This course aims to provide participants with a thorough understanding of fundamental statistical concepts and the practical application of statistical software tools, SPSS and Jamovi, for data analysis. The course covers descriptive statistics, contingency tables, relative risk and odds ratios, confidence intervals, hypothesis testing, and various statistical tests, enabling participants to analyze and interpret data effectively.

Course Topics and Objectives:

1. Descriptive Statistics
   • Learn to summarize and describe the main features of a data set, including measures of central tendency (mean, median, mode) and measures of variability (range, variance, standard deviation).
2. Introduction to SPSS
   • Gain proficiency in using SPSS software for data entry, manipulation, and basic statistical analysis.
3. Introduction to Jamovi
   • Develop skills in using Jamovi for statistical analysis, including data import, exploration, and basic computations.
4. Contingency Tables
   • Understand how to create and interpret contingency tables to analyze the relationship between categorical variables.
5. Relative Risk (RR) and Odds Ratio (OR)
   • Calculate and interpret relative risk and odds ratio to assess the strength of association between exposure and outcome in epidemiological studies.
6. Confidence Intervals
   • Learn to calculate and interpret confidence intervals to estimate the precision of sample statistics and make inferences about population parameters.
7. Hypothesis Testing
   • Understand the principles of hypothesis testing, including null and alternative hypotheses, p-values, and statistical significance.
8. How to Choose Statistical Tests
   • Develop the ability to select appropriate statistical tests based on data type, research questions, and study design.
9. Paired t-Test
   • Conduct and interpret paired t-tests to compare means from two related groups.
10. Wilcoxon Rank-Sum Test
    • Apply the Wilcoxon rank-sum test for non-parametric comparison of two related samples.
11. Unpaired t-Test
    • Perform and interpret unpaired t-tests to compare means from two independent groups.
12. Mann-Whitney U Test
    • Utilize the Mann-Whitney U test for non-parametric comparison of two independent samples.
13. Logic of ANOVA
    • Understand the logic and application of Analysis of Variance (ANOVA) to compare means across multiple groups.
14. Logic of Kruskal-Wallis Test
    • Learn the Kruskal-Wallis test for non-parametric comparison of more than two groups.
15. Logic of Chi-Square Test
    • Understand the chi-square test for independence and goodness of fit, including its assumptions and interpretations.

Course Outcomes:

By the end of this course, participants will be able to:

• Apply descriptive statistics to summarize and interpret data.
• Use SPSS and Jamovi software for various statistical analyses.
• Create and analyze contingency tables to understand relationships between categorical variables.
• Calculate and interpret relative risk, odds ratios, and confidence intervals.
• Perform hypothesis tests and select appropriate statistical tests for different data types and research questions.
• Conduct and interpret paired and unpaired t-tests, Mann-Whitney U tests, ANOVA, Kruskal-Wallis tests, and chi-square tests.
• Make informed decisions based on statistical analysis to support research findings and clinical practice.