1. //
  2. Start
  3. //
  4. Publications
  5. On the utility of indirect methods for d...
  6. Output formats
  7. //

On the utility of indirect methods for detecting faking

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

On the utility of indirect methods for detecting faking. / Goldammer, Philippe; Stöckli, Peter Lucas; Escher, Yannik Andrea et al.
In: Educational and Psychological Measurement, Vol. 84, No. 5, 10.2024, p. 841-868.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

Goldammer, P, Stöckli, PL, Escher, YA, Annen, H & Jonas, K 2024, 'On the utility of indirect methods for detecting faking', Educational and Psychological Measurement, vol. 84, no. 5, pp. 841-868. https://doi.org/10.1177/00131644231209520

APA

Goldammer, P., Stöckli, P. L., Escher, Y. A., Annen, H., & Jonas, K. (2024). On the utility of indirect methods for detecting faking. Educational and Psychological Measurement, 84(5), 841-868. https://doi.org/10.1177/00131644231209520

Vancouver

Goldammer P, Stöckli PL, Escher YA, Annen H, Jonas K. On the utility of indirect methods for detecting faking. Educational and Psychological Measurement. 2024 Oct;84(5):841-868. doi: 10.1177/00131644231209520

Bibtex

@article{651906bdeadc43c0a7444680076b85b2,
title = "On the utility of indirect methods for detecting faking",
abstract = "Indirect indices for faking detection in questionnaires make use of a respondent{\textquoteright}s deviant or unlikely response pattern over the course of the questionnaire to identify them as a faker. Compared with established direct faking indices (i.e., lying and social desirability scales), indirect indices have at least two advantages: First, they cannot be detected by the test taker. Second, their usage does not require changes to the questionnaire. In the last decades, several such indirect indices have been proposed. However, at present, the researcher{\textquoteright}s choice between different indirect faking detection indices is guided by relatively little information, especially if conceptually different indices are to be used together. Thus, we examined and compared how well indices of a representative selection of 12 conceptionally different indirect indices perform and how well they perform individually and jointly compared with an established direct faking measure or validity scale. We found that, first, the score on the agreement factor of the Likert-type item response process tree model, the proportion of desirable scale endpoint responses, and the covariance index were the best-performing indirect indices. Second, using indirect indices in combination resulted in comparable and in some cases even better detection rates than when using direct faking measures. Third, some effective indirect indices were only minimally correlated with substantive scales and could therefore be used to partial faking variance from response sets without losing substance. We, therefore, encourage researchers to use indirect indices instead of direct faking measures when they aim to detect faking in their data.",
keywords = "Business psychology",
author = "Philippe Goldammer and St{\"o}ckli, {Peter Lucas} and Escher, {Yannik Andrea} and Hubert Annen and Klaus Jonas",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2023.",
year = "2024",
month = oct,
doi = "10.1177/00131644231209520",
language = "English",
volume = "84",
pages = "841--868",
journal = "Educational and Psychological Measurement",
issn = "0013-1644",
publisher = "SAGE Publications Inc.",
number = "5",

}

RIS

TY - JOUR

T1 - On the utility of indirect methods for detecting faking

AU - Goldammer, Philippe

AU - Stöckli, Peter Lucas

AU - Escher, Yannik Andrea

AU - Annen, Hubert

AU - Jonas, Klaus

N1 - Publisher Copyright: © The Author(s) 2023.

PY - 2024/10

Y1 - 2024/10

N2 - Indirect indices for faking detection in questionnaires make use of a respondent’s deviant or unlikely response pattern over the course of the questionnaire to identify them as a faker. Compared with established direct faking indices (i.e., lying and social desirability scales), indirect indices have at least two advantages: First, they cannot be detected by the test taker. Second, their usage does not require changes to the questionnaire. In the last decades, several such indirect indices have been proposed. However, at present, the researcher’s choice between different indirect faking detection indices is guided by relatively little information, especially if conceptually different indices are to be used together. Thus, we examined and compared how well indices of a representative selection of 12 conceptionally different indirect indices perform and how well they perform individually and jointly compared with an established direct faking measure or validity scale. We found that, first, the score on the agreement factor of the Likert-type item response process tree model, the proportion of desirable scale endpoint responses, and the covariance index were the best-performing indirect indices. Second, using indirect indices in combination resulted in comparable and in some cases even better detection rates than when using direct faking measures. Third, some effective indirect indices were only minimally correlated with substantive scales and could therefore be used to partial faking variance from response sets without losing substance. We, therefore, encourage researchers to use indirect indices instead of direct faking measures when they aim to detect faking in their data.

