Module 4 Introduction

This module builds on earlier modules about understanding and working with data concerning diverse populations, social phenomena, and social work problems. First, we learn basics about coding and analyzing qualitative data. Then, we learn basics about analyzing quantitative data; these basics are further developed through completing several linked Excel workbook learning activities. In this module, we learn about conducting and interpreting basic univariate and bivariate statistical analyses related to proportion, frequency, probability, percentage, mean, median, mode, variance, normal distribution, standard deviation, bar graphs, pie charts, histograms, and scatterplots. We also learn about comparing different demographic groups using 5 statistical analytic approaches to test hypotheses (null and alternative hypothesis testing): single sample t-tests, independent sample t-tests, one-way analysis of variance, chi-square, and correlation. We become familiar with Type I and Type II errors, and with basic nonparametric principles, as well.

Reading Objectives

After engaging with these reading materials and learning resources, you should be able to:

• Describe how different types of qualitative data might be interpreted or analyzed;
• Recognize and practice univariate data analysis for different types of quantitative variables (categorical, dichotomous categorical, ordinal, and interval/continuous variable types);
• Explain the logic underlying inferential statistics and null hypothesis testing, as well as Type I and Type II errors;
• Identify underlying principles and practice 5 inferential statistics approaches with quantitative data (univariate and bivariate);
• Interpret results of one-sample t-test, independent samples t-test, one-way analysis of variance (Anova), chi-square, and correlation analyses;
• Recognize basic principles of non-parametric data analysis;
• Identify and practice steps in data analysis conducted using Excel software;
• Define key terms related to working with qualitative and quantitative data.