Abstract
Within-person research has become increasingly popular over recent years in the field of organizational studies for its unique theoretical and methodological advantages for studying dynamic intrapersonal processes (e.g., Dalal et al., Journal of Management 40:1396–1436, 2014; McCormick et al., Journal of Management 46:321–350, 2020). Despite the advancements, there remain serious challenges for many organizational researchers to fully appreciate and appropriately implement within-person research—more specifically, to correctly conceptualize and compute the within-person measurement reliability, as well as navigate key within-person research design factors (e.g., number of measurement occasions, T; number of participants, N; and scale length, I) to optimize within-person reliability. By conducting a comprehensive Monte Carlo simulation with 3240 data conditions, we offer a practical guideline table showing the expected within-person reliability as a function of key design factors. In addition, we provide three easy-to-use, free R Shiny web applications for within-person researchers to conveniently (a) compute expected within-person reliability based on their customized research design, (b) compute observed validity based on the expected reliability and hypothesized within-person validity, and (c) compute observed within-person (as well as between-person) reliability from collected within-person research datasets. We hope these much-needed evidence-based guidelines and practical tools will help enhance within-person research in organizational studies.
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Notes
The R shiny web applications are available through URL https://psychmethods.shinyapps.io/WithinPersonResearch or https://tinyurl.com/LevelAlpha.
Our research team also conducted a separate set of simulations using realistic research design conditions based on the empirical review of 103 unique organizational ESM studies published in 10 representative organizational research journals (e.g., scale length of 2–5 items, 10 vs. 25 measurement occasions, 60 vs. 90 vs. 120 participants), to compare the averaged biases and root mean squared errors (RMSE) of the alpha and omega reliability estimates relative to the true reliabilities. We found that the alpha and omega methods were equally efficacious in terms of having little biases and RMSE in estimating within- and between-person reliabilities for unidimensional scales.
References
Allen, M. J., & Yen, W. M. (1979). Introduction to measurement theory. Waveland Press.
Bakker, A. B., Sanz-Vergel, A. I., Rodríguez-Muñoz, A., & Oerlemans, W. G. M. (2015). The state version of the recovery experience questionnaire: A multilevel confirmatory factor analysis. European Journal of Work and Organizational Psychology, 24(3), 350–359. https://doi.org/10.1080/1359432X.2014.903242
Beal, D. J. (2015). ESM 2.0: State of the art and future potential of experience sampling methods in organizational research. Annual Review of Organizational Psychology and Organizational Behavior, 2(1), 383–407. https://doi.org/10.1146/annurev-orgpsych-032414-111335
Beal, D. J., & Weiss, H. M. (2003). Methods of ecological momentary assessment in organizational research. Organizational Research Methods, 6(4), 440–464. https://doi.org/10.1177/1094428103257361
Bliese, P. D., Chan, D., & Ployhart, R. E. (2007). Multilevel methods: Future directions in measurement, longitudinal analyses, and nonnormal outcomes. Organizational Research Methods, 10(4), 551–563. https://doi.org/10.1177/1094428107301102
Brose, A., Voelkle, M. C., Lövdén, M., Lindenberger, U., & Schmiedek, F. (2015). Differences in the between–person and within–person structures of affect are a matter of degree. European Journal of Personality, 29(1), 55–71. https://doi.org/10.1002/per.1961
Cho, E. (2016). Making reliability reliable: A systematic approach to reliability coefficients. Organizational Research Methods, 19(4), 651–682. https://doi.org/10.1177/1094428116656239
Cortina, J. M., Sheng, Z., Keener, S. K., Keeler, K. R., Grubb, L. K., Schmitt, N., Tonidandel, S., Summerville, K. M., Heggestad, E. D., & Banks, G. C. (2020). From alpha to omega and beyond! A look at the past, present, and (possible) future of psychometric soundness in the Journal of Applied Psychology. Journal of Applied Psychology, 105(12), 1351–1381.
