a self-report measure of
political conservatism across groups based on religious affiliation), the design
is nonexperimental. When the groups are formed by the researcher and the
researcher administers a different type or amount of treatment to each group
while controlling extraneous variables, the design is experimental.
The term between-S (like the term independent samples) tells us that each
participant is a member of one and only one group and that the members of
samples are not matched or paired. When the data for a study consist of
repeated measures or paired or matched samples, a repeated measures
ANOVA is required (see Chapter 22 for an introduction to the analysis of
repeated measures). If there is more than one categorical variable or factor
included in the study, factorial ANOVA is used (see Chapter 13). When there
is just a single factor, textbooks often name this single factor A, and if there
are additional factors, these are usually designated factors B, C, D, and so
forth. If scores on the dependent Y variable are in the form of rank or ordinal
data, or if the data seriously violate assumptions required for ANOVA, a
nonparametric alternative to ANOVA may be preferred.
In ANOVA, the categorical predictor variable is called a factor; the
groups are called the levels of this factor. In the hypothetical research
example introduced in Section 6.2, the factor is called “Types of Stress,” and
the levels of this factor are as follows: 1, no stress; 2, cognitive stress from a
mental arithmetic task; 3, stressful social role play; and 4, mock job
interview.
Comparisons among several group means could be made by calculating t
tests for each pairwise comparison among the means of these four treatment
groups. However, as described in Chapter 3, doing a large number of
significance tests leads to an inflated risk for Type I error. If a study includes
k groups, there are k(k – 1)/2 pairs of means; thus, for a set of four groups, the .
Module 3 - CasePERFORM THE RESEARCHCase AssignmentThe Situatio.docxbunnyfinney
Module 3 - Case
PERFORM THE RESEARCH
Case Assignment
The Situation
It is argued that perceptions of service quality vary across cultural groups, as defined by each culture's position on Hofstede's dimensions. The relationship is explicitly mapped between service quality perceptions and cultural dimension positions and the implications drawn for international service market segmentation. The hypotheses constituting their theoretical analysis are also tested. It is shown that the importance of SERVQUAL dimensions is correlated with Hofstede's cultural dimensions. Correlation coefficients are also used to compute a Cultural Service Quality Index that could be used to segment international service markets and allocate resources across segments.
Case Resource
Furrer, Olivier; Shaw-Ching, Ben; & Sudharshan (2000). The Relationships between Culture and Service Quality Perceptions: Basis for Cross-Cultural Market Segmentation and Resource Allocation.
Journal of Service Research,
2:4:(May):355-72. Available November 30, 2012 via EBSCO
Upload your 4-6 page paper by the module deadline.
Assignment Expectations
In preparing CASE3, that is, in preparing your analysis of Furrer, Shaw-Ching, & Sudharshan (2000), ensure that you demonstrate your learning of the marketing research concepts and frameworks for analysis outlined as follows:
Evaluate a Questionnaire
Describe the tradeoffs that need to be made between research design, cost, project implementability, and expected results
Problem
? What is the driving force or main purpose behind this article? Is the problem important (yes or no)? Why was this article written? Defend your positions on all of these issues.
Central hypothesis
? What is the main proposition that the author is trying to express/explore? Is your (the central) hypothesis best classified as descriptive, explanatory, or predictive/causal? Does the main hypothesis call for a measure of association or a measure of difference between two variables? What is the theoretical basis of your (the central) hypothesis? Does this hypothesis logically flow from and relate to the theorized constructs and relationships presented as the basis for the research or was it picked out of thin air? Defend your positions on all of these issues.
Research design
? Is the study and experiment, a quasi-experiment, or a correlation? Defend your position on this issue
Construct Validity
? In your (the central) hypothesis, look for a description of how the cause (that is, the independent (or predictor variable(s)) and the effect (that is, the dependent (or criterion variable(s)) are being measured. Face Validity: Do the measures measure what they are supposed to measure? Internal reliability: Are the measures reliable? What level of measurement is applied to these variables (for example, for each, identify if they are nominal, ordinal, scalar). What is the unit of analysis (for example, is it individual, group, corporate, societal)? Does the unit of analysis match bet.
