This all-in-one text assists human service practitioners, and the students of human service educational programs, in the evaluation of their practice with their clients. It takes readers through the entire research process, step by step, starting with the literature review on the nature of the behavior being served, to the development of their study methods, to the statistical analysis of data using the internet and, finally, to the drawing of conclusions based on the outcome study that was conducted. When readers complete this book, they will be prepared to conduct an outcome evaluation study and to present a report to their agencies or instructors.
Key distinctions of this text include:
- guides for analysis of data using Excel, the internet or SPSS for statistical analysis of data;
- the separation of content into basic concepts and intermediate concepts for use in beginning and intermediate courses in human service research methods;
- an instructor's manual that offers outlines, lists, and test questions additional to those in the text;
- a student workbook with practice assignments for use in courses as well as a set of checklists that serve as a guide for various tasks in the research process; and
- objectives, summaries, and tests in all chapters.
Evaluating Human Service Outcomes could be used as the basic text for a beginning course in human service research in educational programs in social work, counseling, and psychology where a major goal is to complete a research study. It could also be used as a supplemental text for advanced research courses that include the analysis of data. The text also should be of interest to human service practitioners who are working in programs funded by grants that require outcome evaluation.
Author(s): Reginald O. York
Publisher: Springer
Year: 2022
Language: English
Pages: 329
City: Cham
Preface
Contents
About the Author
Chapter 1: The Essence of Outcome Evaluation
Introduction
Objectives
Types of Human Service Evaluation
Outcome Evaluation
Other Types of Human Service Evaluation
The Evaluation of Human Need
The Evaluation of Service Quantity
The Evaluation of Service Quality
The Evaluation of Service Efficiency
The Four Main Purposes of Human Service Research
The Evaluation of Services
The Description of People
The Explanation of Things
The Exploration of the Unknown
The Process of Outcome Evaluation
Step 1: Determine the Research Question and Study Purpose
Step 2: Develop a Knowledge Base for the Study
Step 3: Design the Evaluative Study
Step 4: Collect and Analyze Data
Step 5: Draw Conclusions
Step 6: Describe the Service that Was Evaluated
Don’t Put the Cart Before the Horse!
Evidence-Based Practice as a Guide
This Book
The Organization of This Book
Summary
Chapter 2: Developing Your Knowledge Base
Introduction
Objectives
Steps in the Process of Developing Your Knowledge Base
Step 1: Presenting the Scope of Your Review
Step 2: Finding Your Sources
Step 3: Reviewing Your Sources
Levels of Evidence
Step 4: Writing Your Literature Review
Summary
References
Chapter 3: Developing the Methods for Your Outcome Study
Introduction
Objectives
Selecting Your Study Sample and Generalizing Your Study Findings
Types of Samples
Sampling Error
Two Ways to Generalize Your Study Results
Measuring Your Study Variables
Defining Your Study Variables
Qualitative and Quantitative Forms of Measurement
Reliability and Validity in the Measurement of Psychosocial Variables
Finding a Published Scale
Designing Your Own Measurement Scale
Defining the Variables You Are Measuring
Constructing the Items for the Measurement Device
Determining Your Research Design
Causes of the Clients’ Measured Growth
Group Research Designs
One Group Pretest-Posttest Design
Comparison Group Design
Single-Subject Research Designs
Single-Subject Research Designs that Fail to Control for Maturation
The Limited AB Single-Subject Design
The B Single-Subject Design
Single-Subject Research Designs that Control for Maturation
The AB Single-Subject Research Design
Composing Your Study Hypothesis
Summary
References
Chapter 4: Collecting and Analyzing Your Data
Introduction
Objectives
Collecting Data
Collecting Data from Human Subjects in an Ethical Manner
Recording Your Data
Developing Your Data Plan
Selecting the Statistic for Your Outcome Study
Preliminary Steps for Testing Your Study Hypothesis
The Six Data Situations for Outcome Research
Data Situations that Do Not Fit
Selecting a Statistic for Describing Clients
Common Descriptive Statistics for Data Recorded Numerically
Common Descriptive Statistics for Categorical Data
Selecting a Statistic for Explaining Client Gain
Analyzing Your Data
Reporting Your Results
Summary
Chapter 5: Using the Internet to Analyze Your Outcome Data
Introduction
Objectives
Preliminary Steps
Organizing Your Data
The Six Data Situations for Outcome Evaluative Research
Comparing Matched Pretest and Posttest Scores
Example
Steps in the Process of Comparing Matched Scores
Comparing a Set of Scores to a Single Score
Example
Steps in the Process of Comparing a Set of Scores to a Single Score
