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Disentangling Setting and Mode Effects for Online Competence Assessment

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Education as a Lifelong Process

Part of the book series: Edition ZfE ((EZFE,volume 3))

Abstract

Many large-scale competence assessments such as the National Educational Panel Study (NEPS) have introduced novel test designs to improve response rates and measurement precision. In particular, unstandardized online assessments (UOA) offer an economic approach to reach heterogeneous populations that otherwise would not participate in face-to-face assessments. Acknowledging the difference between delivery, mode, and test setting, this chapter extends the theoretical background for dealing with mode effects in NEPS competence assessments (Kroehne and Martens in Zeitschrift für Erziehungswissenschaft 14:169–186, 2011 2011) and discusses two specific facets of UOA: (a) the confounding of selection and setting effects and (b) the role of test-taking behavior as mediator variable. We present a strategy that allows the integration of results from UOA into the results from proctored computerized assessments and generalizes the idea of motivational filtering, known for the treatment of rapid guessing behavior in low-stakes assessment. We particularly emphasize the relationship between paradata and the investigation of test-taking behavior, and illustrate how a reference sample formed by competence assessments under standardized and supervised conditions can be used to increase the comparability of UOA in mixed-mode designs. The closing discussion reflects on the trade-off between data quality and the benefits of UOA.

Zusammenfassung

Viele großangelegte Assessmentprogramme wie das National Bildungspanel führen neue Testdesigns ein, um die Antwortraten und die Messgenauigkeit zu verbessern. Insbesondere bietet unstandardisiertes Online-Assessments (UOA) eine ökonomische Möglichkeit, um heterogene Bevölkerungsgruppen zu erreichen, die ansonsten nicht an direkten Testung teilnehmen würden. Unter Berücksichtigung des Unterschieds zwischen Testauslieferung, Testmodus und Testsetting erweitert dieses Kapitel den theoretischen Hintergrund für den Umgang mit Moduseffekten in der Kompetenztestung des Nationalen Bildungspanels (NEPS; Kroehne und Martens 2011) und diskutiert zwei spezifische Facetten von UOA: a) Die Konfundierung von Selektionseffekten und Effekten des Testsettings und b) die Rolle des Testbearbeitungsverhaltens als Mediatorvariable. Wir stellen eine Strategie vor, die die Integration von Ergebnissen aus UOA in Ergebnisse computerbasierter Kompetenztestung ermöglicht und welche die Idee des Motivationsfilterns verallgemeinert, das für die Behandlung von schnellem Rateverhalten in Low-Stakes-Assessments bekannt ist. Dabei wird insbesondere der Zusammenhang zwischen Paradaten und der Erforschung von Testbearbeitungsverhalten hervorgehoben. Es wird gezeigt, wie eine Referenzstichprobe mit Kompetenztestung unter standardisierten und überwachten Testbedingungen verwendet werden könnte, um die Vergleichbarkeit von UOA in Mixed-Mode-Designs zu verbessern. Die abschließende Diskussion reflektiert den aus dem Vorgehen resultierenden Kompromiss zwischen Datenqualität und den Vorteilen von UOA.

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Notes

  1. 1.

    The similarity to big data, characterized with V’s (see, for instance, Kitchin and McArdle 2016) has been chosen carefully.

  2. 2.

    Computer-based assessments are used routinely in NEPS in standardized settings, and online delivered tests can also be administered in standardized settings (e.g., Csapó et al. 2014).

  3. 3.

    Apparent differences between modes—such as different layouts, question and task designs, and so forth in the sense of Dillman (2000)—were avoided (unified design) as far as technically possible.

  4. 4.

    To achieve a meaningful comparison of response rates between deliveries (UOA vs. CAPI), the assumption that everything else is equal is crucial when taking into account the complete process of recruitment and invitation to an assessment. Depending on the design of a particular wave, different assessment modes might be combined. A combination used in one particular wave in NEPS is the mixture of CAPI for one random subsample of the cohort and a combination of CATI and UOA for the remaining subsample. The mixture of CATI and UOA incorporates two selection processes: participating in the CATI first followed by the decision to participate in the UOA. Taking both together, the sample composition for the assessment part of interest (i.e., the competence test administered in the CAPI and UOA delivery) is the result of two different selection processes that might best be described as one measurement point (CAPI) versus two measurement points (CATI and UOA). For the resulting samples, the assumption of random equivalent groups seems hardly justifiable without additional verifications and, if necessary, subsequent adjustments.

  5. 5.

    Response rates, given a sample member has responded in a previous wave, correspond to attrition rates (if the unit nonresponse is a final dropout) or temporary dropouts. In waves with competence assessments, temporary dropout is equivalent to test refusal).

  6. 6.

    Note that this is true if the random assignment of respondents to the delivery mode cannot be conducted after the recruitment (Jäckle et al. 2010) that serves as the decision to participate in a particular wave in a panel study.

  7. 7.

    Test-taking behavior can be studied experimentally by, for instance, using different instructional sets, as often done to determine the limits on fakability of personality scales (see for a meta-analysis, Viswesvaran and Ones 1999). Similarly, mediator variables can become treatment variables. However, when the test setting (and not the test-taking behavior) is randomly assigned, the values of the mediator are only observed variables.

  8. 8.

    Complete Responders, Unit Nonresponders, Answering Dropouts, Lurkers, Lurking Drop-Outs, Item Nonresponders, and Item Nonresponding Dropouts.

  9. 9.

    Competence tests are administered with time limits for each domain. Due to the time limits, it is possible to distinguish between omitted responses (i.e., unanswered items that are followed by answered questions) and not reached items (i.e., unanswered items that are not followed by an answered question in a test part due to the time constraint).

  10. 10.

    Using the empirical reference sample allows us to apply the approach even if no normative threshold exists or the appropriateness of thresholds is in doubt (e.g., outdated, derived for a different target population or different domain, etc.).

  11. 11.

    “Für die ersten zwei Teile haben Sie jeweils 30 Minuten Zeit. Es ist nicht möglich, die Bearbeitung der Aufgaben zu unterbrechen und später fortzusetzen. Nehmen Sie sich deshalb bitte eineinhalb Stunden am Stück Zeit.” [For each of the first two parts, you have 30 min. It is not possible for you stop answering the tasks to take a break and continue later. So please reserve 1.5 h time for the test.]

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Authors and Affiliations

  1. German Institute for International Educational Research (DIPF), Frankfurt am Main, Germany

    Ulf Kroehne & Frank Goldhammer

  2. Leibniz Institute for Educational Trajectories; Johannes Kepler University Linz, Bamberg, Germany

    Timo Gnambs

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    Hans-Peter Blossfeld

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    Hans-Günther Roßbach

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Kroehne, U., Gnambs, T., Goldhammer, F. (2019). Disentangling Setting and Mode Effects for Online Competence Assessment. In: Blossfeld, HP., Roßbach, HG. (eds) Education as a Lifelong Process. Edition ZfE, vol 3. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-23162-0_10

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