SAGE Quantitative Research Methods

W Paul Vogt

• Publisher: SAGE Publications Ltd
• Year: 2011
• Print ISBN: 9781848606999
• eBook ISBN: 9781446275719
• Edition: 1
• Topic: Social Sciences
• Subtopic: Social Science Research & Methodology
• Index: Social Sciences

For more than 40 years, SAGE has been one of the leading international publishers of works on quantitative research methods in the social sciences. This new collection provides readers with a representative sample of the best articles in quantitative methods that have appeared in SAGE journals as chosen by W. Paul Vogt, editor of other successful major reference collections such as Selecting Research Methods (2008) and Data Collection (2010).

The volumes and articles are organized by theme rather than by discipline. Although there are some discipline-specific methods, most often quantitative research methods cut across disciplinary boundaries.

Volume One: Fundamental Issues in Quantitative Research

Volume Two: Measurement for Causal and Statistical Inference

Volume Three: Alternatives to Hypothesis Testing

Volume Four: Complex Designs for a Complex World

Table of contents

• Cover
• Contents
• Appendix of Sources
• Editor’s Introduction
• 1.1: General Orientations
• 1 - Ten Statisticians and Their Impacts for Psychologists
• 2 - 14 Conversations about Three Things
• 3 - Minimally Sufficient Research
• 4 - On Quantitizing
• 1.2: Experimental Methods
• 5 - The External Validity of Experiments
• 6 - Randomized Trials for the Real World: Making as Few and as Reasonable Assumptions as Possible
• 7 - Improving Causal Inference: Strengths and Limitations of Natural Experiments
• 8 - Having One’s Cake and Eating It, Too:Combining True Experiments with Regression Discontinuity Designs
• 1.3: Survey Research
• 9 - Capture–Recapture and Anchored Prevalence Estimation of Injecting Drug Users in England: National and Regional Estimates
• 10 - Constructing Summary Indices of Quality of Life: A Model for the Effect of Heterogeneous Importance Weights
• 11 - Advances in Age–Period–Cohort Analysis
• 12 - Selection Bias in Web Surveys and the Use of Propensity Scores
• 1.4: Methods for Missing Data
• 13 - Estimation of Causal Effects via Principal Stratification When Some Outcomes Are Truncated by “Death”
• 14 - Multiple Imputation for Missing Data: A Cautionary Tale
• 15 - Multiple Imputation: Current Perspectives
• 16 - Incomplete Hierarchical Data
• Volume II
• Contents
• 2.1: Measurement/Coding
• 17 - The Cost of Dichotomization
• 18 - Fidelity Criteria: Development, Measurement, and Validation
• 19 - Controlling Error in Multiple Comparisons, with Examples from State-to-State Differences in Educational Achievement
• 20 - Surrogate Endpoint Validation: Statistical Elegance versus Clinical Relevance
• 2.2: Causation
• 21 - Causation in the Social Sciences: Evidence, Inference, and Purpose
• 22 - Statistical Models for Causation: What Inferential Leverage Do They Provide?
• 23 - Identification of Causal Parameters in Randomized Studies with Mediating Variables
• 24 - Matching Estimators of Causal Effects: Prospects and Pitfalls in Theory and Practice
• 25 - Suppressor Variables in Path Models: Definitions and Interpretations
• 2.3: Program Evaluation and Individual Assessment
• 26 - Are Simple Gain Scores Obsolete?
• 27 - Ten Difference Score Myths
• 28 - What Are Value-Added Models Estimating and What Does This Imply for Statistical Practice?
• 29 - Setting Targets for Health Care Performance: Lessons from a Case Study of the English NHS
• 2.4: Statistical Inference
• 30 - The Insignificance of Null Hypothesis Significance Testing
• 31 - Correcting a Significance Test for Clustering
• 32 - A Comparison of Statistical Significance Tests for Selecting Equating Functions
• 33 - The Choice of Sample Size: A Mixed Bayesian/Frequentist Approach
• Volume III
• Contents
• 3.1 - Confidence Intervals and Effect Sizes
• 34 - Toward Policy-Relevant Benchmarks for Interpreting Effect Sizes: Combining Effects with Costs
• 35 - Replication and p Intervals: p Values Predict the Future Only Vaguely, but Confidence Intervals Do Much Better
• 36 - Confidence Intervals about Score Reliability Coefficients, Please: An EPM Guidelines Editorial
• 37 - Finite Sampling Properties of the Point Estimates and Confidence Intervals of the RMSEA
• 3.2: Meta-analysis
• 38 - Integrating Findings: The Meta-analysis of Research
• 39 - Reliability Generalization: Exploring Variance in Measurement Error Affecting Score Reliability across Studies
• 40 - The Relationship between Sample Sizes and Effect Sizes in Systematic Reviews in Education
• 41 - An Exploratory Test for an Excess of Significant Findings
• 42 - Expanded Information Retrieval Using Full-text Searching
• 3.3: Correlation and Regression
• 43 - Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition
• 44 - How Is a Statistical Link Established between a Human Outcome and a Genetic Variant?
• 45 - Using Quasi-variance to Communicate Sociological Results from Statistical Models
• 46 - Weighting Regressions by Propensity Scores
• 3.4: Logit and Probit Regression
• 47 - Comparing Logit and Probit Coefficients across Groups
• 48 - An Additional Measure of Overall Effect Size for Logistic Regression Models
• 49 - The Intermediate Endpoint Effect in Logistic and Probit Regression
• 50 - An Introduction to Crisp Set QCA,with a Comparison to Binary Logistic Regression
• 3.5: Categorical Data Analysis
• 51 - Univariate and Bivariate Loglinear Models for Discrete Test Score Distributions
• 52 - Testing for IIA in the Multinomial Logit Model
• 53 - Goodness-of-Fit Tests and Descriptive Measures in Fuzzy-set Analysis
• 54 - Is Optimal Matching Suboptimal?
• Volume IV
• Contents
• 4.1: Structural Equation Modeling
• 55 - The General Linear Model as Structural Equation Modeling
• 56 - Factor Retention Decisions in Exploratory Factor Analysis: A Tutorialon Parallel Analysis
• 57 - A Comparison of Item Response Theory and Confirmatory Factor Analytic Methodologies for Establishing Measurement Equivalence/Invariance
• 58 - The Importance of Structure Coefficients in Structural Equation Modeling Confirmatory Factor Analysis
• 4.2: Multilevel Modeling
• 59 - Multilevel Modeling: A Review of Methodological Issues and Applications
• 60 - Estimating Statistical Power and Required Sample Sizes for Organizational Research Using Multilevel Modeling
• 61 - From Micro to Meso: Critical Steps in Conceptualizing and Conducting Multilevel Research
• 62 - Growth Modeling Using Random Coefficient Models: Model Building,Testing, and Illustrations
• 4.3: Event History, Survival and Longitudinal Analyses
• 63 - Multi-state Models for Event History Analysis
• 64 - Discrete-Time Survival Mixture Analysis
• 65 - Multilevel Random Coefficient Analyses of Event- and Interval-Contingent Data in Social and Personality Psychology Research
• 66 - Business Cycles and Turning Points: A Survey of Statistical Techniques
• 4.4: Computer-Intensive and Hi-Tech Spatial Analysis Methods
• 67 - The Validity of Publication and Citation Counts for Sociology and Other Selected Disciplines
• 68 - A Web Crawler Design for Data Mining
• 69 - Analysis of Terrorist Social Networks with Fractal Views
• 70 - From Schelling to Spatially Explicit Modeling of Urban Ethnic and Economic Residential Dynamics