Comparing the Gain Scores of Two Groups
Example
Steps in the Process of Comparing the Scores of Two Groups
Comparing Two Groups on the Basis of a Dichotomous Variable
Example
Comparing Multiple Treatment Scores to a Single Baseline Score for One Client
Example
Steps in the Comparison of Multiple Scores to a Single Score
Comparing Multiple Treatment Scores to Multiple Baseline Scores for a Single Client
Example
Steps in the Comparison of Multiple Treatment Scores to Multiple Baseline Scores
Summary
Practice Assignment
Research Case Examples
Chapter 6: Using SPSS to Analyze Your Outcome Data
Introduction
Objectives
Preliminary Steps
Organizing Your Data
The Six Data Situations for Evaluative Studies
Comparing Matched Pretest and Posttest Scores
Example
Steps in the Comparison of Matched Scores
Step 1: Establishing Your Data File
Step 2: Entering Your Data
Step 3: Analyzing Your Data
Step 4: Reporting Your Results
Comparing a Set of Scores to a Single Score for a Group of Clients
Example
Steps in the Comparison of a Set of Scores to a Single Score
Step 1: Establishing Your Data File
Step 2: Entering Your Data
Step 3: Analyzing Your Data
Step 4: Reporting Your Results
Comparing the Gain Scores of Two Groups
Example
Steps in the Comparison of the Scores of Two Groups
Step 1: Establishing Your Data File
Step 2: Entering Your Data
Step 3: Analyzing Your Data
Step 4: Reporting Your Results
Comparing Two Groups on the Basis of a Dichotomous Variable
Example
Steps in the Comparison of Two Groups Using a Dichotomous Variable
Step 1: Establishing Your Data File
Step 2: Entering Your Data
Step 3: Analyzing Your Data
Step 4: Writing Your Results
Comparing Multiple Treatment Scores to a Single Baseline Score for One Client
Demonstration Example
Steps in the Comparison of a Set of Scores to a Single Score for One Client
Step 1: Establishing Your Data File
Step 2: Entering Your Data
Step 3: Analyzing Your Data
Step 4: Reporting Your Results
Comparing Multiple Treatment Scores to Multiple Baseline Scores for a Single Client
Steps in the Comparison of Multiple Treatment Scores to Multiple Baseline Scores for a Single Client
Step 1: Establishing Your Data File
Step 3: Analyzing Your Data
Step 4: Reporting Your Results
Summary
Practice Assignment on the Testing of the Evaluative Hypothesis
Chapter 7: Using Special Excel Files to Analyze Your Outcome Data
Introduction
Objectives
Preliminary Steps
Organizing Your Data
The Six Data Situation for Evaluative Studies
Comparing Matched Pretest and Posttest Scores
Example
Steps in Comparing Matched Scores
Step 1: Organizing Your Data
Step 2: Entering Your Data
Step 3: Analyzing Your Data
Step 4: Reporting Your Results
Comparing a Set of Scores to a Single Score for a Group of Clients
Example
Steps in Comparing a Set of Scores to a Single Score
Step 1: Composing Your Data
Step 2: Entering Your Data
Step 3: Analyzing Your Data
Step 4: Reporting Your Results
Comparing the Gain Scores of Two Groups
Demonstration Example
Steps in Comparing a Set of Scores for Two Groups
Step 1: Composing Your Data
Step 2: Entering Your Data
Step 3: Analyzing the Data
Step 4: Reporting Your Results
Comparing Two Groups on the Basis of a Dichotomous Variable
Demonstration Example
Steps in Comparing Two Groups on the Basis of a Dichotomous Variable
Step 1: Recording Your Data
Step 2: Analyzing the Results
Step 3: Reporting Your Results
Comparing Multiple Treatment Scores to a Single Baseline Score for One Client
Demonstration Example
Step 1: Entering the Data into the Excel File
Step 2: Analyzing the Data
Step 3: Reporting the Results
Comparing Multiple Treatment Scores to Multiple Baseline Scores for a Single Client
Example
Steps in the Comparison of Multiple Treatment Scores to Multiple Baseline Scores
Analyzing the Data
Step 3: Reporting Your Results
Summary
Practice Assignment on the Testing of the Evaluative Hypothesis
Chapter 8: Describing Clients
Introduction
Objectives
The Process of Descriptive Research
Step 1: Determine the Purpose of the Study
Step 2: Select the Study Sample
Step 3: Decide What to Describe
Step 4: Collect Your Data
Step 5: Select the Statistic for Each Variable
Analyzing Descriptive Data
Using the Internet to Compute Descriptive Statistics
Step 1: Enter the Calculator Soup Webpage
Step 2: Enter Your Data into the Blank Box on the Screen
Step 3: Calculate the Descriptive Statistics
Reporting the Results of Your Descriptive Study
Summary
References
Chapter 9: Explaining Client Outcomes
Introduction
Objectives
The Issue of Causation
The Steps in the Explanatory Research Process
Step 1: Determine the Purpose, Research Question, and Knowledge Base
Step 2: Develop Your Explanatory Research Hypothesis
Step 3: Collect and Record Your Data
Step 4: Analyze Your Data
Step 5: Report Your Results
Analyzing Your Explanatory Data
Data Situations Previously Addressed in This Book
Additional Data Situations for Explanatory Research
Data Situation A: Using the Correlation Coefficient to Examine the Relationship Between Two Interval Variables
Example
Option 1: Using the Internet as the Mechanism for Computing the Correlation Coefficient
