|
Graduate students working with Dr. Elhai on a
thesis or dissertation should have a broad level of knowledge in
conducting quantitative statistical/data analysis. This knowledge level
should be achieved by completing the clinical program’s statistics
course sequence, and Multivariate Statistics course.
In addition, when it is time for the student to
propose (as well as later defend) his/her thesis/dissertation, s/he
should review in thorough detail how to analyze data using the specific
data analyses indicated for the project. Listed below are the most
common data analyses Dr. Elhai's research group uses, and recommended
readings for reviewing information on these analytic procedures.
Many of these
recommended readings are full chapters from Tabachnick and Fidell's
(2007) multivariate statistics text.
-----
-Detecting skewed and kurtotic data; data
transformations: Tabachnick & Fidell's (2007) chapter on "Cleaning
up your act."
-Examining and treating missing data:
Graham, Cumsille, & Elek-Fisk (2003); Schafer & Graham (2002).
-----
-Cluster analysis: Aldenderfer & Blashfield
(1984); Blashfield (1980).
-Confirmatory factor analysis: (See "structural
equation modeling" below).
-Count regression models (e.g., Poisson,
negative binomial, zero-inflated, zero-truncated regression): (see
relevant chapter on count regression models in) Long (1997); Long &
Freese (2006).
-Diagnostic test performance (e.g.,
sensitivity, specificity): Elwood (1993).
-Discriminant function analysis: Tabachnick
& Fidell (2007).
-Effect size and power: Cohen (1992).
-Exploratory factor analysis/principal
components analysis: Tabachnick & Fidell (2007); Fabrigar, Wegener,
MacCallum & Strahan (1999).
-Interactions (See "mediation/moderation"
below).
-Logistic regression: Tabachnick & Fidell
(2007).
-Mediation/moderation: Aiken & West (1991); Baron & Kenny
(1986); for a brief overview see Frazier, Tix, & Barron (2004).
-Multivariate analysis of variance/covariance:
Tabachnick & Fidell (2007).
-Ordinary least-squares multiple regression:
Tabachnick & Fidell (2007).
-Path analysis: (See "structural equation
modeling" below).
-Receiver operating curve analysis: Metz
(1978); Hanley & McNeil (1982, 1983).
-Structural equation modeling: For a
general, easy-to-read intermediate level of understanding (sufficient
for a thesis or dissertation), see Kline (2004); for a sophisticated,
advanced level of understanding, see Bollen (1989); for supplemental
information on testing model fit, see Hu & Bentler (1998, 1999) and Muthen &
Muthen (2006).
References
Aiken, L. S., & West, S.
G. (1991). Multiple regression: Testing and interpreting interactions.
London, England: Sage Publications.
Aldenderfer, M. S., &
Blashfield, R. K. (1984). Cluster analysis. Newbury Park,
California: Sage.
Baron, R. M., & Kenny, D.
A. (1986). The moderator-mediator variable distinction in social
psychological research: Conceptual, strategic, and statistical
considerations. Journal of Personality and Social Psychology, 51,
1173-1182.
Blashfield, R. K. (1980).
Propositions regarding the use of cluster analysis in clinical research.
Journal of Consulting and Clinical Psychology, 48, 456-459.
Bollen, K. A. (1989).
Structural equations with latent variables. New York City: John
Wiley & Sons.
Cohen, J. (1992). A power
primer. Psychological Bulletin, 112, 155-159.
Elwood, R. W. (1993).
Psychological tests and clinical discriminations: Beginning to address
the base rate problem. Clinical Psychology Review, 13, 409-419.
Fabrigar, L. R., Wegener,
D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of
exploratory factor analysis in psychological research. Psychological
Methods, 4, 272-399.
Frazier, P. A., Tix, A.
P., & Barron, K. E. (2004). Testing moderator and mediator effects in
counseling psychology research. Journal of Counseling Psychology, 51,
115-134.
Graham, J. W., Cumsille,
P. E., & Elek-Fisk, E. (2003). Methods for handling missing data. In J.
A. Schinka, I. B. Weiner & W. F. Velicer (Eds.), Handbook of
psychology. Research methods in psychology (Vol. 2, pp. 87-114). New
York, New York: John Wiley & Sons.
Hanley, J. A., & McNeil,
B. J. (1982). The meaning and use of the area under a receiver operating
characteristic (ROC) curve. Radiology, 143, 29-36.
Hanley, J. A., & McNeil,
B. J. (1983). A method of comparing the areas under receiver operating
characteristic curves derived from the same cases. Radiology, 148,
839-843.
Hu, L., & Bentler, P. M.
(1998). Fit indices in covariance structural modeling: Sensitivity to
underparameterized model misspecification. Psychological Methods, 3,
424-453.
Hu, L., & Bentler, P. M.
(1999). Cutoff criteria for fit indexes in covariance structure
analysis: Conventional criteria versus new alternatives. Structural
Equation Modeling, 6, 1-55.
Kline, R. B. (2004).
Principles and practice of structural equation modeling (2nd ed.).
New York, New York: Guilford Press.
Long, J. S. (1997).
Regression models for categorical and limited dependent variables.
Thousand Oaks, California: Sage Publications.
Long, J. S., & Freese, J.
(2006). Regression models for categorical dependent variables using
Stata (2nd ed.). College Station, Texas: StataCorp.
Metz, C. E. (1978). Basic
principles of ROC analysis. Seminars in Nuclear Medicine, 8,
283-298.
Muthén, B. O., & Muthén,
L. (2006). Chi-square difference testing using the Satorra-Bentler
scaled chi-square. 2006, from http://statmodel.com/chidiff.shtml
Schafer, J. L., & Graham,
J. W. (2002). Missing data: Our view of the state of the art.
Psychological Methods, 7, 147-177.
Tabachnick, B. G., &
Fidell, L. S. (2007). Using multivariate statistics (5th ed.).
Boston, Massachusetts: Allyn and Bacon.
|
