We discuss the various types of tests of differences such as independent/dependent sample t-tests, ANOVAs, and MANOVAs. There will be a brief question and answer session at the end of the presentation.

1. How to Conduct and Interpret Tests
of Differences
Justin D’Souza
Quantitative Specialist

2. Table of
Contents
Statistics Solutions Services
Conducting Tests of Differences
SPSS Demo
Summary
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3. Services Offered
by Statistics
Solutions
• Topic Development
• Prospectus or Concept Papers
• Introduction Chapter
• Literature Review Chapter (identifying articles)
• Methodology Chapter (Quantitative/Qualitative).
• IRB forms
• Data entry templates
• Survey Monkey upload
• Results Chapter (Quantitative/Qualitative)
• Discussion Chapter
• Powerpoints for Defense
• Journal Publications
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4. Normality
Assumption
Normality Assumption
 Typically, parametric statistics (t-tests, ANOVAs, etc.) assume that the data follow a normal
(bell-shaped) distribution.
 There are various ways to check for normality (Shapiro-Wilk test, Kolmogorov-Smirnov
test, skewness/kurtosis, scatterplots/histograms). And if the data do not follow a normal
distribution, non-parametric techniques (Mann-Whitney U tests, Kruskal-Wallis tests, etc)
can potentially be used as an alternative.
 If you have a large sample size (>50), you can use the central limit theorem to justify using
parametric techniques even if tests normality are not showing a bell-shaped curve. Howell
(2013) states that violations of normality are not problematic when the sample size for
research exceeds 50 cases.
 Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Belmont CA:
Wadsworth Cengage Learning.
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5. Parametric and
Non-Parametric
Alternatives
Parametric Tests and Non-Parametric Alternatives
Parametric Test Non-Parametric Alternative
Independent sample t-test Mann-Whitney U test
ANOVA Kruskal Wallis test
Dependent sample t-test Wilcoxon-Signed Rank test
Repeated Measures ANOVA Friedman ANOVA

6. Independent Sample t-Test:
Assesses for differences in a
continuous variable between two
independent groups.

7. Setting Up
Independent
Sample t-test
 Need to have an independent variable that is dichotomous (two groups).
Examples: Gender (male vs female), group in experimental study (treatment vs
control)
 Need to have a continuous dependent variable.
 Check for assumptions of an independent sample t-test.
 https://statistics.laerd.com/spss-tutorials/independent-t-test-using-spss-
statistics.php
 If assumptions are not supported – consider Mann-Whitney U test or use sample
size to justify proceeding with analysis.
 Run your statistical analysis in Intellectus Statistics software.
Need help with your dissertation? Call 727-442-4290

8. Analysis of Variance (ANOVA):
Assesses for differences in a
continuous variable between two or
more independent groups.

9. Setting Up
ANOVA
 Need to have at least one independent variable that has two or more groups.
Examples: Ethnicity (White, Black, Asian, Hispanic, etc), Location (North,
South, East, West).
 Need to have a continuous dependent variable.
 Check for assumptions of an ANOVA.
 https://statistics.laerd.com/spss-tutorials/one-way-anova-using-spss-
statistics.php
 If assumptions are not supported – consider Kruskal-Wallis test or use sample
size to justify proceeding with analysis.
 Run your statistical analysis in Intellectus Statistics software.
Need help with your dissertation? Call 727-442-4290

10. Paired (Dependent) Sample t-Test:
Assesses for differences in a matched
continuous variable between two
points in time.

11. Setting Up
Dependent
(Paired) Sample
t-test
 Need to have one matched dependent variable that is measured two times.
 Check for assumptions of a dependent sample t-test:
 https://statistics.laerd.com/spss-tutorials/dependent-t-test-using-spss-
statistics.php
 If assumptions are not supported – consider Wilcoxon-Signed Rank test or use
sample size to justify proceeding with analysis.
 Run your statistical analysis in Intellectus Statistics software.
Need help with your dissertation? Call 727-442-4290

12. Repeated MeasuresANOVA:
Assesses for differences in a matched
continuous variable between two or
more points in time.

13. Setting Up
Repeated
Measures
ANOVA
 Need to have one matched dependent variable that is measured two or more
times.
 Check for assumptions of a repeated measures ANOVA:
 https://statistics.laerd.com/spss-tutorials/one-way-anova-repeated-
measures-using-spss-statistics.php
 If assumptions are not supported – consider Friedman ANOVA test or use
sample size to justify proceeding with analysis.
 Run your statistical analysis in Intellectus Statistics software.
Need help with your dissertation? Call 727-442-4290

14. Summary
 Identify independent grouping variables and dependent variables.
 Is goal to test for differences between groups, test for differences over time?
 Use Intellectus Statistics to analyze your data and present interpretation.
Need help with your dissertation? Call 727-442-4290

15. Additional
Support
Statistics Solutions is a full-service dissertation consulting
company providing graduate students timely, editorial
support for their dissertations and scholarly projects
For information about our services, receive a
complementary 30-min consultation available Mon-Fri 9-5
ET
Contact us at info@statisticssolutions.com
Phone: 727-442-4290