Option 2: Using SPSS to Compute the Correlation Coefficient
Reporting the Results and Conclusions for the Correlation Example
Data Situation B: Using ANOVA When You Are Comparing the Scores of Several Groups
Example
Option 1: Using the Internet to Test Your Hypothesis Using ANOVA
Option 2: Using SPSS to Test Your Hypothesis Using ANOVA
Reporting the Results of the Study Using ANOVA
Summary
Reference
Chapter 10: Getting Ideas on How to Improve Service Through Qualitative Surveys
Introduction
Objectives
Quantitative and Qualitative Measurement
The Essence of Qualitative Research
Exploratory Research and Qualitative Measurement
The Nature of Qualitative Data
Approaches to Qualitative Research
The Social Survey
Steps in the Process of Conducting a Social Survey
Step 1: Determine the Purpose of the Survey
Step 2: Select Your Study Sample
Step 3: Design Your Measurement Tool
Step 4: Administer the Survey
Step 5: Analyze Data
Step 6: Draw Conclusions
One Model for Content Analysis of Qualitative Data
Step 1: First-Level Coding
Step 2: Credibility Assessment of First-Level Codes
Step 3: Second-Level Coding
Step 4: Enumeration of Second-Level Codes
Other Steps
Drawing Conclusions
An Exercise in the Content Analysis of Qualitative Data
Description of the Study
The 2019 Cohort
The 2022 Cohort
Tasks in the Content Analysis of These Data
Summary
Chapter 11: Writing Your Research Report
Introduction
Objectives
Reporting the Purpose of Your Study and the Knowledge Base
Reporting Your Study Purpose and Research Question
Reporting Your Knowledge Base
Reporting Your Study Methods
Reporting Your Study Sample
Describing Your Measurement Tools
Stating Your Study Hypothesis
Describing Your Research Design
Writing Your Results and Conclusions
Describing the Service Being Evaluated
Describing the Objectives of the Service
Describing the Structure of the Service
Describing the Personnel of the Service
Describing the Model of the Service
Summary of Your Description of the Service
Summary
References
Chapter 12: Facing the Challenges for Outcome Evaluation
Introduction
How Can We Justify the Resources Expended for Our Services?
Is Our Knowledge Base a Sufficient Guide?
Can We Generalize Our Findings on a Logical Basis?
Does Our Measurement Tool Pass the Test of Face Validity?
Do We Know that Our Service Was Delivered According to Promise?
Does Our Research Design Have to Control for Normal Growth over Time?
Why Should We Be Concerned with Statistical Significance?
How Do We Know if We Have Practical Significance?
Are Our Conclusions Consistent with Our Data?
Did We Put the Cart Before the Horse?
Summary
References
Appendix: Inventory of Critical Research Concepts (N = 111)
AB Single-Subject Research Design
Alternative Treatment Design
Analysis of Variance (ANOVA)
B Single-Subject Research Design
Bar Chart
Baseline Period
Causation
Chance
Cherry Picking
Chi Square
Code in Content Analysis of Qualitative Data
Comparison Group
Comparison Group Research Design
Content Analysis in Qualitative Research
Content Validity
Correlation
Correlation Coefficient
Criterion Validity
Data Forms
Data Plan
Descriptive Study
Descriptive Statistics
Dichotomous Data
Directional Hypothesis
Effect Size
Empirical Relationship
Evaluative Study
Evidence-Based Practice
Excel Files for Data Analysis
Explanatory Study
Experimental Group Research Design
Face Validity
False Positive and False Negative
Fisher Exact Test
Frequency
Generalization of Study Results
GraphPad
Group Research Designs
One-Group Pretest-Posttest Research Design
One-Sample t Test
Open-Ended Questions on a Survey
Outcome Objective
History as a Threat to Internal Validity
Human Service Outcome
Hypothesis
Hypothesis Testing
Independent t Test
Inferential Statistics
Institutional Review Board
Internal Consistency Reliability
Interval Level of Measurement
Limited AB Single-Subject Research Design
Logical Generalization
Logical Justification of a Service
Matched Scores
Maturation as a Threat to Internal Validity
Mean
Measurement Error
Median
Meta-Analysis of Evidence
Measurement Error
Median
Mode
Model of a Service
Narrative Analysis of Qualitative Data
Nominal Level of Measurement
Null Hypothesis
One-Group Pretest-Posttest Research Design
One-Sample t Test
One-Tailed Test
Ordinal Level of Measurement
Paired t Test
Pie Chart
Posttest-Only Control Group Design
Proportion
Pretest Scores and Posttest Scores
Qualitative Measurement
Quantitative Measurement
Random Sample
Range
Ratio Level of Measurement
Reliability
Research Designs
Research Types
Sample
Sampling Error
Sample Types
Scientific Generalization
Scientific Justification for the Delivery of a Given Service
Single-Subject Research Designs
Social Desirability Bias
Spearman Rank Correlation Coefficient
SPSS
Standard Deviation
Statistics
Statistical Significance
Structure of the Service
Study Population
Systematic Review of Evidence
Systemic Literature Review
Systematic Random Sampling Procedure
Test-Retest Reliability
Threats to Internal Validity
Treatment Group
Treatment Period
Two-Tailed Test
Validity
Variable
Variance
Index
