General Research and Survey Methods references
Compilations and Methods references
General and Qualitative references
Center for Epidemiologic Studies Depression (CES-D) Scale references
Race/Ethnicity references
Racial and Ethnic Identity references
IRT and DIF Readings
Reference List Sub-Sections:
General Research Methods
General Survey Methods
Introduction
The following list of references was compiled by members of the Measurement and Methods Core leaders and participants. It is intended to serve as a list of basic, classic, and high quality references for those interested in learning more about quantitative and qualitative measurement issues in general and in diverse and older populations. It also includes references on minority health issues. The list is not exhaustive, but reflects references that RCMAR investigators’ find particularly useful. It also includes some general research and survey methods references to set the stage for new investigators. The development of the list is an ongoing process, thus it will be updated as RCMAR investigators identify new references in these areas, and new areas of relevance.
General Research Methods References
Campbell, DT and Stanley, JC. Experimental and Quasi-Experimental Designs for Research. Chicago: Rand McNally College Publishing Company, 1963.
Describes research designs at three levels: pre-experimental, quasi-experimental, and experimental and discusses the barriers to unambiguous conclusions (threats to validity) associated with each design. Quasi-experimental designs in particular may pose validity threats that are acceptable when a randomized experimental design is not feasible.
Hulley, SB and Cummings, SR. Designing Clinical Research: An Epidemiologic Approach. Williams & Wilkins, Baltimore, 1988.
This book is designed for applied clinical research. It outlines the 6 steps in the research process including choosing the question, developing the protocol, pretesting and revising the protocol, carrying out the study, analyzing the findings, and drawing and disseminating conclusions. Topics included under these steps include choosing the sample, planning the measurements, various forms of study design, estimating sample size, quality control, and writing a research proposal.
Bailey, KD. Methods of Social Research. New York: The Free Press, 1987.
Describes the relationship between theory and research. Discusses the advantages and disadvantages of various data collection techniques, and their reliability and validity. Topics covered include an overview of the research process, selection of research questions, hypothesis construction, measurement, survey sampling, questionnaire construction, mailed questionnaires, data management and analysis, presentation and interpretation of data, ethics in social research, and theory construction, evaluation and testing.
Champion, DJ. Basic Statistics for Social Research. New York: Macmillan Publishing, Inc., 1981.
Describes the role of statistics in social research. Provides a broad description of statistical tests and their application. Discusses strengths and weaknesses of each test. Appendix includes statistical tables and instructions on using them. Forms a foundation for advanced statistical methods.
Kidder, LH Selltiz, Wrightsman and Cook's Research Methods in Social Relations. New York: Holt, Rinehart and Winston, 1981.
Discusses various data collection strategies. Experimental designs, quasi-experimental designs, and survey research designs are described. Presents information on analyzing data and preparing reports. Addresses ethical issues in research. Measurement issues such as reliability and validity, questionnaires and interviews, scaling, indirect assessment, and observational and archival data are discussed. An introduction to sampling is included in the Appendix.
Kraemer, HC and S Thiemann. How Many Subjects? Statistical Power Analysis in Research. Newbury Park, CA: Sage. 1987.
This book is somewhat of a classic in research. It contains on overview of all of the issues in power analysis and has separate chapters on power analysis in various forms of analyses (e.g., correlations, linear regressions, equality of means). It contains formulas for use in calculating power for these approaches.
Levy, PS and S Lemeshow. Sampling of Populations: Methods and Applications. NY:Wiley. 1991.
This book is about sample surveys—descriptive studies where the objective is the estimation of population values from sample data, e.g., population means or proportions. The original edition was titled Sampling for Health Professionals (1980). It is intended for the applied researcher/statistician. It covers the most common sampling plans, e.g., simple random, stratified, and cluster, and estimation procedures for obtaining unbiased parameter estimates and standard errors for each of the sampling plans presented. The examples are detailed and easy to follow.
