Cronk B.C. How to Use SPSS: A Step-By-Step Guide to Analysis and Interpretation

2016. — 276 pages. ISBN13: 978-1-936-52344-3 (pbk)How to use SPSS is an illustrated step-by-step guide to the Statistical Package for the Social Sciences data analysis software program. Throughout the book, Cronk uses screen shots to illustrate each step. This book is an excellent supplement to data analysis textbooks that include using the SPSS program, or this book may be used alone as a guide through the data analysis and write-up process. Examples of write-ups for both significant and non-significant results are presented for each type of data analysis procedure (e.g., t-test, ANOVA, MANOVA). The following is a chapter-by-chapter synopsis of Cronk's book. At the time of this review, the book in hand was Cronk's fourth edition. Perhaps this review will help you in your decision-making process of whether to buy the book.
A diagram on the inside of the front cover can be used as a visual guide to help select the best analysis procedure based on the research criteria (e.g., differences in groups, prediction, one dependent variable, parametric or non-parametric).
In Chapter 1, Cronk explains how to define variables and value labels, enter data, run an analysis, and view and print output files. Also presented is how to modify data by adding cases or variables.
In Chapter 2, Cronk expands on how to enter and modify data. Mentioned are the four measurement scales (nominal, ordinal, interval, and ratio) and how the data screen looks when data are missing. Cronk also demonstrates how to select cases. For example, if data were entered for four teachers (Teachers 1, 2, 3, & 4) and the researcher wanted to only analyze data for Teacher 2, then only Teacher 2's data could be selected. Also presented is how to compute a new variable. For example, if Teacher 1 gave students four quizzes, then these four quizzes (Q1, Q2, Q3, & Q4) could be combined to form a new composite variable. How to recode variables is also explained. For example, in surveys, some items could be stated positively (e.g., I like sports) and some items could be stated negatively (e.g., I dislike pizza). By recoding variables, the researcher can reverse the responses (e.g., 1 would change to 5, 2 would change to 4, 3 would change to 3, 4 would change to 2, and 5 would change to 1). By doing this, all variables could be analyzed on the same scale or similar items could be combined into composite variables.
In Chapter 3, Cronk explains how to perform descriptive statistics to calculate the mean, standard deviation, skew, and percentile rank. Also presented is how to run the Crosstabs command and read the output table. Comparing mean scores for multiple groups and for more than one independent variable is also presented. Cronk illustrates how to transform scores from different scales to z-scores for comparison, which is accomplished through the descriptive command.
In Chapter 4, Cronk explains how to graph data. The first part of the chapter begins with how to edit graphs. Cronk shows how bar charts, pie charts, and histograms are used to compare frequencies. Cronk also shows how scatterplots are used to examine the relationships between one or two independent variables and the dependent variable. Cronk demonstrates how the second independent variable is marked (plotted) in a different color from the first independent variable. For example, a scatter plot could be used to show the relationship between a person's height by weight and gender. Advanced bar charts are also presented.
In Chapter 5, Cronk explains how to calculate the Pearson Correlation Coefficient (Pearson r) for determining the strength of a linear relationship between two variables that are normally distributed. For variables that are not normally distributed, Cronk analyzes their relationship using the Spearman Correlation Coefficient (Spearman rho). For predicting one value based on one or more variables, Cronk uses simple linear regression and multiple linear regression, respectively. Example write-ups of these types of analyses are given for both significant and non-significant results. These write-ups can be used as models to help present results.
In Chapter 6, Cronk begins with a discussion of hypotheses, Type I and Type II errors, significant levels (p or alpha), one- and two-tailed tests, and degrees of freedom. Following this information is an explanation of how to run single-sample t-tests, independent-samples t-tests, paired-samples t-tests, one-way ANOVA, factorial ANOVA, repeated measures ANOVA, mixed-design ANOVA, ANCOVA, and MANOVA. All tests are presented with explanations, screen shots, and example write-ups of results.
In Chapter 7, Cronk discusses non-parametric inferential statistics. The chi-square goodness of fit is explained with details and screen shots to help show how to conduct the analysis. Also explained are the chi-square test of independence, Mann-Whitney U Test (explained is when to use this instead of the independent t-test), Wilcoxon Test (explained is when to use this instead of the paired-samples [dependent] t-test), Kruskal-Wallis H Test, and Friedman Test. Once again, screen shots are provided to guide the researcher and write-ups are given to help correctly present results.
In Chapter 8, Cronk focuses on test construction. Cronk shows how to use the bivariate correlation for item-total analysis to analyze the internal consistency of a data set, which is measure of reliability. An example of a correlation matrix is presented along with how to interpret the output. Cronk also explains how to use Cronbach's Alpha as a measure of internal consistency. Criteria for the data are given, screen shots show how to run the analysis, and a brief example of how to read the output is given. Test-retest reliability and criterion-related validity are also discussed.
The Appendixes include information on effect size (Cohen's d, r squared [coefficient of determination] and Eta squared), three practice data sets, and a glossary of approximately 60 terms.
The only way to fully comprehend the information contained in this book is to read it, study it, and follow the examples. The SPSS software program is capable of many powerful quantitative analyses and this step-by-step guide is like a key capable of unlocking the potential of the SPSS program. Directions on how to run various analyses and the write-ups are invaluable. This step-by-step guide continues to be a favored book to use during my research projects and is an excellent supplement to my research textbooks.AudienceOrganization
SPSS Versions
Availability of SPSS
Conventions
Screenshots
Practice Exercises
New to This Edition: Online SupplementsGetting Started
Starting SPSS
Entering Data
Defining Variables
Loading and Saving Data Files
Running Your First Analysis
Examining and Printing Output Files
Modifying Data Files
Entering and Modifying Data
Variables and Data Representation
Selection and Transformation of Data
Descriptive Statistics
Frequency Distributions and Percentile Ranks for a Single Variable
Frequency Distributions and Percentile Ranks for Multiple Variables
Measures of Central Tendency and Measures of Dispersion for a Single
Group
Measures of Central Tendency and Measures of Dispersion for Multiple
Groups
Standard Scores
Graphing Data
Graphing Basics
Bar Charts, Pie Charts, and Histograms
The SPSS Chart Builder
Scatterplots
Advanced Bar Charts
Editing SPSS Graphs
Prediction and Association
Pearson Correlation Coefficient
Spearman Correlation Coefficient
Simple Linear Regression
Multiple Linear Regression
Parametric Inferential Statistics
Review of Basic Hypothesis Testing
Single-Sample t Test
Independent-Samples t Test
Paired-Samples t Test
One-Way ANOVA
Factorial ANOVA
Repeated-Measures ANOVA
Mixed-Design ANOVA
Analysis of Covariance
Multivariate Analysis of Variance (MANOVA)
Nonparametric Inferential Statistics
Chi-Square Goodness of Fit
Chi-Square Test of Independence
Mann-Whitney U Test
Wilcoxon Test
Kruskal-Wallis H Test
Friedman Test
Test Construction
Item-Total Analysis
Cronbach’s Alpha
Test-Retest ReliabilityAppendix A
Appendix B
Appendix C
Appendix D
Appendix ECriterion-Related Validity
Effect SizePractice Exercise Data Sets
Practice Data Set 1
Practice Data Set 2
Practice Data Set 3Sample Data Files Used in Text
COINS.sav
GRADES.sav
HEIGHT.sav
QUESTIONS.sav
RACE.sav
SAMPLE.sav
SAT.sav
Selecting the Appropriate Inferential Test