You are here

Quantitative Research in Psychology

Quantitative Research in Psychology

Five Volume Set
Edited by:

December 2014 | 1 912 pages | SAGE Publications Ltd

Quantitative psychology is a branch of psychology developed using certain methods and approaches which are designed to answer empirical questions, such as the development of measurement models and factor analysis. While quantitative psychology is often associated with the use of statistical models and psychological measurement research methods, this five volume set draws together the key conceptual and methodological techniques and addresses each research question at length. Each volume is accompanied by an introduction which contextualises the subject area, giving an understanding of established theories and how they are continuing to develop in one of the most fundamental and broadly researched psychological fields.

These volumes are an excellent resource for academics and scholars who will benefit from the framing provided by the editorial introduction and overview, and will also appeal to advanced students and professionals studying or using quantitative psychological methods in their research.

Volume One: Statistical hypothesis testing and power

Volume Two: Measurement

Volume Three: Research Design and sampling

Volume Four: Statistical Tests

Volume Five: Complex Models

What Is Statistical Significance?

Ralph Tyler
Bayesian Statistical Inference for Psychological Research

Ward Edwards, Harold Lindman and Leonard Savage
Statistical Difficulties of Detecting Interactions and Moderator Effects

Gary McClelland and Charles Judd
The Earth Is Round (p < 0.05)

Jacob Cohen
Power Analysis and Determination of Sample Size for Covariance Structure Modeling

Robert MacCallum, Michael Browne and Hazuki Sugawara
Computing Contrasts, Effect Sizes, and Counternulls on Other People's Published Data: General Procedures for Research Consumers

Ralph Rosnow and Robert Rosenthal
Statistical Significance Testing and Cumulative Knowledge in Psychology: Implications for the Training of Researchers

Frank Schmidt
The Appropriate Use of Null Hypothesis Significance Testing

Robert Frick
Controlling the Rate of Type I Error over a Large Set of Statistical Tests

H.J. Keselman, Robert Cribbie and Burt Holland
Hypothesis Testing and Theory Evaluation at the Boundaries: Surprising Insights from Bayes's Theorem

David Trafimow
Mindless Statistics

Gerd Gigerenzer
An Alternative to Null-Hypothesis Significance Tests

Peter Killeen
False-Positive Psychology Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant

Joseph Simmons, Leif Nelson and Uri Simonsohn
The Proof and Measurement of Association between Two Things

C. Spearman
A Method of Scaling Psychological and Educational Tests

Louis Thurstone
Multiple Factor Analysis

Louis Thurstone
Coefficient Alpha and the Internal Structure of Tests

Lee Cronbach
The Relation of Test Score to the Trait Underlying the Test

Frederic Lord
Construct Validity in Psychological Tests

L. Cronbach and P. Meehl
Convergent and Discriminant Validation by the Multitrait-Multimethod Matrix

Donald Campbell and Donald Fiske
The Axioms and Principal Results of Classical Test Theory

Melvin Novick
A General Approach to Confirmatory Maximum Likelihood Factor Analysis

K. Jöreskog
Intraclass Correlations: Uses in Assessing Rater Reliability

Patrick Shrout and Joseph Fleiss
Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM Algorithm

R. Darrell Bock and Murray Aitkin
A Taxonomy of Item Response Models

David Thissen and Lynne Steinberg
The New Rules of Measurement

Susan Embretson
The Concept of Validity

Denny Borsboom, Gideon Mellenbergh and Jaap van Heerden
On the Use, the Misuse, and the Very Limited Usefulness of Cronbach's Alpha

Klaas Sijtsma
A Two-Tier Full-Information Item Factor Analysis Model with Applications

Li Cai
Statistical Power of Abnormal-Social Psychological-Research – A Review

Jacob Cohen
Do Studies of Statistical Power Have an Effect on the Power of Studies?

