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Universität Duisburg-Essen

Fakultät für Bildungswissenschaften

Lehrstuhl für Lehr-Lernpsychologie

Person Fit Analysis with Simulation-based Methods

Dissertation zur Erlangung des Grades Dr. phil.

vorgelegt von Christian Spoden

geboren am 27.01.1982 in Mülheim a.d. Ruhr

Erstgutachter: Prof. Dr. Dr. Detlev Leutner, Universität Duisburg-Essen

Zweitgutachter: Prof. Dr. Christian Tarnai, Universität der Bundeswehr München

Tag der mündlichen Prüfung: 16. Juli 2014

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Figure 3.1. Empirical Type I error rates of two person fit statistics and two approaches

to generate p-values (normalization formula, NOR; Markov chain Monte Carlo

simulation of the Rasch Sampler, MCMC (RS)). A: statistic U3, 20 items; B: statistic l0,

20 items; C: statistic U3, 40 items; D: statistic l0, 40 items; E: statistic U3, 60 items; F:

statistic l0, 60 items.

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3.4.2 Evaluation of statistical power and Type I error rate

Power and Type I error rate are evaluated under a fixed nominal -level. Power is

estimated in the guessing and the cheating conditions as the percentage of aberrant response

vectors with a p-value of the person fit statistic smaller than . The Type I error rate is

estimated as the percentage of non-aberrant response vectors with a p-value of the person fit

statistic smaller than . The same -levels as in Simulation 1 were used ( = .05 and = .10).

3.4.3 Results

Power rates of the statistics are presented in Figures 3.2 and 3.3. For both cheating and

guessing power increase with increasing item number, percentage of aberrancy and α-level.

Cheating was generally easier to detect than guessing. In the cheating conditions, power is in

most conditions highest for MCMC (RS), except for 20 items and an aberrancy rate of 20 %,

where higher rates are found for U3 under NOR. With the parametric statistic l0, advantages

of MCMC (RS) are stronger than for the nonparametric statistic U3. Differences between

both methods also grow with decreasing item number. Over all test lengths, power rates for

U3 and l0 are very similar under MCMC (RS), while under NOR U3 outperforms l0. These

differences reflect inflated Type I error rates of U3NOR and deflated Type I error rates of lNOR

(Emons et al., 2002). The highest power in all cheating conditions is found for both U3 and l0

and in the condition of 60 items, an aberrancy rate of 40 % and = .10, where the percentage

of correctly detected vectors is near 100 % with MCMC (RS). The lowest rates are found for

20 items, an aberrancy rate of 20 % and = .05, where rates below .35 indicate that model

violations are generally hard to detect.

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