The document outlines how to conduct and interpret t-tests, including independent, paired, and one-sample tests, discussing the assumptions required for each type. It emphasizes the importance of checking for normality in data distributions and provides alternative non-parametric tests if those assumptions are not met. Additionally, it offers consulting services for dissertation support and encourages contacting the company for assistance.

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

2. Table of
Contents
Statistics Solutions Services
Conducting t-tests
Intellectus 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. Comparisons
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Comparison Parametric Test Non-Parametric Test
Differences between two
groups
Independent sample t-test Mann-Whitney U test
Differences between pretest
and posttest
Paired sample t-test Wilcoxon-Signed Rank test
Differences between variable
and specified value
One-sample t-test One-sample Wilcoxon test

5. Normality
Assumption
Normality Assumption
 Typically, parametric statistics 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 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.
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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, run a Mann-Whitney U test.
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8. Paired (Dependent) Sample t-Test:
Assesses for differences in a matched
continuous variable between two
points in time.

9. Setting Up Paired
Sample t-test
 Need to have one matched dependent variable that is measured two
times.
 Check for assumptions of a paired sample t-test:
 https://statistics.laerd.com/spss-tutorials/dependent-t-test-using-
spss-statistics.php
 If assumptions are not supported, run a Wilcoxon-Signed Rank
test.
Need help with your dissertation? Call 727-442-4290

10. One-Sample t-Test:
Assesses for differences between a
continuous variable and a specified
value.

11. Summary
 Identify independent grouping variables and dependent variables.
 Is goal to test for differences between groups, test for differences over time?
 Use statistical software (Intellectus Statistics, SPSS, Stata, etc.) to analyze your data
and present interpretations.
Need help with your dissertation? Call 727-442-4290

12. 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