Peter Sedlmeier and Gerd Gigerenzer
A Power Primer

Jacob Cohen
Optimal Design in Psychological Research

Gary McClelland
Statistical Analysis and Optimal Design for Cluster Randomized Trials

Stephen Raudenbush
Analysis of a Trial Randomised in Clusters

Sally Kerry and J. Martin Bland
The Design and Analysis of Longitudinal Studies of Development and Psychopathology in Context: Statistical Models and Methodological Recommendations

John Willett, Judith Singer and Nina Martin
Missing Data: Our View of the State of the Art

Joseph Schafer and John Graham
Propensity Score Estimation with Boosted Regression for Evaluating Causal Effects in Observational Studies

Daniel McCaffrey, Greg Ridgeway and Andrew Morral
The Persistence of Underpowered Studies in Psychological Research: Causes, Consequences, and Remedies

Scott Maxwell
The Scree Test for the Number of Factors

Raymond Cattell
Graphs in Statistical Analysis

F.J. Anscombe
Primary, Secondary, and Meta-Analysis of Research

Gene Glass
The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic and Statistical Considerations

Reuben Baron and David Kenny
Thirteen Ways to Look at the Correlation Coefficient

Joseph Lee Rodgers and Alan Nicewander
Why Covariance: A Rationale for Using Analysis of Covariance Procedures in Randomised Studies

Matthew Taylor and Mark Innocenti
Factor Analysis in the Development and Refinement of Clinical Assessment Instruments

Frank Floyd and Keith Widaman
Fixed-and Random-Effects Models in Meta-Analysis

Larry Hedges and Jack Vevea
How Many Discoveries Have Been Lost by Ignoring Modern Statistical Methods?

Rand Wilcox
A Comparison of Methods to Test Mediation and Other Intervening Variable Effects

David MacKinnon et al.
Probing Interactions in Fixed and Multilevel Regression: Inferential and Graphical Techniques

Daniel Bauer and Patrick Curran
Discrete Time Survival Mixture Analysis

Bengt Muthén and Katherine Masyn
A Better Lemon Squeezer? Maximum-Likelihood Regression with Beta-Distributed Dependent Variables

Michael Smithson and Jay Verkuilen
Average Causal Effects from Nonrandomized Studies

Joseph Schafer and Joseph Kang
Significance Tests and Goodness of Fit in the Analysis of Covariance Structures

P. Bentler and Douglas Bonett
The Dimensionality of Tests and Items

Roderick McDonald
Asymptotically Distribution-Free Methods for the Analysis of Covariance Structures

M. Browne
Model Selection and Akaike's Information Criterion (AIC): The General Theory and Its Analytical Extensions

Hamparsum Bozdogan
Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach

James Anderson and David Gerbing
Comparative Fit Indexes in Structural Models

P. Bentler
Model Selection in Covariance Structures Analysis and the "Problem" of Sample Size: A Clarification

Robert Cudeck and Susan Henly
Bootstrapping Goodness-of-Fit Measures in Structural Equation Models

Kenneth Bollen and Robert Stine
Modeling Incomplete Longitudinal and Cross-Sectional Data Using Latent Growth Structural Models

J. McArdle and Fumiaki Hamagami
Growth Curve Analysis in Accelerated Longitudinal Designs

Stephen Raudenbush and Wing-Shing Chan
Distinguishing between Moderator and Quadratic Effects in Multiple Regression

Robert MacCallum and Corinne Mar
The Robustness of Test Statistics to Nonnormality and Specification Error in Confirmatory Factor Analysis

Patrick Curran, Stephen West and John Finch
Cutoff Criteria for Fit Indices in Covariance Structure Analysis: Conventional Criteria versus New Alternatives

Li-tze Hu and Peter Bentler
On Sensitivity of Structural Equation Modeling to Latent Relation Misspecifications

Tenko Raykov
To Parcel or Not to Parcel: Exploring the Question, Weighing the Merits

Todd Little et al.
Distributional Assumptions of Growth Mixture Models: Implications for Overextraction of Latent Trajectory Classes

Daniel Bauer and Patrick Curran
Autoregressive Latent Trajectory (ALT) Models: A Synthesis of Two Traditions

Keneth Bollen and Patrick Curran
In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler's (1999) Findings

Herbert Marsh, Kit-Tai Hau and Zhonglin Wen
Sufficient Sample Sizes for Multilevel Modeling

Cora Maas and Joop Hox
An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models

Feinian Chen et al.

Jeremy Miles

Jeremy Miles, RAND Corporation, USA. More About Author

Brian Stucky