Be sure to support with reference to the week’s Learning Resources.docxgarnerangelika
Be sure to support with reference to the week’s Learning Resources and other scholarly evidence in APA Style.
Continuous Variable
The continuous variable I selected is “Problems w/Public Health Clinics”. The means for this data is 7.43, the median is 7.4335 and the mode is 0. For the variable, the mean is the best measure it incorporates every value of this variable making it more representative of the variable (Frankfort-Nachmias, Leon-Guerrero, & Davis, 2020). The standard deviation is 5.11961, this number represents the difference of the data points from the mean. This number means that the scores regarding problems with health clinics differed from the mean by 5.11961. The variance is 26.210 describes the degree to which the data points are spread. I would describe this variable as having a lot of variability and extreme. Those that had no problems with public clinics more than double the amount of those that had a median amount of problems and triple those that had lots of problems.
Categorical Variable
Categorical variables are nominal or ordinal and cannot be described with mean or median. The best central tendency for these variables are mode, which is the score that shows up most frequently (Frankfort-Nachmias, Leon-Guerrero, & Davis, 2020). The categorical variable I selected is “country’s present economic condition compared to 12 months ago”, is mode is a tie for same and better all with the frequency of 2872 and a percent of 27.8%. This variable has low variability. Most of the participants believe there has been a small change or no change in economic conditions over the past 12 months.
References
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.
Wagner, III, W. E. (2020).
Using IBM® SPSS® statistics for research methods and social science statistics
(7th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 4, “Organization and Presentation of Information”
Chapter 11, “Editing Output”
.
Be sure to support with reference to the week’s Learning Resources.docx
Final analysis & Discussion_Volen
1. JANET VOLEN
A RESEARCH PROTOCOL
Prepared by:
Dr. Alfonso Scandrett Jr
Medical Epidemiologist
6317 High View Road
Greensboro, North Carolina 27410
Research Investigator:
Ms. Janet Volen
October 4, 2011
Ms. Janet Volen of Columbus, Ohio, has asked me to complete the descriptive analysis for the project
entitled:
“The use of the Framingham Risk Score to initiate lifestyle modification and for patient
education, by identifying men and women at increased risk for future cardiovascular events.”
I have provided the following objective analysis:
I have undertaken the preliminary research and will provide recommendations to the client.
Fully addressed and provided the client with the scope of the project
Individual consultation on research project
Provide suggestions for possible improvements to your methodology and instrument.
The appropriate statistical procedures were used based on the nature of the project and the
hypothetical outcomes.
I have actively input data on to a tally sheet for stat evaluation using the “MYSTAT” statistical
analysis program.
I have run statistical analysis of your data upon your request.
I have helped with the statistical interpretation of that data upon your request.
(As it is not in my scope of expertise, I was not instructed to write or assist in the writing of the
final thesis or project paper for this agreement).
2. Materials
1. I have sent an analysis that is descriptive in nature.
2. I have sent under a separate email cover graphs and statistical data runs (optional to keep)
3. I have sent under an email mailing a “EXCEL” copy with attachments that should enhance this
descriptive analysis and should be included as graphs and charts in your final presentation.
4. Knowing that you need to write the final paper I will leave up to you how you feeland would like
the supportive materials to be used. Of course, you can also develop your own as well.
ANALYSIS OF THE PROJECT
Goal:
1. The Framingham Score will be used as the dependent variable to conduct lifestyle changes for
patient education by identifying men and women who are at risk for future cardiovascular events.
Methodology
Along with the descriptive information of all research categories,a reliability coefficient was performed.
