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# Introductory Statistics Using SPSS

Second Edition

- Herschel Knapp - University of Southern California, USA

October 2016 | 312 pages | SAGE Publications, Inc

The updated

**Second Edition**of Herschel Knapp’s friendly and practical introduction to statistics shows students how to properly select, process, and interpret statistics without heavy emphasis on theory, formula derivations, or abstract mathematical concepts. Each chapter is structured to answer questions that students most want answered:*What statistical test should I use for this situation? How do I set up the data? How do I run the test? How do I interpret and document the results?*Online tutorial videos, examples, screenshots, and intuitive illustrations help students "get the story" from their data as they learn by doing, completing practice exercises at the end of each chapter using prepared downloadable data sets.**Available with Perusall—an eBook that makes it easier to prepare for class**

*Perusall*is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more.Preface

Acknowledgments

About the Author

PART I: STATISTICAL PRINCIPLES

1. Research Principles

Learning Objectives

Overview—Research Principles

Rationale for Statistics

Research Questions

Treatment and Control Groups

Rationale for Random Assignment

Hypothesis Formulation

Reading Statistical Outcomes

Accept or Reject Hypotheses

Variable Types and Levels of Measure

Continuous

Interval

Ratio

Categorical

Nominal

Ordinal

Good Common Sense

Key Concepts

Practice Exercises

2. Sampling

Learning Objectives

Overview—Sampling

Rationale for Sampling

Time

Cost

Feasibility

Extrapolation

Sampling Terminology

Population

Sample Frame

Sample

Representative Sample

Probability Sampling

Simple Random Sampling

Stratified Sampling

Proportionate and Disproportionate Sampling

Systematic Sampling

Area Sampling

Nonprobability Sampling

Convenience Sampling

Purposive Sampling

Quota Sampling

Snowball Sampling

Sampling Bias

Optimal Sample Size

Good Common Sense

Key Concepts

Practice Exercises

3. Working in SPSS

Learning Objectives

Video

Overview—SPSS

Two Views: Variable View and Data View

Variable View

Name

Type

Width

Decimals

Label

Values

Missing

Columns

Align

Measure

Role

Data View

Value Labels Icon

Codebook

Saving Data Files

Good Common Sense

Key Concepts

Practice Exercises

PART II: STATISTICAL PROCESSES

4. Descriptive Statistics

Learning Objectives

Videos

Overview—Descriptive Statistics

Descriptive Statistics

Number (n)

Mean (µ)

Median

Mode

Standard Deviation (SD)

Variance

Minimum

Maximum

Range

SPSS—Loading an SPSS Data File

Run SPSS

Data Set

Test Run

SPSS—Descriptive Statistics: Continuous Variables (age)

Statistics Tables

Histogram With Normal Curve

Skewed Distribution

SPSS—Descriptive Statistics: Categorical Variables (gender)

Statistics Tables

Bar Chart

SPSS—Descriptive Statistics: Continuous Variable (age) Select by Categorical Variable (gender)—Female or Male Only

