Intrinsic & extrinsic motivation scales of the adapted MLSQ: A Rasch-based construct validity study

Keywords: Assessment, Higher Education, learning patterns, Motivation

Presenting Author:Tine Nielsen, University of Copenhagen, Denmark

The aim was to conduct a validity study to ascertain the psychometric properties of the Danish translation of the intrinsicand extrinsic motivation (IM and EM) subscales of the Motivated Strategies for Learning Questionnaire (Pintrich et al., 1991)in a higher education context. The items of the IM and EM scales were translated using a forward-backward approachinvolving three subject matter and psychometric experts. The response scale was adapted from a 7-point scale withmeaning anchors only at the extremes to a 4-point scale with meaning anchors for all response categories. Raschmeasurement models; the ordinal Rasch model (RM; Masters, 1982) and graphical loglinear Rasch models (GLLRM;Kreiner & Christensen, 2002, 2204, 2007), were employed. The analyses emphasized whether the IM and EM wereseparate constructs or either end of a single construct, local independence of items, and measurement invariance (i.e. nodifferential item functioning) relative to age, gender, year cohort and admission quota. The data sample was collected to behighly comparable across for the purpose of conducting a validity study. The sample consisted of three consecutive yearcohorts of psychology students enrolled in a full term course in personality psychology placed in the second term of thebachelor program in psychology (N = 590, cohort response rates 88%, 84%, and 84%, for the 2015, 2016 and 2017cohorts, respectively ). Data was collected one month into the personality course/the second term thus addressing studentmotivation in relation to personality psychology and at the same time point in the bachelor program. Result confirmed thatthe IM and EM subscales were two separate subscales, which were negatively though weakly correlated. Neither the IM orthe EM subscales fit the pure RM, but the departures could be adjusted for in in both cases. No evidence of DIF relative togender, year cohort, or admission quota. Thus both the IM and the EM subscales fit GLLRMs of slightly different complexity: the IM fit a GLLRM adjusted for DIF on one item relative to age and local dependence between two items,while the EM fit a GLLRM only with local dependence between two items. Targeting of the subscales to the students wasgood for both subscales, while reliability was good for the EM subscale and the oldest students on the IM subscale. TheEM score was associated with age, so that the youngest students were more extrinsically motivated, while there was nosignificant association between the IM score and age after adjusting for DIF. The Implications of the results are that theDanish IM and EM subscales can be used to study motivation in a higher education context. However, it is suggested toalso undertake further validity studies focusing on measurement invariance across academic disciplines, at different timepoints in degree programs, and addressing motivation in relation to a variety of courses or subjects. Further, validity studiesfocused on the issue of cross-cultural measurement invariance across a number of language version, could substantiallyadvance the understanding of motivation in higher education across cultural settings.

References

Kreiner, S., & Christensen, K. B. (2002). Graphical Rasch models. In M. Mesbah, B. F. Cole, & M. T. Lee (Eds.), Statistical methods for quality of life studies (pp. 187–203). Dordrecht: Kluwer Academic Publishers. http://dx.doi.org/10.1007/978-1-4757-3625-0_15.

Kreiner, S., & Christensen, K. B. (2004). Analysis of local dependence and multidimensionality in graphical loglinear Rasch models. Communication in Statistics –Theory and Methods, 33(6), 1239–1276. http://dx.doi.org/10.1081/sta-120030148.

Kreiner, S., & Christensen, K. B. (2007). Validity and objectivity in health-related scales: Analysis by graphical loglinear Rasch models. In von Davier, & Carstensen (Eds.), Multivariate and mixture distribution Rasch models (pp. 329–346). New York: Springer. http://dx.doi.org/10.1007/978-0-387-49839-3_21.

Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149–174. http://dx.doi.org/10.1007/BF02296272.

Pintrich, P.R., Smith, D.A.F., Garcia, T., McKeachie, W.J. (1991). A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). Technical Report No. 91-8-004. The Regents of The University of Michigan.