Abstract
In educational measurement, multiple test forms are often constructed to measure the same construct. Linking procedures can be used to disentangle differences in test form difficulty and differences in the proficiency of examinees so that scores for different test forms can be used interchangeably. Multiple data collection designs can be used for collecting data to be used for linking. Differential motivation refers to the difference in test-taking motivation that exists between high-stakes and low-stakes administration conditions. In a high-stakes administration condition, an examinee is expected to work harder and strive for maximum performance, whereas a low-stakes administration condition elicits typical, rather than maximum, performance. Differential motivation can be considered a confounding variable when choosing a data collection design. We discuss the suitability of different data collection designs and the way they are typically implemented in practice with respect to the effect of differential motivation. An example using data from the Eindtoets Basisonderwijs (End of Primary School Test) highlights the need to consider differential motivation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Despite the theoretical difference between linking and equating, the same statistical methods are used in the two procedures. Therefore, the terms equating and linking are used interchangeably for the purpose of this paper.
References
Angoff WH (1971) Scales, norms, and equivalent scores. In: Thorndike RL (ed) Educational measurement, 2nd edn. American Council of Education, Washington, pp 508–600
Béguin AA (2000) Robustness of equating high-stakes tests. Unpublished doctoral dissertation, Twente University, Enschede, The Netherlands
Béguin AA, Maan A (2007) IRT linking of high-stakes tests with a low-stakes anchor. Paper presented at the 2007 Annual National Council of Measurement in Education (NCME) meeting, April 10–12, Chicago
Cohen J (1988) Statistical power analysis for the behavioural sciences, 2nd edn. Lawrence Erlbaum Associates, Hillsdale
Embretson SE, Reise SP (2000) Item response theory for psychologists. Lawrence Erlbaum, Mahwah
Emons WHM (1998) Nonequivalent groups IRT observed-score equating: its applicability and appropriateness for the Swedish Scholastic Aptitude Test. Twente University (unpublished report)
Holland PW, Rubin DR (eds) (1982) Test equating. Academic, New York
Holland PW, Wightman LE (1982) Section pre-equating: a preliminary investigation. In: Holland PW, Rubin DR (eds) Test equating. Academic, New York, pp 271–297
Kiplinger VL, Linn RL (1996) Raising the stakes of test administration: the impact on student performance on the National Assessment of Educational Progress. Educ Assess 3:111–133
Kolen MJ, Brennan RL (2004) Test equating, scaling, and linking, 2nd edn. Springer Verlag, New York
Linacre JM (2002) What do infit and outfit, mean-square and standardized mean? Rasch Meas 16:878
Maier MH (1993) Military aptitude testing: the past fifty years (DMCM Technical Report 93-700). Defence Manpower Data Center, Montery, CA
Mair P, Hatzinger R, Maier M (2010) eRm: Extended Rasch Modeling. Retrieved from http: //CRAN.R-project.org/package=eRm
Meijer RR, Sijtsma K (2001) Methodology review: evaluating person fit. Appl Psychol Meas 25:107–135
Mittelhaëuser M, Béguin AA, Sijtsma K (2011) Comparing the effectiveness of different linking designs: the internal anchor versus the external anchor and pre-test data (Report No. 11-01). Retrieved from Psychometric Research Centre Web site: http://www.cito.nl/~/media/cito_nl/Files/Onderzoek%20en%20wetenschap/cito_mrd_report_2011_01.ashx
Mittelhaëuser M, Béguin AA, Sijtsma K (2013) Modeling differences in test-taking motivation: exploring the usefulness of the mixture Rasch model and person-fit statistics. In: Millsap RE, van der Ark LA, Bolt DM, Woods CM (eds) New developments in quantitative psychology. Springer, New York, pp 357–370
O’Neill HF, Sugrue B, Baker EL (1996) Effects of motivational interventions on the National Assessment of Educational Progress mathematics performance. Educ Assess 3:135–157
Rasch G (1960) Probabilistic models for some intelligence and attainment tests. Danish Institute for Educational Research, Copenhagen
Reckase MD (2009) Multidimensional item response theory models. Springer Verlag, New York
Reise SP, Flannery WP (1996) Assessing person-fit on measures of typical performance. Appl Meas Educ 9:9–26
Scheerens J, Glas C, Thomas SM (2003) Educational evaluation, assessment and monitoring: a systematic approach. Swets & Zeitlinger, Lisse
Verhelst ND, Glas CAW, Verstralen HHFM (1995) One-parameter logistic model (OPLM). Cito, National Institute for Educational Measurement, Arnhem
von Davier AA (2013) Observed-score equating: an overview. Psychometrika 78:605–623
von Davier AA, Holland PW, Thayer DT (2004) The kernel method of test equating. Springer, New York
Wise SL, DeMars CE (2005) Low examinee effort in low-stakes assessment: problems and potential solutions. Educ Assess 10:1–17
Wise SL, Kong X (2005) Response time effort: a new measure of examinee motivation in computer-based tests. Appl Meas Educ 18:163–183
Wolf LF, Smith JK (1995) The consequence of consequence: motivation, anxiety and test performance. Appl Meas Educ 8:227–242
Wolf LF, Smith JK, Birnbaum ME (1995) The consequence of performance, test, motivation, and mentally taxing. Appl Meas Educ 8:341–351
Wright BD, Masters GN (1982) Rating scale analysis. Mesa Press, Chicago
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Mittelhaëuser, MA., Béguin, A.A., Sijtsma, K. (2015). Selecting a Data Collection Design for Linking in Educational Measurement: Taking Differential Motivation into Account. In: Millsap, R., Bolt, D., van der Ark, L., Wang, WC. (eds) Quantitative Psychology Research. Springer Proceedings in Mathematics & Statistics, vol 89. Springer, Cham. https://doi.org/10.1007/978-3-319-07503-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-07503-7_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07502-0
Online ISBN: 978-3-319-07503-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)