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The 3^{rd} edition of ** A Student Guide to SPSS** provides easy to follow step-by-step instructions on how to compute introductory and advanced statistics using one of the most popular statistical software programs in psychology, business, education, and other social sciences. Written in a non-intimidating, easy to read language, this guide is suitable for individuals with little to no experience using statistical software. As such, it would be of practical use to anyone who needs a simple and straightforward introduction to the most commonly used features of SPSS.

This guide to SPSS was originally developed to complement the lecture component of introductory undergraduate courses in statistics. The 2^{nd} and 3^{rd} editions were expanded to increase the guide’s suitability for more advanced undergraduate statistics courses. While most statistics textbooks teach students how to hand calculate statistics, this guide gives students the opportunity to learn how to analyze large datasets not conducive to hand calculations, providing them with the practical skills necessary for graduate school and/or a career in research.

**Features**

- Learning objectives at the beginning of each chapter help students keep on track and instructors apprised of the functions that students have learned so they can create SPSS assignments for students.
- Concrete examples with screenshots of SPSS are used throughout to make it easier for students to learn how to compute and interpret statistics.
- Examples of reporting statistics in the style of the American Psychological Association (APA)- using the 7
^{th}edition of their manual- are included.

**New to the Third Edition **

- Throughout the guide, elaborations on the meaning and interpretation of various statistics and demonstrations of more advanced statistical analyses have been added.
- The chapter on multiple regression has been expanded to include a new example that describes how to include a nominal predictor variable with more than two categories in a multiple regression analysis as well as how to interpret the results.
- A brief discussion of the tolerance statistic has been added to the advanced regression chapter.
- The chapter on one-way ANOVA has been expanded to include one-way within-groups ANOVA (in addition to one-way between-groups ANOVA).

**Content**

- The 3
^{rd}edition ofcontains 9 chapters on getting started with SPSS, descriptive statistics, correlation, simple regression, multiple regression, advanced regression (hierarchal regression, stepwise regression), the sign test, t-tests (single sample, paired samples, independent samples), and one-way ANOVA (one-way between, one-way within).*A Student Guide to SPSS*

**1 AN INTRODUCTION TO SPSS **

Getting Started

Opening SPSS

Data and Variable View Windows

Data View

Variable View

Creating Variables

Entering Data

Saving the SPSS Dataset (File→Save As)

Handy Tools and Tricks

Viewing Value Labels

Sorting Data (Data→Sort Cases)

Selecting Cases (Data→Select Cases)

Splitting Data into Groups (Data→Split File)

Recoding Variables (Transform→Recode into Different Variables)

Computing New Variables (Transform→Compute Variable)

Reporting Decimal Remainders and Rounding

Reporting Decimal Remainders

Rounding

**2 DESCRIPTIVE STATISTICS **

Central Tendency and Variability

Computing Indicators of Central Tendency and Variability (Analyze→Descriptive

Statistics→Frequencies)

Interpreting the Results

Descriptive Statistics

Sample Size (N)

Variability

Central Tendency

Reporting the Results

Skewness and Kurtosis

Skewness

Kurtosis

Computing Skewness and Kurtosis Statistics (Analyze→Descriptive

Statistics→Frequencies)

Interpreting the Results

Reporting the Results

z Scores

Transforming Raw Scores to z Scores (Analyze→Descriptive Statistics→Descriptives)

Interpreting the Results

** 3 CORRELATION **

Sample Data Files

Opening Sample Data Files

Scatterplots

Generating Scatterplots (Graphs→Legacy Dialogs→Scatter/Dot)

Interpreting Scatterplots

Pearson Correlation Coefficients (r)

Computing Pearson Correlation Coefficients (Analyze→Correlate→Bivariate)

Interpreting the Results

Determining Statistical Significance

Reporting the Results

Spearman Rank Order Correlation Coefficients (rs)

Computing Spearman Rank Order Correlation Coefficients (Analyze→Correlate→

Bivariate)

Interpreting the Results

Reporting the Results

Point Biserial Correlation Coefficients (rpb)

Computing Point Biserial Correlation Coefficients (Analyze→Correlate→Bivariate)

Interpreting the Results

Reporting the Results

Phi Coefficients (ϕ)

Computing Phi Coefficients (Analyze→Caorrelate→Bivariate)

Interpreting the Results

Reporting the Results

Directional Hypotheses and One-Tailed Correlation Analyses

Conducting One-Tailed Correlation Analyses (Analyze→Correlate→Bivariate)

Interpreting the Results

Reporting the Results

Predicting the Wrong Direction

Partial Correlation (pr)

Interpreting Partial Correlations

Computing Partial Correlations (Analyze→Correlate→Partial)

Interpreting the Results

Reporting the Results

**4 SIMPLE REGRESSION **

Introduction to Regression

Conducting a Simple Regression Analysis (Analyze→Regression→Linear)

Interpreting the Results

Assessing the Model’s Accuracy

The Correlation Coefficient (r)

The Coefficient of Determination (r2)

The Standard Error of Estimate (SEE)

Assessing Statistical Significance

The Regression Model

The Predictor

Constructing the Equation for the Least-Squares Regression Line

Using the Regression Equation to Make Predictions

Reporting the Results

**5 MULTIPLE REGRESSION **

Introduction to Multiple Regression

Multiple Regression with Two Predictor Variables

Conducting a Multiple Regression Analysis (Analyze→Regression→Linear)

Interpreting the Results

Assessing the Model’s Accuracy

The Multiple Correlation (R)

The Multiple Coefficient of Determination (R2)

The Standard Error of Estimate (SEE)

Assessing Statistical Significance

The Regression Model

The Predictors

Constructing the Equation for the Least-Squares Regression Line

Using the Regression Equation to Make Predictions

Bivariate, Partial, Semipartial, and Squared Semipartial Correlations

Bivariate Correlations (r)