Cranford, J. A., Shrout, P. E., Iida, M., Rafaeli, E., Yip, T., & Bolger, N. (2006). A procedure for evaluating sensitivity to within-person change: Can mood measures in diary studies detect change reliably? Personality and Social Psychology Bulletin, 32(7), 917–929. https://doi.org/10.1177/0146167206287721
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi-org.proxy.lib.pdx.edu/https://doi.org/10.1007/BF02310555
Curran, P. J., & Bauer, D. J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62(1), 583–619. https://doi.org/10.1146/annurev.psych.093008.100356
Dalal, R. S., Bhave, D. P., & Fiset, J. (2014). Within-person variability in job performance. Journal of Management, 40(5), 1396–1436. https://doi.org/10.1177/0149206314532691
DiStefano, C. (2002). The impact of categorization with confirmatory factor analysis. Structural Equation Modeling: A Multidisciplinary Journal, 9(3), 327–346. https://doi.org/10.1207/S15328007SEM0903_2
Ellis, A. M., Bauer, T. N., Mansfield, L. R., Erdogan, B., Truxillo, D. M., & Simon, L. S. (2015). Navigating uncharted waters: Newcomer socialization through the lens of stress theory. Journal of Management, 41(1), 203–235. https://doi.org/10.1177/0149206314557525
Ferrer, E., & McArdle, J. J. (2010). Longitudinal modeling of developmental changes in psychological research. Current Directions in Psychological Science, 19(3), 149–154. https://doi.org/10.1177/0963721410370300
Fisher, C. D., & To, M. L. (2012). Using experience sampling methodology in organizational behavior. Journal of Organizational Behavior, 33(7), 865–877. https://doi.org/10.1002/job.1803
Fisher, G. G., Matthews, R. A., & Gibbons, A. M. (2016). Developing and investigating the use of single-item measures in organizational research. Journal of Occupational Health Psychology, 21(1), 3–23. https://doi.org/10.1037/a0039139
Fleeson, W. (2001). Toward a structure- and process integrated view of personality: Traits as density distributions of states. Journal of Personality and Social Psychology, 80(6), 1011–1027. https://doi.org/10.1037/0022-3514.80.6.1011
Gabriel, A. S., Podsakoff, N. P., Beal, D. J., Scott, B. A., Sonnentag, S., Trougakos, J. P., & Butts, M. M. (2019). Experience sampling methods: A discussion of critical trends and considerations for scholarly advancement. Organizational Research Methods, 22(4), 969–1006. https://doi.org/10.1177/1094428118802626
Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multi-level confirmatory factor analysis framework. Psychological Methods, 19(1), 72–91. https://doi.org/10.1037/a0032138
Huang, P.-H., & Weng, L.-J. (2012). Estimating the reliability of aggregated and within-person centered scores in ecological momentary assessment. Multivariate Behavioral Research, 47(3), 421–441. https://doi.org/10.1080/00273171.2012.673924
Hox, J. (2002). Multi-level analysis: Techniques and applications. Lawrence Erlbaum Associates.
Logg, J. M., & Dorison, C. A. (2021). Pre-registration: Weighing costs and benefits for researchers. Organizational Behavior and Human Decision Processes, 167, 18–27. https://doi.org/10.1016/j.obhdp.2021.05.006
McCormick, B. W., Reeves, C. J., Downes, P. E., Li, N., & Ilies, R. (2020). Scientific contributions of within-person research in management: Making the juice worth the squeeze. Journal of Management, 46(2), 321–350. https://doi.org/10.1177/0149206318788435
McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates Publishers.
Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55 (1), 107–122. https://doi-org.proxy.lib.pdx.edu/https://doi.org/10.1007/BF02294746
Neale, M. C., Hunter, M. D., Pritikin, J. N., Zahery, M., Brick, T. R., Kirkpatrick, R. M., et al. (2016). OpenMx 2.0: Extended structural equation and statistical modeling. Psychometrika, 81, 535–549. https://doi.org/10.1007/s11336-014-9435-8
Nezlek, J. B. (2017). A practical guide to understanding reliability in studies of within-person variability. Journal of Research in Personality, 69, 149–155. https://doi.org/10.1016/j.jrp.2016.06.020
Nguyen, A. N., Yang, L.-Q., Wang, W., & Huang, P.-H. (November 2019). Is your diary “Reliable”?: A review of current measurement practices in ESM research. Paper presented at the Biennnial conference of Conference of Work, Stress, and Health, Philadelphia, PA.
