Using statistics in the social and health sciences with SPSS and Excel / Martin Lee Abbott.

Format:

Book

Author/Creator:

Abbott, Martin, 1949- author.

Series:

THEi Wiley ebooks.

THEi Wiley ebooks

Language:

English

Subjects (All):

Mathematical statistics--Data processing.

Mathematical statistics.

Multivariate analysis--Data processing.

Multivariate analysis.

Microsoft Excel (Computer file).

SPSS (Computer file).

Physical Description:

1 online resource (585 pages) : illustrations (some color)

Edition:

1st ed.

Place of Publication:

Hoboken, New Jersey : Wiley, 2017.

Language Note:

English

System Details:

Access using campus network via VPN at home (THEi Users Only).

Summary:

Provides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSS® and Excel® applications This book identifies connections between statistical applications and research design using cases, examples, and discussion of specific topics from the social and health sciences. Researched and class-tested to ensure an accessible presentation, the book combines clear, step-by-step explanations for both the novice and professional alike to understand the fundamental statistical practices for organizing, analyzing, and drawing conclusions from research data in their field. The book begins with an introduction to descriptive and inferential statistics and then acquaints readers with important features of statistical applications (SPSS and Excel) that support statistical analysis and decision making. Subsequent chapters treat the procedures commonly employed when working with data across various fields of social science research. Individual chapters are devoted to specific statistical procedures, each ending with lab application exercises that pose research questions, examine the questions through their application in SPSS and Excel, and conclude with a brief research report that outlines key findings drawn from the results. Real-world examples and data from social and health sciences research are used throughout the book, allowing readers to reinforce their comprehension of the material. Using Statistics in the Social and Health Sciences with SPSS® and Excel® includes: * Use of straightforward procedures and examples that help students focus on understanding of analysis and interpretation of findings * Inclusion of a data lab section in each chapter that provides relevant, clear examples * Introduction to advanced statistical procedures in chapter sections (e.g., regression diagnostics) and separate chapters (e.g., multiple linear regression) for greater relevance to real-world research needs Emphasizing applied statistical analyses, this book can serve as the primary text in undergraduate and graduate university courses within departments of sociology, psychology, urban studies, health sciences, and public health, as well as other related departments. It will also be useful to statistics practitioners through extended sections using SPSS® and Excel® for analyzing data.

Contents:

Cover

Title Page

Copyright

Dedication

Contents

Preface

Acknowledgments

Chapter 1 Introduction

Big Data Analysis

Visual Data Analysis

Importance of Statistics for the Social and Health Sciences and Medicine

Historical Notes: Early Use of Statistics

Approach of the Book

Cases from Current Research

Research Design

Focus on Interpretation

Coverage of Statistical Procedures

Chapter 2 Descriptive Statistics: Central Tendency

What is the Whole Truth? Research Applications (Spuriousness)

Descriptive and Inferential Statistics

The Nature of Data: Scales of Measurement

Nominal Data

Ordinal Data

Interval Data

Ratio Data

Choosing the Correct Statistical Procedure for the Nature of Research Data

Descriptive Statistics: Central Tendency

Mean

Median

Mode

Central Tendency and Levels of Data

Using SPSS® and Excel to Understand Central Tendency

SPSS®

Excel

Distributions

Describing the Normal Distribution: Numerical Methods

Central Tendency

Skewness

Kurtosis

Descriptive Statistics: Using Graphical Methods

Frequency Distributions

Column Charts" in Excel

Bar Charts" and "Histograms

Bar Charts and Histograms in SPSS®

Terms and Concepts

Data Lab and Examples (with Solutions)

Data Lab: Solutions

Chapter 3 Descriptive Statistics: Variability

Range

Percentile

Scores Based on Percentiles

Using SPSS® and Excel to Identify Percentiles

SPSS® Procedures

Excel Procedures

Standard Deviation and Variance

Calculating the Variance and Standard Deviation

The Deviation Method

The Average Deviation

The Computation Method

The Sum of Squares

Population SD and Inferential SD

Obtaining SD from Excel and SPSS®

Data Lab: Solutions.

Chapter 4 The Normal Distribution

The Nature of the Normal Curve

The Standard Normal Score: Z Score

The Z Score Table of Values

Navigating the Z Score Distribution

Calculating Percentiles

Creating Rules for Locating Z Scores

Calculating Z Scores

Working with Raw Score Distributions

Using SPSS® to Create Z Scores and Percentiles

Creating Z Scores

Creating Percentiles in SPSS®

Using Excel to Create Z Scores

STANDARDIZE Function

Using Excel and SPSS® for Distribution Descriptions

NORM.S.DIST Function

NORM.DIST Function

Chapter 5 Probability And the Z Distribution

The Nature of Probability

Elements of Probability

Empirical Probability

Combining Probabilities

Combining Probabilities: Addition Rule

Combining Probabilities: Multiplication Rule

Combinations and Permutations

Combination

Permutation

Conditional Probability: Using Bayes' Theorem

Z Score Distribution and Probability

Transforming a Raw Score to a Z Score: Statistical Testing

Transforming a Z Score to a Raw Score: Estimation

Transforming Cumulative Proportions to z Scores

Deriving Sample Scores from Cumulative Percentages

Using SPSS® and Excel to Transform Scores

Using the Attributes of the Normal Curve to Calculate Probability

Calculating "Areas" of the Standard Normal Distribution

Inclusion Area Example

Exclusion Area Example

Exact" Probability

Estimating "Exact" Probabilities

From Sample Values to Sample Distributions

Chapter 6 Research Design and Inferential Statistics

Theory

Hypothesis

Types of Research Designs

Experiment

Randomization

Control and Treatment Groups.