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Strengthening Causal Inference in
Nonrandomized Health Disparity Designs
Research in racial/ethnic health disparities is increasingly interested in identifying the causal mechanisms by which social inequities are translated into disparities in health outcomes. Of necessity, much of this kind of research is observational or naturalistic in design and relies on correctly specified causal modes to explicate the structural paths and control for confounding. Inherent methodological problems in these designs include preexisting group differences, specification of relevant control variables (potential confounders), and measurement error. Some helpful readings are described below.
Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues for field settings. Chicago: Rand McNally.
Chapters 2 and 3 describe threats to internal and external validity in categories of sample selection, research design, measurement, and statistical conclusions. Chapter 4 by Charles Reichardt covers essentials in statistical control of confounding including problems posed by unreliable measurement.
Cochran, W. G. (1965). The planning of observational studies of human populations. Journal of the Royal Statistical Society, Series A (General), 128(2), 234-266.
Cochran makes a strong case for causal analysis, includes recommendations on the planning, analysis, and interpretation of observational data from a statistician’s perspective.
Shadish, W. R. (2002). Revisiting field experiments: Field notes for the future. Psychological Methods, 7(1): 3-18.
Shadish covers recent developments in quasi experimental and observational designs including multilevel analyses, propensity scoring adjustment methods, and Rubin’s causal model.http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=11928889&query_hl=11&itool=pubmed_docsum
Wainer, H. (1991). Adjusting for differential base-rates: Lord's paradox again. Psychological Bulletin, 109, 147-151.
Wainer proposes some guidelines for the choice between difference score adjustment and regression adjustment. Very useful because methodological problems with the use of difference scores have steered analyst’s away from what often seems so sensible on intuitive grounds. Wainer’s analysis supports the use of difference scores when the expected change in the control group is nil.
Pocock, S. J., Assmann, S. E., et al. (2002). Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Statistics in Medicine, 21(19), 2917-2930.
This is a very informative discussion of covariate selection and of the need for well explicated causal models to guide analysis. Randomized designs face many of the same problems in analysis as nonrandomized designs do when moderating (or mediating) variables are involved. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=12325108&query_hl=5&itool=pubmed_docsum
Greenland, S., & Brumback, B. (2002). An overview of relations among causal modeling methods. International Journal of Epidemiology ,31(5), 1030-1037.
This article describes four types of causal models for health-sciences research: structural-equations models, graphical models based on the work of Jueda Pearl, potential-outcome (counterfactual) models (Rubin’s causal model), and the sufficient-component cause model. Causal analysis is, in some sense, a field of study not tied to a particular analytic technique. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=12435780&query_hl=7&itool=pubmed_docsum
Hernan, M. A., Hernandez-Diaz, S., et al. (2002). Causal knowledge as a prerequisite for confounding evaluation: An application to birth defects epidemiology. American Journal of Epidemiology, 155(2), 176-184.
The authors use examples from epidemiology to show how blind statistical adjustment can go wrong. A priori knowledge and causal models are essential to determine the appropriate covariance adjustment model. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=11790682&query_hl=9&itool=pubmed_docsum
Pearl, J. (2000). Causality: Models, reasoning, and inference. New York, NY, Cambridge University Press.
Pearl’s work represents the most comprehensive new development in thinking about causal modeling. It is somewhat difficult because so much of it is new.
Hayduk, L., Cummings, G., et al. (2003). Pearl's D-separation: One more step into causal thinking. Structural Equation Modeling, 10(2), 289-311.
This is a good introduction to some of Pearl’s key ideas.
Winship, C. & Harding, D. J. (2004). A General Strategy for the Identification of Age, Period, Cohort Models: A Mechanism Based Approach http://www.wjh.harvard.edu/~winship/cfa_papers/WinshipHardingAPC.pdf.
The authors apply causal modeling ideas to solve a difficult problem in developmental research.
Last updated December 2005
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