Standard statistical measures (means,standard deviations) are reported. To determine possible
interactions and main effects between demographic and clinical variables, a two-way analysis of variance
and student T-Test was completed. Other analysis such as regression analysis, factorial analysis and
discriminate analysis were also reviewed for possible use. The evaluation process was begun with the
examination of two data groups: demographics and clinical. The demographic variables were: Age,
Gender, Race,Marital Status, Income, Education, Employment and Living conditions as describe in Table
1. The clinical variables covered Age, Total Cholesterol, HDL,Systolic Blood Pressure,the Framingham
and Comparable Risk scores. The mean scores,standard deviation with confidence intervals, and range
were also provided in Table 1. P-Values were calculated at the .05 significant levels for each variable. A
significant P-value suggests a degree of heterogeneity among those variable whereas those variable that
were not significant suggest a more homogeneity in nature in the responses of those subjects. However,a
degree of caution regarding generalizing a statement about these variable should be considered because of
the small population number (n=50).
Demographics:
The total number of participants (n=50) were measured over a range of demographic variables (gender,
age, sex, ethnicity, marital status,education, income, and Living situations). Specifics to the different
types of response can be viewed by examining the demographic sheet. A number of variables showed
significant p-values. Which suggest an overall mean score difference among the subjects of that particular
variable and their response?
The Story Line:
To determine if any relationship existed between research categories,a correlation matrix was
designed. Either the variable strength or the role they play directly or indirectly in to the research
question is important. To determine strength a Pearson Moment Correlations is used. The cut off for
practical use this study was set at r=.30 which is based on Witte’s (1993) recommendation for making
correlation comparisons (See CORRELATION MATRIX). However values that were >r=.40 were
only reported. I am reporting that the relationship between research categories was low although
statistical significance was obtained in a number of the relationships. My overall goal was to identify
patterns among the variables. Please note the following relationships:
3. PCS 2 – PCS1 .91 HDL – PCS2 .43
HDL – PCS1 .46 AGE – ComRisk .60
MCS2 – MCS1 .92 TChol – SYS_BP .46
FramScore – Tchol. .63 HDL – FramScore .56
FramScore-ComRisk .56
NOTE:It is very interesting to me that your intervention, which is the
Framingham scores,did not correlation well with both your pre and posttest
components. However,to save face I will say that the .3 correlations that I failed
to list with PCS1 and PCS2 are fine. I am just mentioning to you that that later in
my discussion I point out some issue regarding this especially in the area of
internal consistency and t-test. Make a note of this observation.
A demographic analysis using all variables was conducted to be able to determine size and strength of
each variable and the role that each variable might play in its relationship to the research question.
(See “SOCIODEMOGRAPHIC ANDCLINICALVARIBLES”)
Bar graphs are provided (see my email) to show visual demographics of selected demographic
variables and clinical variables. Equally Chi-Sq cross tabs were used in order to demonstrate variable
associations. The P-value that is seen with the Chi-Sq was set at the .05 degree of significances.
Variables were treated as nominal and categorical class variables. Coding on the excel data sheet (see
data sheet) shows the coding that was used in order to describe each variable category.
The pre and post variables of MCS1, MCS2, PCS1 and PCS2 were set in a single factor (one-way)
ANOVA to determine the variance between them. (See TABLE A). Table A demonstrates a non-
significant relationship between these variables. This means that the means of each variable is close
in nature and do not suggest any special conditions that would highlight any one particular variable as
being a major player in your study. In nonprofessionals’ terms, this would mean that your posttest
response is not a factor after your intervention.
A factorial analysis was conducted to determine those variables that would be good indicators for
proving the research hypothesis. Both demographic and clinical variables were loaded into the
factorial analysis. The process produced seven factors; two factors presented Eigen values of 1.4 to
2.9. The other five factors were too weak to mention. Its Eigen Value indicates the strength of a
factor. Eigen Values of less than one are usually not interpreted. It is important to mention that all
variables except for MCS2 loaded very strongly onto the first factor that had an Eigen Value of 2.9l
explaining 41.5 percent of the variance. The p-value to prove that there was one or more variables
that did not load was p=.000 with 21 degrees of freedom. The second Eigen Value equaled 1.4 which
itself explained 20.3 percent of the variance. So what does this all mean? The number of variables
that load on that particular factor generates a factor’s strength. For explanations sake:It is kind of like
a mathematical vote of support. Variables are loaded in order of their mathematical perceived
strength. You have two that show their strength in regards to the dependent variable. Both of these
Eigen values explain 61.8 percent of the explained variance, which means that we can attest to why
certain things are happening regarding you, hypothesis but we also know that there is 49.2 percent of
the variance that is not explained. So if 61.8 percent is explaining occurrences over 2/3 of the time
then can say that accepting the null we would maybe be able to explain what happening 2 out of 3
times. Are those odds that one would want to gamble on…..???