Variables

Quasi-Experimental Design

Non-Experimental or Post Facto Research Designs

The Nature of Research Design

Research Design Varieties

Sampling

Inferential Statistics

One Sample from Many Possible Samples

Central Limit Theorem and Sampling Distributions

The Sampling Distribution and Research

Populations and Samples

The Standard Error of the Mean

Transforming" the Sample Mean to the Sampling Distribution

Example

Findings

Discussion

Z Test

The Hypothesis Test

Statistical Significance

Practical Significance: Effect Size

Z Test Elements

Using SPSS® and Excel for the Z Test

Chapter 7 The T Test for Single Samples

Introduction

Z Versus T: Making Accommodations

Post Facto: Comparative Design

Parameter Estimation

Estimating the Population SD

New Symbol Sx: The Estimated SD of the Population

Biased versus Unbiased Estimates

New Symbol Sm: The Estimated SD of the Sampling Distribution of Means (or Simply, "Standard Error of the Mean")

The T Test

Degrees of Freedom

The T Test: A Research Example

Sample and Population Means (M- )

The Estimated Population SD (Sx) and Estimated Standard Error of the Mean (Sm)

Calculating the T Ratio Value

Interpreting the Results of the T Test for a Single Mean

The T Distribution

Using df with the T Distribution

The Hypothesis Test for the Single Sample T Test

Type I and Type II Errors

Type I (Alpha) Errors ( )

Type II (Beta) Errors ( )

Areas of Comparison Distributions

Effect Size

Effect Size for the Single Sample T Test

Another Measurement of the (Cohen's d) Effect Size

Power, Effect Size, and Beta

One- and Two-Tailed Tests

Two-Tailed Tests.

One-Tailed Tests

Choosing a One- or Two-Tailed Test

A Note about Power

Point and Interval Estimates

Calculating the Interval Estimate of the Population Mean

The Value of Confidence Intervals

Using SPSS® and Excel with the Single Sample T Test

SPSS® and the Single Sample T test

Excel and the Single Sample T test

Chapter 8 Independent Sample T Test

A Lot of "Ts

Experimental Designs and the Independent T Test

Dependent Sample Designs

Between and Within Research Designs

Using Different T Tests

Pre-test or No Pre-test

Example of Experiment

Post Facto Designs

Independent T Test: The Procedure

Creating the Sampling Distribution of Differences

The Nature of the Sampling Distribution of Differences

The Mean and Standard Deviation of the Sampling Distribution of Differences

Calculating the Estimated Standard Error of Difference with Equal Sample Size

The Degrees of Freedom for the Independent T Test

Using Unequal Sample Sizes

Derivation of the Formula for Unequal Sample Sizes

The Independent T Ratio

Independent T Test Example

The Setting

The Research Data

Hypothesis Test Elements for the Example

The Null Hypothesis

The Alternative Hypothesis

The Critical Value of Comparison

The Calculated T Ratio

Statistical Decision

Interpretation

Research Design of the Example

Before-After Convention with the Independent T Test

Confidence Intervals for the Independent T Test

Cohen's d Method

The Eta Squared Method

The Assumptions for the Independent T Test

Assumptions 1 and 2: Independence and Interval Level

Assumption 3: Normal Distribution of Sample Groups

Assumption 4: Equal Variance.

SPSS® Explore for Checking the Normal Distribution Assumption

Excel Procedures for Checking the Equal Variance Assumption

F Distribution

Use "Right" Side Critical Values

What Outcome Meets the Assumption for Equality of Variances?

SPSS® Procedure for Checking the Equal Variance Assumption

The Homogeneity of Variance Assumption for the Independent T Test

A Rule of Thumb

Using SPSS® and Excel with the Independent T Test

SPSS® Procedures for the Independent T Test

Excel Procedures for the Independent T Test

Effect Size for the Independent T Test Example

Parting Comments

Nonparametric Statistics: The Mann-Whitney U Test

Graphics in the Data Summary

Chapter 9 Analysis of Variance

A Hypothetical Example of ANOVA

The Nature of ANOVA

The Components of Variance

The Process of ANOVA

Calculating ANOVA

Calculating the Variance: Using the Sum of Squares (SS)

Calculating Components of Variance Using SS

Creating a Data Table

Using Mean Squares (MS)

Degrees of Freedom in ANOVA

Calculating Mean Squares (MS)

The F Ratio

The F Distribution

Post Hoc Analyses

Varieties" of Post Hoc Analyses

The Post Hoc Analysis Process

Tukey's HSD (Range) Test Calculation

Mean Comparison Table

Compare Mean Difference Values from HSD

Post Hoc Summary

Assumptions of ANOVA

Additional Considerations with ANOVA

The Hypothesis Test: Interpreting ANOVA Results

Are the Assumptions Met?

Is Population Normally Distributed?

Are the Variances Equal?

Are the Samples Independently Chosen?

Does the Dependent Variable Consist of Interval Data?

Manual Calculations

Post Hoc Analysis

Using SPSS® and Excel with One-Way ANOVA

SPSS® Procedures with One-Way ANOVA.

General Linear Model (GLM) Approach to Analyzing ANOVA.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed August 21, 2016).

ISBN:

9781119121060

111912106X

9781119121053

1119121051

OCLC:

954284941