Partial Correlations (pr)

Semipartial Correlations (sr)

Squared Semipartial Correlations (sr2)

Reporting the Results

Multiple Regression with a Nominal Predictor with Two Categories

Conducting a Multiple Regression Analysis (Analyze→Regression→Linear)

Interpreting the Results

Assessing the Model’s Accuracy

The Multiple Correlation (R)

The Multiple Coefficient of Determination (R2)

The Standard Error of Estimate (SEE)

Assessing Statistical Significance

The Regression Model

The Predictors

Constructing the Equation for the Least-Squares Regression Line

Using the Regression Equation to Make Predictions

Bivariate, Partial, Semipartial, and Squared Semipartial Correlations

Bivariate Correlations (r)

Partial Correlations (pr)

Semipartial Correlations (sr)

Squared Semipartial Correlations (sr2)

Reporting the Results

Multiple Regression with a Nominal Predictor with More than Two Categories

Creating New Dichotomous Variables

Conducting a Multiple Regression Analysis (Analyze→Regression→Linear)

Interpreting the Results

Assessing the Model’s Accuracy

The Multiple Correlation (R)

The Multiple Coefficient of Determination (R2)

The Standard Error of Estimate (SEE)

Assessing Statistical Significance

The Regression Model

The Predictors

Constructing the Equation for the Least-Squares Regression Line

Using the Regression Equation to Make Predictions

Reporting the Results

**6 ADVANCED REGRESSION **

Hierarchical Regression

Conducting a Hierarchical Regression Analysis (Analyze→Regression→Linear)

Interpreting the Results

Model Comparisons

Model Statistics

Predictor Statistics

Excluded Variables

Reporting the Results

Conducting a More Complex Hierarchical Regression Analysis

(Analyze→Regression→Linear)

Interpreting the Results

Model Comparisons

Predictor Statistics

Model Statistics

Excluded Variables

Reporting the Results

Stepwise Regression

Conducting a Stepwise Regression Analysis (Analyze→Regression→Linear)

Interpreting the Results

Finding the Best Model

Model Statistics

Predictor Statistics

Excluded Variables

Constructing the Equation for the Least-Squares Regression Line

Using the Regression Equation to Make Predictions

Reporting the Results

**7 SIGN TEST **

Introduction to the Sign Test

Two-Tailed Sign Test

Conducting a Sign Test (Analyze→Nonparametric Tests→Legacy Dialogs→2 Related

Samples)

Interpreting the Results

Reporting the Results

One-Tailed Sign Test

Conducting a Sign Test (Analyze→Nonparametric Tests→Legacy Dialogs→

2 Related Samples)

Interpreting the Results

Reporting the Results

Predicting the Wrong Direction

**8 t-TESTS **

Single Sample t-Test

Conducting a Single Sample t-Test (Analyze→Compare Means→

One-Sample t Test)

Interpreting the Results

Descriptive Statistics

Assessing Statistical Significance

Calculating Confidence Intervals for the Population Mean

Effect Sizes (Cohen’s d and Hedges’ g)

Reporting the Results

One-Tailed Single Sample t-Test

Reporting the Results

Predicting the Wrong Direction

Paired Samples t-Test

Conducting a Paired Samples t-Test (Analyze→Compare Means→PairedSamples T Test)

Interpreting the Results

Descriptive Statistics

Assessing Statistical Significance

Effect Sizes (Cohen’s d and Hedges’ g)

Reporting the Results

Calculating the 99% Confidence Intervals for the Difference in Means

Reporting the Results

One-Tailed Paired Samples t-Test

Reporting the Results

Predicting the Wrong Direction

Independent Samples t-Test

Conducting an Independent Samples t-Test (Analyze→Compare Means→IndependentSamples T Test)

Interpreting the Results

Descriptive Statistics

Levene’s Test of Homogeneity of Variance

Assessing Statistical Significance

Effect Sizes (Cohen’s d, Hedges’ g, and Glass’s Δ)

Reporting the Results

Calculating the 99% Confidence Intervals for the Difference in Means

Reporting the Results

One-Tailed Independent Samples t-Test

Reporting the Results

Predicting the Wrong Direction

**9 ONE-WAY ANOVA **

Introduction to One-Way ANOVA

One-Way Between-Groups ANOVA

Conducting a One-Way Between-Groups ANOVA (Analyze→General Linear

Model→Univariate)

Interpreting the Results

Descriptive Statistics

Levene’s Test of Homogeneity of Variance

Main Effect

Effect Size

Post Hoc Tests

Reporting the Results

One-Way Within-Groups ANOVA

Conducting a One-Way Within-Groups ANOVA (Analyze→General Linear

Model→Repeated Measures)

Interpreting the Results

Descriptive Statistics

Mauchly’s Test of Sphericity

Main Effect

Effect Size

Post Hoc Tests

Reporting the Results

**Carrie Cuttler**

“Written in a clear and accessible style, Dr. Cuttler’s *Student Guide to SPSS* provides an excellent introduction to one of the most popular statistical software packages used by psychologists. It is an outstanding resource for any undergraduate course in behavioral statistics.”

—D. Geoffrey Hall, PhD - University of British Columbia

“*A Student Guide to SPSS* lays out step-by-step the most common kinds of simple analyses for introductory students. With the help of the guide, students are able to complete analyses in SPSS on their own, outside the classroom or computer lab. An invaluable resource for anyone wanting to incorporate an SPSS lab component to an introductory statistics course.”

—Dana S. Thordarson, PhD - University of British Columbia

#### Related ISBN's: 9781792411854, 9781792408632

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** ISBN ** 9781792411854

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