AB - Indirect indices for faking detection in questionnaires make use of a respondent’s deviant or unlikely response pattern over the course of the questionnaire to identify them as a faker. Compared with established direct faking indices (i.e., lying and social desirability scales), indirect indices have at least two advantages: First, they cannot be detected by the test taker. Second, their usage does not require changes to the questionnaire. In the last decades, several such indirect indices have been proposed. However, at present, the researcher’s choice between different indirect faking detection indices is guided by relatively little information, especially if conceptually different indices are to be used together. Thus, we examined and compared how well indices of a representative selection of 12 conceptionally different indirect indices perform and how well they perform individually and jointly compared with an established direct faking measure or validity scale. We found that, first, the score on the agreement factor of the Likert-type item response process tree model, the proportion of desirable scale endpoint responses, and the covariance index were the best-performing indirect indices. Second, using indirect indices in combination resulted in comparable and in some cases even better detection rates than when using direct faking measures. Third, some effective indirect indices were only minimally correlated with substantive scales and could therefore be used to partial faking variance from response sets without losing substance. We, therefore, encourage researchers to use indirect indices instead of direct faking measures when they aim to detect faking in their data.

KW - Business psychology

UR - http://www.scopus.com/inward/record.url?scp=85176914583&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/dfe1d40e-2d83-3d4a-b01d-b9b8ee8b8c8e/

U2 - 10.1177/00131644231209520

DO - 10.1177/00131644231209520

M3 - Journal articles

C2 - 39318482

VL - 84

SP - 841

EP - 868

JO - Educational and Psychological Measurement

JF - Educational and Psychological Measurement

SN - 0013-1644

IS - 5

ER -

Other publications by the same author(s)

Addressing climate change with behavioral science: A global intervention tournament in 63 countries

Vlasceanu, M., Doell, K. C., Bak-Coleman, J. B., Todorova, B., Berkebile-Weinberg, M. M., Grayson, S. J., Patel, Y., Goldwert, D., Pei, Y., Chakroff, A., Pronizius, E., Broek, K. L. V. D., Vlasceanu, D., Constantino, S., Morais, M. J., Schumann, P., Rathje, S., Fang, K., Aglioti, S. M., Alfano, M., Alvarado-Yepez, A. J., Andersen, A., Anseel, F., Apps, M. A. J., Asadli, C., Awuor, F. J., Azevedo, F., Basaglia, P., Bélanger, J. J., Berger, S., Bertin, P., Białek, M., Bialobrzeska, O., Blaya-Burgo, M., Bleize, D. N. M., Bø, S., Boecker, L., Boggio, P. S., Borau, S., Bos, B., Bouguettaya, A., Brauer, M., Brick, C., Brik, T., Briker, R., Brosch, T., Buchel, O., Buonauro, D., Butalia, R., Carvacho, H., Chamberlain, S. A. E., Chan, H-Y., Chow, D., Chung, D., Cian, L., Cohen-Eick, N., Contreras-Huerta, L. S., Contu, D., Cristea, V., Cutler, J., D'Ottone, S., Keersmaecker, J. D., Delcourt, S., Delouvée, S., Diel, K., Douglas, B. D., Drupp, M. A., Dubey, S., Ekmanis, J., Elbaek, C. T., Elsherif, M., Engelhard, I. M., Escher, Y. A., Etienne, T. W., Farage, L., Farias, A. R., Feuerriegel, S., Findor, A., Freira, L., Friese, M., Gains, N. P., Gallyamova, A., Geiger, S. J., Genschow, O., Gjoneska, B., Gkinopoulos, T., Goldberg, B., Goldenberg, A., Gradidge, S., Grassini, S., Gray, K., Grelle, S., Griffin, S. M., Grigoryan, L., Grigoryan, A., Grigoryev, D., Gruber, J., Guilaran, J., Hadar, B., Hahnel, U. J. J., Halperin, E., Harvey, A. J., Haugestad, C. A. P., Herman, A. M., Hershfield, H. E., Himichi, T., Hine, D. W., Hofmann, W., Howe, L., Huaman-Chulluncuy, E. T., Huang, G., Ishii, T., Ito, A., Jia, F., Jost, J. T., Jovanović, V., Jurgiel, D., Kácha, O., Kankaanpää, R., Kantorowicz, J., Kantorowicz-Reznichenko, E., Mintz, K. K., Kaya, I., Kaya, O., Khachatryan, N., Klas, A., Klein, C., Klöckner, C. A., Koppel, L., Kosachenko, A. I., Kothe, E. J., Krebs, R., Krosch, A. R., Krouwel, A. P. M., Kyrychenko, Y., Lagomarsino, M., Lamm, C., Lange, F., Cunningham, J. L., Lees, J., Leung, T. Y., Levy, N., Lockwood, P. L., Longoni, C., Ortega, A. L., Loschelder, D. D., Lu, J. G., Luo, Y., Luomba, J., Lutz, A. E., Majer, J. M., Markowitz, E., Marsh, A. A., Mascarenhas, K. L., Mbilingi, B., Mbungu, W., McHugh, C., Meijers, M. H. C., Mercier, H., Mhagama, F. L., Michalakis, K., Mikus, N., Milliron, S., Mitkidis, P., Monge-Rodríguez, F. S., Mora, Y. L., Moreau, D., Motoki, K., Moyano, M., Mus, M., Navajas, J., Nguyen, T. L., Nguyen, D. M., Nguyen, T., Niemi, L., Nijssen, S. R. R., Nilsonne, G., Nitschke, J. P., Nockur, L., Okura, R., Öner, S., Özdoğru, A. A., Palumbo, H., Panagopoulos, C., Panasiti, M. S., Pärnamets, P., Paruzel-Czachura, M., Pavlov, Y. G., Payán-Gómez, C., Pearson, A. R., Costa, L. P. D., Petrowsky, H. M., Pfattheicher, S., Pham, N. T., Ponizovskiy, V., Pretus, C., Rêgo, G. G., Reimann, R., Rhoads, S. A., Riano-Moreno, J., Richter, I., Röer, J. P., Rosa-Sullivan, J., Ross, R. M., Sabherwal, A., Saito, T., Sarrasin, O., Say, N., Schmid, K., Schmitt, M. T., Schoenegger, P., Scholz, C., Schug, M. G., Schulreich, S., Shreedhar, G., Shuman, E., Sivan, S., Sjåstad, H., Soliman, M., Soud, K., Spampatti, T., Sparkman, G., Spasovski, O., Stanley, S. K., Stern, J. A., Strahm, N., Suko, Y., Sul, S., Syropoulos, S., Taylor, N. C., Tedaldi, E., Tinghög, G., Huynh, L. D. T., Travaglino, G. A., Tsakiris, M., Tüter, İ., Tyrala, M., Uluğ, Ö. M., Urbanek, A., Valko, D., Linden, S. V. D., Schie, K. V., Stekelenburg, A. V., Vanags, E., Västfjäll, D., Vesely, S., Vintr, J., Vranka, M., Wanguche, P. O., Willer, R., Wojcik, A. D., Xu, R., Yadav, A., Zawisza, M., Zhao, X., Zhao, J., Żuk, D. & Bavel, J. J. V., 09.02.2024, In: Science Advances. 10, 6, 19 p., eadj5778.

