Easyguide to Research Design and SPSS

by Beth M. Schwartz, Janie H. Wilson, Dennis M. Goff

Overview

An Easy Guide to Research Design & SPSS provides clear and concise guidance for selecting the most appropriate design and statistical analysis to test a research hypothesis. By connecting experimental design with an appropriate statistical test for data analysis, the Easy Guide details the exact steps for inputting and analyzing data in SPSS, offers a “how-to” for interpreting the output from SPSS analyses, and provides guidance in formatting SPSS output into APA style.

Product Details

ISBN-13:	9781452288826
Publisher:	SAGE Publications
Publication date:	04/28/2014
Series:	Easyguide
Edition description:	New Edition
Pages:	277
Product dimensions:	6.65(w) x 8.96(h) x 0.45(d)

About the Author

Beth M. Schwartz is the William E. and Catherine Ehrman Thoresen ’23 Professor of Psychology and Assistant Dean of the College at Randolph College, in Lynch­burg, Virginia. She received a BA at Colby College (Maine) and a Ph D in cognitive psychology at the State University of New York at Buffalo. Her scholarship focuses on two areas of interest: (a) children’s memory development and how this applies to children’s eyewitness reports and (b) the scholarship of teaching and learn­ing/pedagogical research. In addition to numerous professional presentations at conferences, she has published many book chapters and articles in a variety of scholarly journals, including the Journal of Higher Education, Law and Human Behavior, and Applied Developmental Science. She has also edited and coauthored books, including Child Abuse: A Global View (Schwartz, Mc Cauley, & Epstein, 2001), Optimizing Teaching and Learning (Gurung & Schwartz, 2009), and Evidence-Based Teaching for Higher Education (Schwartz & Gurung, 2012). She is a member of the American Psychological Association (APA) and the American Psy­chological Society and is a Fellow of Division 2 of APA (Society for the Teaching of Psychology). She is an award-winning teacher at Randolph College, where she teaches Introduction to Psychology, Research Methods, Cognitive Psychology, and the capstone course. She received the Award for Outstanding Teaching and Mentoring from the American Psych-Law Society, the Gillie A. Larew Award for Distinguished Teaching at Randolph College, and the Distinguished Faculty Achievement Certificate from the State Council of Higher Education for Virginia.

Table of Contents

Section 1: Overview Basic Design Decisions
Chapter 1: Marriage – ‘Til Death Do Us Part
We Want to Help
Basic Steps of Research
Chapter 2: The Two Faces of Correlation
Correlation as a Research Design
Correlations as a Statistic
Chapter 3: Why Your Type fo Data Really Does Matter: Nominal, Ordinal, Interval, or Ratio
Nominal Data
Ordinal Data
Interval Data
Ratio Data
Chapter 4: To KISS (Keep It Simply Student) or to Complicate Matters: Nominal Data
How Many Variables Should I Include?
How Many Participants Should I Include
How Many Independent Variables ( IVs) Should I Include
Including More Than One Independnt Variables (IV)
Choosing the Number of Levels of Each Variable
Choosing Your Dependent Variables (DVs)
Avoiding the Un-Measurable Dependent Variables (DVs)
How Many Dependent Variables (DVs) To Include
Section II: Your Basic SPSS Tool Box
Chapter 5: Why SPSS and Not Other Software, Your Calculator, Fingers, or Toes
The Structure of SPSS
Chapter 6: SPSS Data Hygiene: Columns, and Labels, and Values….Oh My.
When to Create Your Data File: Yes Even Before Data Collection
Setting Up Your Data File
Naming and Labeling Your Variables
How to Keep Track and Remember the Details of Your Data File
Creating New Variables in Your Data File: Transformations
Conducting Analyses With Only Parts of Your Collected Data
Chapter 7: Tell Me About It: Descriptive Statistics
Describing Nominal Data
Describing Ordinal Data
Describing Interval of Ratio Data
Describing Data with Two Samples
Section III: Designs, Statistics, Interpretaion, and Write-Up in APA Style
Chapter 8: Celebrate Your Independence!: Between-Groups Designs
One IV, Two Levels
Between Groups with 2 Levels of an IV
Independent-Samples T-Test with a Pseudo-IV
Between Groups with More than 2 Levels of an IV
Chapter 9: Everybody Plays!: Repeated-Measures Designs
One Independent Variable (IV) with Two-Levels
Expanding the Number of Levels for Your Independent Variable
Adding Another Factor: Within-Subjects Factorial Designs
Chapter 10: Complicating Matters: Advanced Research Designs
Mixed Designs: One Between Variable and One Repeated-Measures
A Multivariate Design: Measuring It All Including More Than One Dependent Variable In Your Design
ANCOVA
Chapter 11: How do I know if That Relationship is Real? Correlations
Correlational Design: Two Variables
Interpreting Pearson’s r
Analyzing Your Data
Prediction with Two Variables: Simple Linear Regression
Prediction with Several Variables: Multiple Linear Regression
Chapter 12: Statistics Charades: Two Variables, Two Categories…..is it a Chi Square?
I Only Have One Question
One-way Chi Square With More Than Two Levels
Now I Only Have Two Questions
Section IV: Summary
Chapter 13: Mapping Your Decisions: You Can Get There From Here
Making Basic Decisions About Your Design
Data with Distinct Groups
Interval or Ratio Data with Many Levels
Chapter 14: Sample Results Sections for Each Type of Design
Independent-Samples T-Test (True IV)
Independent-Samples T-Test (Pseudo-IV)
One-Way, Between-Groups ANOVA (True IV)
Paired-Samples T-Test
One-Way, Within-Groups (Repeated-Measures) ANOVA
Two-Way, Within-Groups (Repeated-Measures) ANOVA
Pearson’s r Correlation
Pearson’s r Correlation and Regression
One-Way ^X2
Two-Way ^X2
Chapter 15: Did I do that? Taking Advice From the Experts
Questions About Research Design
Questions About Analyzing Your Data
Questions About Interpreting Your Data and Presenting Your Results
When Interpreting Your Data

Editorial Reviews

...Competition for books like this is fierce and the core elements of the book under review are very good: the language is expressive and always engaging, the examples are excellent and the integration of statistics and design is an essential one.

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