SPSS for Research Methods: A Basic Guide

by Georjeanna Wilson-Doenges

Overview

A new guide showing the best ways for students to analyze data and interpret results in SPSS.

Georjeanna Wilson-Doenges’s direct writing style, real sample data from her research methods class, integrated APA-style results, and detailed yet clear screenshots ensure students will be confident and enthusiastic about using SPSS.

Product Details

ISBN-13:	9780393938821
Publisher:	Norton, W. W. & Company, Inc.
Publication date:	12/19/2014
Edition description:	New Edition
Pages:	208
Product dimensions:	7.80(w) x 9.90(h) x 0.30(d)

About the Author

Georjeanna Wilson-Doenges is Chair and Professor of Psychology at the University of Wisconsin-Green Bay. She received her PhD from the University of California, Irvine. Her research interests involve individuals’ interpretation of sense of community, student engagement in college, experiential learning, and assessment of undergraduate psychology learning outcomes. She has taught research methods and statistics courses with an SPSS lab component for over 25 years. Year after year, she has refined her teaching to help students who are often fearful of data analysis by making her courses engaging, and to promote success in even the most math-insecure students. Throughout her career, Dr. Wilson-Doenges has emphasized teaching and writing about data analysis in clear and accessible ways.

Table of Contents

Preface ix

An Introduction to SPSS xvii

Chapter 1 SPSS Data Entry Basics 3

In this chapter we introduce the SPSS environment and walk students through the basics of creating or importing a data set in SPSS. We then cover the basics of data set-up.

Before We Get Started: Navigating in SPSS 3

1.1 Windows in SPSS 3

1.1a The Data Window 4

1.1b The Output Window 5

1.1c Syntax Window 6

1.2 Navigating and Commands in SPSS 7

1.3 Importing Data from Other Sources 7

1.3a Importing Data from Excel 8

1.3b Importing Data from Online Survey Software: Example-Qualtrics 10

1.4 Data Setup and Manual Entry 11

1.4a Setting Up for Data Entry through Variable View 12

1.4b Manual Data Entry in Data View 15

1.5 Data Transformation 16

1.5a Recoding Values 18

1.5b Collapsing or Combining Values 21

1.5c Computing Summative Scales 23

Chapter 2 Getting a Feel for Your Data: Descriptive Statistics 27

The first step in any research project is getting a good sense of the main qualities of the data. In this chapter we show students how to calculate basic descriptive statistics focusing on the most common statistics and ways to illustrate the data.

Using Descriptive Statistics 28

Before We Get Started: Identify Your Variable Type 28

Frequency Tables and Basic Graphs 28

2.1 Categorical Data: Describing Our Data with Frequency Tables and Graphs 29

2.2 Quantitative Data: Describing Our Data with Frequency Tables and Graphs 32

2.3 Descriptive Statistics: Measures of Central Tendency and Variability for Quantitative Data 39

2.4 Crosstabular Tables: Describing Categorical Data with Crosstabs 43

Correlational Research: Testing Association Claims

Chapter 3 Looking for Relationships: Correlation, Reliability, and Chi-Square 51

The correlation coefficient is one of the most common statistics out there to understand association claims. In this chapter, you will learn the fundamentals of interpreting associations using graphs, the application of the correlation for understanding relationships and reliability, and the use of chi-square for understanding relationships among categorical variables.

Before We Get Started: Types of Variables and Association Claims 53

3.1 Using Scatterplots to Show Association between Two Quantitative Variables 53

13.2 Using Bivariate Correlation to Show Association between Two Quantitative Variables 56

3.3 Using Correlation to Test Reliability: Internal, Test-Retest, and Inter-Rater Reliability 59

3.3a Internal Reliability 60

3.3b Test-Retest Reliability 62

3.3c Inter-Rater Reliability 62

3.4 Using Chi-Square to Test Association Claims for Two Categorical Variables 66

Experimental Research: Making Causal Claims

Chapter 4 Simple Experiments:Testing Simple Differences in Means 75

Comparing sample means is one of the most common statistical procedures used when a researcher employs an experimental design to test a hypothesis. This chapter will cover the independent-samples t-test that compares two sample means to test for a significant difference between groups, one-way analysis of variance that compares two or more sample means to test for significant differences between groups, and the paired samples t-test that compares two sample means for groups that are paired in some way to test for a significant difference between groups.

Before We Get Started: Between-Groups versus Within-Groups Designs 78

4.1 Testing Between-Group Experimental Differences: Independent Samples t-Tests 78

4.2 Testing Between-Group Experimental Differences: One-Way ANOVA 83

4.3 Testing Within-Group Experimental Differences: Paired-Samples t-Tests 91

Chapter 5 Taking It Up a Notch: Multivariate Experiments with Analysis of Variance 97

When the complexity of comparing means goes beyond two sample means or just one independent variable (as in Chapter 4), you should use more complex forms of ANOVA. This chapter will cover the basics of ANOVA with multiple independent variables including testing for sample mean differences considering the combined effect of two independent variables (factorial), looking at main effects and interactions, and using more complex designs involving paired data (repeated measures).

Before We Get Started: Between-Groups versus Within-Groups Designs 99

5.1 Testing Complex Between-Group Experimental Differences: Factorial ANOVA 100

5.1a Graphing an Interaction 107

5.2 Testing Complex Within-Group Experimental Differences: Repeated Measures ANOVA 112

Chapter 6 Predicting Outcomes Using Multiple Regression 123

Regression analyses provide a glimpse into bow science begins to explain behavior by demonstrating bow different variables can be used to predict outcomes. Multiple Regression allows you to statistically control for third variables when making association claims. In this chapter, you will learn multiple regression analyses., including testing for mediation and moderation.

Before We Get Started: Types of Variables and Association Claims 124

6.1 Testing for Association among Multiple Variables: Multivariate Linear Regression 125

6.2 Testing for Association among Multiple Variables: Hierarchical Linear Regression 131

6.3 Using Regression to Test for Mediation 138

Step 1 Association c. Motivation. Predicting GPA 140

Step 2 Association a. Motivation Predicting Positive Teacher Perception 142

Step 3 Association b. Positive Teacher Perception Predicting GPA 143

Step 4 Association c Again. Motivation and Positive Teacher Perception Predicting GPA 145

6.4 Using Correlation to Test for Moderation 147

Chapter 7 Analyzing Data with Nonparametric Statistics 159

Many research projects use data that cannot be analyzed using the statistics we've covered so far because the data fail to meet the assumptions of the parametric test. In this chapter, you will learn how to use a range of common nonparametric statistics that could be used wben your data do not meet para metric assumptions.

Before We Get Started: Why Use Nonparametric Statistics? 159

7.1 Spearman's Rho as a Measure of Association between Two Variables 160

7.2 Mann-Whitney U Test as a Test of Differences between Two Means 164

7.3 Kruskal-Wallis Test as a Test of Between-Group Differences 169

7.4 Wilcoxon Signed-Rank Test as a Test of Within-Group Differences 176

Photo Credits 182

Index 183