SPSS for Academic Research

Course details

Explore how to run tests for academic research with SPSS, the leading statistical software. In this course, Yash Patel dives into SPSS, focusing on how to run and interpret data for the most common types of quantitative tests. Topics include t-tests, analysis of variance (ANOVA), and understanding the statistical measurements behind academic research. Review the tenants of qualitative testing, including the central theorem, P values, and confidence intervals, and specific use cases for tests in SPSS. For each type, Yash provides some general guidelines and assumptions, along with a challenge and solution exercise to practice what you've learned.

Learning objectives
Quantitative vs. qualitative analysis
Sample size considerations
Normal distribution
Estimating the population mean
One-sample t-test
Paired-sample t-test
One-way and two-way ANOVA
Repeated measure ANOVA

Concepts

0. Introduction

01 - Welcome
02 - What you should know
03 - Course setup and the exercise files

1. General Notions about Science and Research

04 - Social science and statistics
05 - Science vernacular
06 - Quantitative vs. qualitative

2. Quantitative Research Fundamentals

07 - Populations and samples
08 - Cyclical experimentation
09 - Sample size considerations
10 - Normal distribution
11 - The central limit theorem
12 - Estimate the population mean
13 - Z vs. T statistic
14 - Hypotheses, confidence intervals, and p-values

3. One-Sample T-Test

15 - Scenario and context
16 - Assumptions and hypotheses
17 - Data analysis in SPSS
18 - Interpretation of results
19 - Challenge - One-sample t-test
20 - Solution - One-sample t-test

4. Paired-Samples T-Test

21 - Scenario and context
22 - Assumptions and hypotheses
23 - Data analysis in SPSS
24 - Interpretation of results
25 - Challenge - Paired-samples t-test
26 - Solution - Paired-samples t-test

5. Balanced One-Way ANOVA

27 - Scenario and context
28 - Assumptions and hypotheses
29 - Data analysis in SPSS
30 - Interpretation of results
31 - Challenge - One-way ANOVA
32 - Solution - One-way ANOVA

6. Two-Way ANOVA

33 - Scenario and context
34 - Assumptions and hypotheses
35 - Data analysis in SPSS
36 - Interpretation of results
37 - Challenge - Two-way ANOVA
38 - Solution - Two-way ANOVA

7. Repeated Measures ANOVA

39 - Scenario and context
40 - Assumptions and hypotheses
41 - Data analysis in SPSS
42 - Interpretation of results
43 - Challenge - Repeated measures ANOVA
44 - Solution - Repeated measures ANOVA

Conclusion

45 - Next steps