4. Those factors are then place into a Regression Analysis. In this study’s case Regression Analysis
were run. Each was given a DEPENDENT variable. The dependent variable used was the variable
that was suggested in the research question as ones that should be considered. Regression 1 had PCS2
as a dependent variable and Regression 2 had MCS2 as a dependent variable. The goal here is to
determine which of these INDEPENDENT variables explains the most variance as it relates to
answering your research question. The higher the variance means the better the chance of explaining
away the variance of the alternative Hypothesis. A lower variance means the greater the chance of
rejecting the original hypothesis. For explanation sake picture going to jail and having witnesses
who come forth to speak positively on yourbehalf.In orderto determine if those witnessesare strong
enough in your favorwe run a factorial analysisthen we put themon the witness stand to see how
much they truly do know in your favor… thisis the regression. Do you like my analogy?
o JANET> Please know that in the above explanation it is important for you
to understand that factors in a factor analysis are to determined (operative
word here is determined)which of your variables when placed together
with other variables are strong enough to be used to explain why
something is happening. In other words if your group had a significant
average score increase due to your intervention there are a factor of
variables working togetherin that intervention were the ones that caused it.
HOWEVER, the factors in a regression are seeking to be more specific, by
identifying those specific independent variables that to explain variances.
Now with the above being said… Check out the following dialogue>>
Looking at PCS2 as the dependent variable …..the regression analysis found that the following
variables Comparative Risk, Framingham Scores,Systolic BP, HDL and Total Cholesterol explained
approximately 26 percent of the variance of the Dependent Variable. The question is what happened
to the rest of the variance. The remaining 74 percent is not explained and could suggest a Type-I error
if hypothesis is accepted. The MCS2 (Mental Health) variable only explains 16 percent of the
variance which leave 85 percent un-explained. To take the analysis a bit further what is being said
here is that all of the variables mentioned above are combine and through a regression analysis, they
do a very bad job in explaining the dependent variable of MSC2 and PCS2. The unexplained error in
both of the analysis could be due to a multitude of reason that is not explained here.
A type I error, also known as a false positive, occurs when a statistical test rejects a true null
hypothesis (H0). For example, if a null hypothesis states a patient is healthy, and the patient is
indeed healthy, but the test rejects this hypothesis, falsely suggesting that the patient is sick. The
rate of the type I error is denoted by the Greek letter alpha (α) and usually equals the significance
level (or size) of a test.
A type II error, also known as a false negative, occurs when the test fails to reject a false null
hypothesis. For example, if a null hypothesis states a patient is healthy, and the patient is in fact
sick, but the test fails to reject the hypothesis, falsely suggesting that the patient is healthy. The
rate of the type II error is denoted by the Greek letter beta (β) and related to the power of a test
(which equals 1-β).
The desired (i.e., non-erroneous) outcomes of the test are called true positive meaning "rejecting
null hypothesis, when it is false" and true negative meaning "not rejecting null hypothesis, when
it is true". A statistical test can either reject (prove false) or fail to reject (fail to prove false) a
5. null hypothesis, but never prove it true (i.e., failing to reject a null hypothesis does not prove it
true).
In colloquial usage type I error can be thought of as "convicting an innocent person" and type II
error "letting a guilty person go free".
