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Measurement and Modelling Lab

Current Projects in the MML 

Advancing Questionnaire Design/Use

Our current focus is studying response processes on questionnaires of health/well-being/depressive symptomology using a combination of quantitative and qualititative research methodologies. Of particular interest is the language background of the respondent as well as linguistic features of the items on the questionnaire, and their role in the response generation process by the respondent. We are using questionnaire surveys as well as interviews with our study participants to gain understanding of commonalities and very individualized processes/responses.

Advancing Statistical Practice

A key aim in our lab is to advance statistical practice. A current focus is on disseminating underutilized statistical procedures, including but not limited to procedures that Fouladi and colleagues have studied and/or developed, e.g., analysis of correlation pattern models. Because easy user-interface is important to helping the adoption of statistical procedures, the MML lab is working to provide web-based Shiny implementations of some of these procedures. 

MMLR2 (2017): https://shiny.rcg.sfu.ca/u/pserafin/rsquared

R2 was developed by Steiger and Fouladi (1992) to advance the practice of multiple regression analysis planning and results reporting. The original R2 program is an MSDOS program which implements procedures not generally available in commonly used statistical software, and provides an early illustration of the application of the use of principles of non-centrality interval estimation discussed in the chapter by Steger and Fouladi (1997) in the widely cited book What if there were no significance tests (Harlow, Mulaik, & Steiger, eds., 1997). R2 provides exact confidence intervals for the squared multiple correlation coefficent based on a random regressor model, varied power calculations, distributional probability calculations, and non-standard hypothesis tests of the squared multiple correlation. 

MMLR2 implements some of the key features of R2 but also uses the more commonly used fixed regressor model framework for some the procedures.

MMLR2 provides a web-based interface permitting users to perform:

  • Calculation of confidence intervals and lower confidence bounds for the squared multiple correlation, using either a fixed regressor method or the random regressor method described by Zou (2007) .
  • Power calculation for tests of significance of the squared multiple correlation based on a fixed regressor model.
  • Calculation of the sample size necessary to achieve a desired level of power for testing a hypothesis of zero multiple correlation using a fixed regressor model.
  • Calculation of the sample size necessary to achieve a desired level of power for testing a hypothesis of a multiple correlation value other than zero, using a fixed regressor model.

MMLWBCORR (2017): https://shiny.rcg.sfu.ca/u/pserafin/wbcorr/

WBCORR (Within-Between CORRelational tests) is correlation pattern hypotheis test program developed for Mathematica by Steiger (2004). WBCORR can handle raw or correlation data, in one or more samples, with or without equal sample sizes, and with or without the assumption of multivariate normaltiy. The program implements GLS, TSGLS, ADF, and TSADF chi-square statistics. See Steiger (2004) for a discussion of the methods employed by WBCORR.

MMLWBCORR provides a web-based interface permitting users to perform analyses similar to those analyzable using Steiger's WBCORR, using the GLS, TSGLS, ADF, and TSADF chi-square statistics. However, MLWBCORR differs from the Steiger's Mathematica WBCORR (2004) in its implementation of tests of correlation patterns for common specified values, and its treatment of missing data. In particular, MLWBCORR draws on work presented in Yuan, Lambert, Fouladi (2004) for tests of multivariate normality under conditions of missing data conditions.