Data Analysis for Social Workers / Edition 1

Data Analysis for Social Workers / Edition 1

by Denise Montcalm, David Royse
     
 

ISBN-10: 0205289037

ISBN-13: 9780205289035

Pub. Date: 04/27/2001

Publisher: Pearson

This book provides readers with an understanding of the important considerations directly related to analyzing various forms of data - both qualitative and quantitative. While practice problems are included so that readers can self-test their knowledge of major concepts, the book does not drown its readers in page after page of problem sets and

Overview

This book provides readers with an understanding of the important considerations directly related to analyzing various forms of data - both qualitative and quantitative. While practice problems are included so that readers can self-test their knowledge of major concepts, the book does not drown its readers in page after page of problem sets and tedious calculations. The numerous practice examples of concepts, and the engaging, conversational writing style will be appealing and effective for many types of learners. For anyone with an interest in introductory statistics as it relates to social work.

Product Details

ISBN-13:
9780205289035
Publisher:
Pearson
Publication date:
04/27/2001
Edition description:
New Edition
Pages:
352
Product dimensions:
7.00(w) x 9.10(h) x 0.80(d)

Table of Contents

Preface.

1. Introduction.

Developing the Right Mindset.

The Role of Data in Effective Day-to-Day Practice.

The Focus of This Book.

Introducing Different Types of Analyses.

Synchronizing Our Efforts.

2. Ethical Considerations.

Protecting Human Subjects.

Ethical Dilemmas.

Telling the Truth with Statistics.

3. The Nature of Data.

What are Data?

Levels of Measurement.

Selecting a Level of Measurement.

Coding Data.

Computer Application.

4. Constructing and Interpreting Frequency Tables.

Frequency Tables.

Cumulative Frequencies.

Grouped Frequencies.

Understanding the “Stuff” of Which Tables are Made.

Relative Frequencies.

Cautionary Notes Regarding Data Misrepresentation.

Computer Application.

Developing Thematic Categories for Qualitative Data.

An Introduction to Coding.

Interpreting Your Findings.

5. Preparing and Interpreting Graphical Displays.

Understanding the “Stuff” of which Graphs are Made.

Bar and Line Graphs.

Pie or Circle Graphs.

Histograms.

The Frequency Polygon.

Using Figures to Display Differences Across Groups.

Steam & Leaf Design.

Graphical Displays with Single System Designs.

Graphically Displaying Qualitative Date.

Recognizing Distorted Data.

Computer Application: Creating Graphical Displays.

6. Computing and Interpreting Measures of Central Tendency.

Identifying the Typical Response.

The Mode.

The Median.

The Mean.

Deciding Which Measure to Use.

Computer Application: Computing Central Tendency.

Describing the “Typical” Qualitative Response.

7. Computing and Interpreting Measures of Dispersion.

The Range (R).

Percentiles and Quartiles.

The Interquartile Range (IQR).

The Variance.

The Standard Deviation (sd or SD).

Computer Application: Computing Dispersion.

The Boxplot: Obtaining a Five-Number Summary.

Qualitatively Describing the Variability of Responses.

8. The Normal Distribution.

What Shape Are Your Data In?

Viewing Symmetry in the Context of Modality.

Defining Properties of Normal Distributions.

The Area Under a Normal Curve.

The Standard Normal or Z-score Distribution.

Computer Application: Measures of Distribution.

9. An Introduction to the World of Inferential Statistics.

Parameters versus Statistics.

Inference: Moving From the Few to the Many.

Probability Sampling: Our Gateway to Inference.

Probability: The Basics.

Predictable Characteristics of a Sampling Distribution.

Estimation Procedures.

Constructing an Interval Estimate (a Confidence Interval).

10. Hypothesis Testing.

Defining the Research and Statistical Hypothesis.

Directionality: Which Way Do the Data Flow?

The “Null” as a Research Hypothesis.

The Hypothesis Testing Procedure.

Separating Statistical from Substantive Significance.

Why Do We Fail to Reject, Rather Than Accept H0.

Type I and Type II Error.

11. Bivariate Analysis.

The Difference Between Univariate and Bivariate Analyses.

Starting a Bivariate Analysis.

Computer Application: Computing Chi Square.

Formula Alert: Calculating Chi Square Manually.

Measures of Association.

Chi Square with a Control Variable.

Computer Application: Computing a Three-Way Crosstabulation in SPSS.

12. Understanding and Interpreting Correlation.

The Use of Correlation by Social Workers.

Scattergrams.

Perfect Correlation.

A Note About Causality.

Outliers.

Interpreting the Strength (Magnitude) of a Correlation.

Statistical Significance.

Negative Correlation.

Formula Alert: Calculating Pearson r.

Conditions Needed for Correlation.

Rank-Order Correlation.

Formula Alert: Spearman's Rank Order Correlation Coefficient.

The Correlation Matrix.

More on the Regression Line.

Formula Alert: Calculating the Slope and Y-Intercept.

13. T-Tests and ANOVA: Testing Hypotheses About Means.

The t-Test (Independent Samples).

Formula Alert: Derivation of the t-Test.

Independent Samples t-Tests.

One-sample t-Tests.

One-Way Analysis of Variance (ANOVA).

Reporting t-Tests and ANOVAS in Manuscripts.

14. A Glimpse into Multivariate Analysis.

Multiple Correlation.

Outcomes Associated with Multiple Correlation.

Why Adjust R2?

Conditions Needed For Multiple Correlation.

Multiple Regression.

Another Least Squares Method.

Multiple R and Multiple R2 Revisited.

The b Coefficient and the Standard Beta.

Multiple Regression in Action.

Selecting Variables for Inclusion.

Conditions Needed for Multiple Regression.

Dummy Variables.

Two-Way (Two Factor) Analysis of Variance.

The Language of ANOVA.

The Logic of 2-Way ANOVA.

Two-Way ANOVA in Action.

The Output of 2-Way ANOVA.

Conditions Needed for 2-Way ANOVA.

15. Selecting the Appropriate Statistical Test.

Constructing Convincing and Credible Research.

Considerations in Selecting a Statistical Test.

A Statistical Guide.

Nonparametric Data.

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