Research output: Journal contributions › Journal articles › Research › peer-review

Careless responding detection revisited: Accuracy of direct and indirect measures

Goldammer, P., Stöckli, P. L., Escher, Y. A., Annen, H., Jonas, K. & Antonakis, J., 12.2024, In: Behavior Research Methods. 56, 8, p. 8422-8449 28 p.

Research output: Journal contributions › Journal articles › Research › peer-review

Context matters: Why women are not worse negotiators than men

Burmester, M., Escher, Y., Oomen, D. & Petrowsky, H., 2024, In: The In-Mind. 3/2024, 49

Research output: Journal contributions › Journal articles › Research › peer-review

The International Climate Psychology Collaboration: Climate change-related data collected from 63 countries

Doell, K. C., Todorova, B., Vlasceanu, M., Bak Coleman, J. B., Pronizius, E., Schumann, P., Azevedo, F., Patel, Y., Berkebile-Wineberg, M. M., Brick, C., Lange, F., Grayson, S. J., Pei, Y., Chakroff, A., van den Broek, K. L., Lamm, C., Vlasceanu, D., Constantino, S. M., Rathje, S., Goldwert, D., Fang, K., Aglioti, S. M., Alfano, M., Alvarado-Yepez, A. J., Andersen, A., Anseel, F., Apps, M. A. J., Asadli, C., Awuor, F. J., Basaglia, P., Bélanger, J. J., Berger, S., Bertin, P., Białek, M., Bialobrzeska, O., Blaya-Burgo, M., Bleize, D. N. M., Bø, S., Boecker, L., Boggio, P. S., Borau, S., Borau, S., Bos, B., Bouguettaya, A., Brauer, M., Brik, T., Briker, R., Brosch, T., Buchel, O., Buonauro, D., Butalia, R., Carvacho, H., Chamberlain, S. A. E., Chan, H. Y., Chow, D., Chung, D., Cian, L., Cohen-Eick, N., Contreras-Huerta, L. S., Contu, D., Cristea, V., Cutler, J., D'Ottone, S., De Keersmaecker, J., Delcourt, S., Delouvée, S., Diel, K., Douglas, B. D., Drupp, M. A., Dubey, S., Ekmanis, J., Elbaek, C. T., Elsherif, M., Engelhard, I. M., Escher, Y. A., Etienne, T. W., Farage, L., Farias, A. R., Feuerriegel, S., Findor, A., Freira, L., Friese, M., Gains, N. P., Gallyamova, A., Geiger, S. J., Genschow, O., Gjoneska, B., Gkinopoulos, T., Goldberg, B., Goldenberg, A., Gradidge, S., Grassini, S., Gray, K., Grelle, S., Griffin, S. M., Grigoryan, L., Grigoryan, A., Grigoryev, D., Gruber, J., Guilaran, J., Hadar, B., Hahnel, U. J. J., Halperin, E., Harvey, A. J., Haugestad, C. A. P., Herman, A. M., Hershfield, H. E., Himichi, T., Hine, D. W., Hofmann, W., Howe, L., Huaman-Chulluncuy, E. T., Huang, G., Ishii, T., Ito, A., Jia, F., Jost, J. T., Jovanović, V., Jurgiel, D., Kácha, O., Kankaanpää, R., Kantorowicz, J., Kantorowicz-Reznichenko, E., Mintz, K. K., Kaya, I., Kaya, O., Khachatryan, N., Klas, A., Klein, C., Klöckner, C. A., Koppel, L., Kosachenko, A. I., Kothe, E. J., Krebs, R., Krosch, A. R., Krouwel, A. P. M., Kyrychenko, Y., Lagomarsino, M., Cunningham, J. L., Lees, J., Leung, T. Y., Levy, N., Lockwood, P. L., Longoni, C., Ortega, A. L., Loschelder, D. D., Lu, J. G., Luo, Y., Luomba, J., Lutz, A. E., Majer, J. M., Markowitz, E., Marsh, A. A., Mascarenhas, K. L., Mbilingi, B., Mbungu, W., McHugh, C., Meijers, M. H. C., Mercier, H., Mhagama, F. L., Michalaki, K., Mikus, N., Milliron, S. G., Mitkidis, P., Monge-Rodríguez, F. S., Mora, Y. L., Morais, M. J., Moreau, D., Motoki, K., Moyano, M., Mus, M., Navajas, J., Nguyen, T. L., Nguyen, D. M., Nguyen, T., Niemi, L., Nijssen, S. R. R., Nilsonne, G., Nitschke, J. P., Nockur, L., Okura, R., Öner, S., Özdoğru, A. A., Palumbo, H., Panagopoulos, C., Panasiti, M. S., Pärnamets, P., Paruzel-Czachura, M., Pavlov, Y. G., Payán-Gómez, C., Pearson, A. R., da Costa, L. P., Petrowsky, H. M., Pfattheicher, S., Pham, N. T., Ponizovskiy, V., Pretus, C., Rêgo, G. G., Reimann, R., Rhoads, S. A., Riano-Moreno, J., Richter, I., Röer, J. P., Rosa-Sullivan, J., Ross, R. M., Sabherwal, A., Saito, T., Sarrasin, O., Say, N., Schmid, K., Schmitt, M. T., Schoenegger, P., Scholz, C., Schug, M. G., Schulreich, S., Shreedhar, G., Shuman, E., Sivan, S., Sjåstad, H., Soliman, M., Soud, K., Spampatti, T., Sparkman, G., Spasovski, O., Stanley, S. K., Stern, J. A., Strahm, N., Suko, Y., Sul, S., Syropoulos, S., Taylor, N. C., Tedaldi, E., Tinghög, G., Huynh, L. D. T., Travaglino, G. A., Tsakiris, M., Tüter, İ., Tyrala, M., Uluğ, Ö. M., Urbanek, A., Valko, D., van der Linden, S., van Schie, K., van Stekelenburg, A., Vanags, E., Västfjäll, D., Vesely, S., Vintr, J., Vranka, M., Wanguche, P. O., Willer, R., Wojcik, A. D., Xu, R., Yadav, A., Zawisza, M., Zhao, X., Zhao, J., Żuk, D. & Van Bavel, J. J., 01.10.2024, In: Scientific data. 11, 1, 17 p., 1066.

Research output: Journal contributions › Journal articles › Research › peer-review

DOI