SPSS—(Re)Selecting All Variables

Good Common Sense

Key Concepts

Practice Exercises

5. t Test and Mann-Whitney U Test

Learning Objectives

Videos

Overview—t Test

Example

Research Question

Groups

Procedure

Hypotheses

Data Set

Pretest Checklist

Pretest Checklist Criterion 1—Normality

Pretest Checklist Criterion 2—n Quota

Pretest Checklist Criterion 3—Homogeneity of Variance

Test Run

Results

Pretest Checklist Criterion 2—n Quota

Pretest Checklist Criterion 3—Homogeneity of Variance

p Value

Hypothesis Resolution

a Level

Documenting Results

Type I and Type II Errors

Type I Error

Type II Error

Overview—Mann-Whitney U Test

Test Run

Results

Good Common Sense

Key Concepts

Practice Exercises

6. ANOVA and Kruskal-Wallis Test

Learning Objectives

Videos

Layered Learning

Overview—ANOVA

Example

Research Question

Groups

Procedure

Hypotheses

Data Set

Pretest Checklist

Pretest Checklist Criterion 1—Normality

Pretest Checklist Criterion 2—n Quota

Pretest Checklist Criterion 3—Homogeneity of Variance

Test Run

Results

Pretest Checklist Criterion 2—n Quota

Pretest Checklist Criterion 3—Homogeneity of Variance

Comparison 1—Text : Text With Illustrations

Comparison 2—Text : Video

Comparison 3—Text With Illustrations : Video

Hypothesis Resolution

Documenting Results

Overview—Kruskal-Wallis Test

Test Run

Results

Good Common Sense

Key Concepts

Practice Exercises

7. Paired t Test and Wilcoxon Test

Learning Objectives

Videos

Overview—Paired t Test

Pretest/Posttest Design

Step 1: Pretest

Step 2: Treatment

Step 3: Posttest

Example

Research Question

Groups

Procedure

Step 1: Pretest

Step 2: Treatment

Step 3: Posttest

Hypotheses

Data Set

Pretest Checklist

Pretest Checklist Criterion 1—Normality of Difference

Test Run

Results

Hypothesis Resolution

Documenting Results

?% Formula

Overview—Wilcoxon Test

Test Run

Results

Good Common Sense

Key Concepts

Practice Exercises

8. Correlation and Regression—Pearson and Spearman

Learning Objectives

Videos

Overview—Pearson Correlation

Example 1—Pearson Regression

Research Question

Groups

Procedure

Hypotheses

Data Set

Pretest Checklist

Pretest Checklist Criterion 1—Normality

Test Run

Correlation

Regression (Scatterplot With Regression Line)

Results

Scatterplot Points

Scatterplot Regression Line

Pretest Checklist Criterion 2—Linearity

Pretest Checklist Criterion 3—Homoscedasticity

Correlation

Hypothesis Resolution

Documenting Results

Negative Correlation

No Correlation

Overview—Spearman Correlation

Example 2—Spearman Correlation

Research Question

Groups

Procedure

Hypotheses

Data Set

Pretest Checklist

Test Run

Results

Hypothesis Resolution

Documenting Results

Alternative Use for Spearman Correlation

Correlation Versus Causation

Overview—Other Types of Statistical Regression: Multiple Regression and Logistic Regression

Multiple Regression (R2)

Logistic Regression

Good Common Sense

Key Concepts

Practice Exercises

9. Chi-Square

Learning Objectives

Video

Overview—Chi-Square

Example

Research Question

Groups

Procedure

Hypotheses

Data Set

Pretest Checklist

Pretest Checklist Criterion 1—n = 5 per Cell

Test Run

Results

Pretest Checklist Criterion 1—n = 5 per Cell

Hypothesis Resolution

Documenting Results

Good Common Sense

Key Concepts

Practice Exercises

PART III: DATA HANDLING

10. Supplemental SPSS Operations

Learning Objectives

Data Sets

Overview—Supplemental SPSS Operations

Generating Random Numbers

Sort Cases

Data Set

Select Cases

Data Set

Recoding

Data Set

Importing Data

Importing Excel Data

Data Set

Importing ASCII Data (Generic Text File)

Data Set

SPSS Syntax

Data Set

Data Sets

Good Common Sense

Key Concepts

Practice Exercises

Glossary

Index

### Supplements

Student Study Site

The** Student Study Site** includes:

**Data sets**for use with exercises**Tutorial videos**demonstrating how to use SPSS to work with data**Solutions**to the odd-numbered exercises in the book

Instructor Teaching Site

Password-protected** Instructor Resources **include the following:

- Editable, chapter-specific
**Microsoft® PowerPoint® slides**that offer complete flexibility in easily creating a multimedia presentation for your course **Tutorial videos**demonstrating how to use SPSS to work with data**Solutions**to the all of the exercises in the book

Satisfy my expectations about a textbook to be used for my course. Students can learn many statistical methods without too many mathematics.

School of Nursing, Baylor Univ School Of Nursing

April 21, 2022

I highly recommend this book for everyone who wish to use SPSS. It is well written, easy to understand and to follow. The learning objectives at the beginning of the chapters give a good starting point.

Childhood and Youth study, Univeristy of Chichester

December 14, 2017

Finally a clear and concise book that presents with parametric and non-parametric equivalents together. I'm a fan!

Department of Psychology, Surrey University

June 21, 2017