Based on the hypothesis statement a T-Test was employed to either accept or reject the null
hypotheses of the research question. However,it should be noted that the T-test is not as rigorous of
an evaluations tool as the ANOVA. I need to point out that the ANOVA seems to be given more of
an acceptance of the null hypothesis where the t-test is showing MENTAL Health (MCS1 and MCS2)
have significant alpha. I did not run a post hoc test when looks at interactions between variables
because of this. The variable used in this analysis were:
Instrument Reliability
Here we are seeking to determine if the survey instrument was reliable. To determine if the research
categories were reliable, a Cronbach Alpha that estimates internal consistency was performed on the pre
and posttest variables (MCS1, PCS1,MCS2, and PCS2). The results showed an internal reliability
coefficient of .69. Whereas when just the internal components of MSC1 and MSC2 were combined, they
yield a higher alpha of .94 and PCS1 and PCS2 together had an alpha of .94. From a reporting standpoint,
this is excellent which means there is a strong reliability that your post-test responses based on your
intervention are measuring what you want them to measure. However,this does not mean that your
subjects are understanding and buying into what you are saying. Interestingly the Framingham Scores
when connected to the PCS2 response their alpha was consistent (.-96) but in a negative fashion. This was
not the case when the Mental Health responses were combine and examined (alpha=.01). These outcomes
suggest that the items in your Framingham score may be reliable and consistent with the items in your
Physical Health approach but not in the manner, you may want. This also may suggest that the items in
your Framingham scores when examined with Mental Health they are not consistent. Note that one
observation could be that the Framingham scores are physical in nature like those of Physical Health,
whereas the Mental Health is not. Just a point to look at.
T-test
However,the results of the t-test do not back up my above observation. The results of the t-test suggest
that your middle-aged subjects are more incline mentally then they are physically to response to your
intervention. I think being a nurse you might agree that it is easier to think about being healthier rather
than doing things to bring down one’s cholesterol level or BP or HDL and risk factors. Given the nature
of your populations, I would say this might be the case,especially if they are sedentary. Janet what do
you think???
Hypothesis #1:
Ho: > There will be no measured effect of reducing the risks of cardiovascular disease among
mid-life adults (men/women) when a low-intensity lifestyle intervention is introduced.
Ha:> There will be a measured effect of reducing the risks of cardiovascular disease among mid-
life adults (men/women) when a low-intensity lifestyle intervention is introduced.
6. Hypothesis: #2:
Ho:> The use of the SF-36v2 health and well-being survey is not an effective means for
measuring the Framingham Study as an intervention in reducing the risk of cardiovascular
disease.
Ha:> The use of the SF-36v2 health and well-being survey is an effective means for measuring
the Framingham Study as an intervention in reducing the risk of cardiovascular disease.
Discussion and Recommendation:
Based on the aforementioned evidence, it is my opinion that regarding both research hypotheses 1 and 2,
you should not accept the alternative hypothesis that there are significant changes in the way your
subjects responded to your interventions. Although there were some significant values shown in the
different analysis, the proof is clear those significant changes were at best borderline and acceptances of
either hypothesis based on one significant, t-test may indicate a type-1 error. To further support, this
claim I conducted a non-parametric test called Mann Whitney. This test is similar to the ANOVA that is
used for parametric analysis. The response to those analysis are presented in your exceldocuments. In
doing, this I believe you will find that even the Mann Whitney shows no significant differences in most of
your variables except a few. Briefly when examining both males and females the results seem to suggest
that there is a variance in the Framingham Intervention when gender is considered. Gender also seems to
be a factor among Comparative scores. A cross-tabulation would show which questions are causing the
concern if those data were available. Perhaps for another study. HDL seems to be an issue among the four
African-Americans of the study.
Recommendations:
Although significant changes in the data relative to this project were not sufficient because of the study
design:
It should be noted in your discussion that the instruments used (your survey) has been validated in
a previous studies where the population and parameters were stronger and could be used in future
clinical research. This should go in your recommendation or restrictions chapter.
It is also recommended that this study be continued but with larger populations numbers from a
more diverse and randomly selected population.
The manner in which the intervention was used may not have provided the means and methods
needed to show a significant mean response in the post-test. The related literature does show the
Framingham model as a strong and robust instrument that has been used to measure health
behaviors and health outcomes.
I would recommend if this research were going to be further examined that the research
statement be revamped to state the problem. It present form is confusing.
The individual categories that make up the Mental and Physical Health outcomes will be
very helpful in future analysis as they would yield a better and specific picture as to the
borderline data that is being presented.