Introduction to Data Analysis

Course Overview

This course provides a rigorous introduction to quantitative approaches to data analysis through descriptive and inferential perspectives. Topics include descriptive summaries of data, graphical representations, probability distributions, correlation, simple regression, hypothesis testing, and introduction to special topics of regression, factor analysis, and non-normal data distributions. Students will develop the knowledge and skills to analyze and interpret the data and report the findings to inform practical decisions based on context-specific needs. Students will learn to select appropriate statistical procedures, calculate statistics, and perform statistical tests by hand and through SPSS, interpret and write-up results.

Course Format

This class is scheduled as an in-person class; although, a few times this semester, as needed (e.g., due to professional travel) we will be meeting remotely during our scheduled day and time via Zoom. Work will include presentations covering course content (e.g., modules as organized around book chapters and other readings), demonstrations of and applied practice with statistical software, in- and out-of-class activities and homework assignments, and a final take-home assignment. Modules should be completed in sequential order (i.e., Module 0, Module 1, … Module 18).

Prerequisites

None, although a decent foundation in mathematics is desired

Textbooks

Required Text Salkind, N. J. (2016). Statistics for people who (think they) hate statistics (7th ed.). Sage Publications, Inc.

Grade Points

• A 90 - 100
• B 80 - 89
• C 70 - 79
• D 60 - 69 E (Failure) <60 There are no plus/minus grades in this course

Schedule

Week	Date	Topic	Materials
		Introduction to Course Course orientation and overview of expectations.
		Statistics or Sadistics? It's Up to You Foundations of statistical thinking and introductory concepts.
		Computing and Understanding Averages Measures of central tendency and descriptive statistics.
		Understanding Variability Variance, standard deviation, and dispersion.
		Creating Graphs Data visualization principles and graphical summaries.
		Correlation Computing and interpreting correlation coefficients.
		Reliability and Validity Measurement principles in social science research.
		Hypothesis Testing Logic of statistical inference and threats to validity.
		Probability and the Normal Distribution Probability theory and the bell-shaped curve.
		Statistical Significance p-values and interpretation of significance.
		One-Sample z Test Testing population means using z-tests.
		Independent Samples t-Test Comparing means between groups.
		Paired Samples t-Test Comparing related groups.
		Analysis of Variance (ANOVA) Comparing multiple group means.
		Factorial ANOVA Multi-factor experimental designs.
		Correlation and Relationships Interpreting statistical relationships.
		Linear Regression Prediction and modeling using regression.
		Chi-Square and Nonparametric Tests Statistical tests for categorical and non-normal data.
		Advanced Statistical Procedures Overview of additional important statistical techniques.