Ohly, S., Sonnentag, S., Niessen, C., & Zapf, D. (2010). Diary studies in organizational research. Journal of Personnel Psychology, 9(2), 79–93. https://doi.org/10.1027/1866-5888/a000009
Ployhart, R. E., & Vandenberg, R. J. (2010). Longitudinal research: The theory, design, and analysis of change. Journal of Management, 36(1), 94–120. https://doi.org/10.1177/0149206309352110
Raudenbush, S. W., Rowan, B., & Kang, S. J. (1991). A multilevel, multivariate model for studying school climate with estimation via the EM algorithm and application to US high-school data. Journal of Educational Statistics, 16(4), 295–330. https://doi.org/10.2307/1165105
R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Accessed 3/8/2020
Reis, H. T., & Gable, S. L. (2000). Event-sampling and other methods for studying everyday experience. In H. T. [Ed Reis & C. M. [Ed Judd (Eds.), Handbook of research methods in social and personality psychology (pp. 190–222, Chapter xii, 558 Pages). Cambridge University Press (New York, NY, US).
Rhemtulla, M., Brosseau-Liard, P. E., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373. https://doi.org/10.1037/a0029315
Robinson, M. D., & Clore, G. L. (2002). Belief and feeling: Evidence for an accessibility model of emotional self-report. Psychological Bulletin, 128(6), 934.
Rogosa, D., Brandt, D., & Zimowski, M. (1982). A growth curve approach to the measurement of change. Psychological Bulletin, 92(3), 726–748. http://dx.doi.org.proxy.lib.pdx.edu/https://doi.org/10.1037/0033-2909.92.3.726
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Miufflin Company.
Shrout, P. E. (1998). Measurement reliability and agreement in psychiatry. Statistical Methods in Medical Research, 7(3), 301–317. https://doi.org/10.1177/096228029800700306
Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15, 72–101. https://doi.org/10.2307/1422689
Tay, L., Woo, S. E., & Vermunt, J. K. (2014). A conceptual and methodological framework for psychometric isomorphism: Validation of multilevel construct measures. Organizational Research Methods, 17(1), 77–106. https://doi.org/10.1177/1094428113517008
Wanous, J. P., & Reichers, A. E. (1996). Estimating the reliability of a single-item measure. Psychological Reports, 78(2), 631–634. https://doi.org/10.2466/pr0.1996.78.2.631
Wänström, L. (2009). Sample sizes for two-group second-order latent growth curve models. Multivariate Behavioral Research, 44(5), 588–619. https://doi.org/10.1080/00273170903202589
Willett, J. B. (1989). Some results on reliability for the longitudinal measurement of change: Implications for the design of studies of individual growth. Educational and Psychological Measurement, 49 (3), 587- 602. https://doi-org.proxy.lib.pdx.edu/https://doi.org/10.1177/001316448904900309
Zapf, D., Dormann, C., & Frese, M. (1996). Longitudinal studies in organizational stress research: A review of the literature with reference to methodological issues. Journal of Occupational Health Psychology, 1(2), 145–169. https://doi.org/10.1037/1076-8998.1.2.145
Acknowledgements
We thank Dr. Jason Newsom for the feedback on the earlier versions of this manuscript. We also thank Stefanie Fox, M.S., for her proofreading of the most recent version of this manuscript.
Funding
This research was supported by the grant T03OH008435 awarded to Portland State University, funded by the Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH, CDC, or HHS. This research is also supported by the National Science Foundation under Grant awarded to Wei Wang (No. 16406229). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do notnecessarily reflect the views of the National Science Foundation.
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Liu-Qin Yang and Wei Wang contributed equally to this article
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Yang, LQ., Wang, W., Huang, PH. et al. Optimizing Measurement Reliability in Within-Person Research: Guidelines for Research Design and R Shiny Web Application Tools. J Bus Psychol 37, 1141–1156 (2022). https://doi.org/10.1007/s10869-022-09803-5
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DOI: https://doi.org/10.1007/s10869-022-09803-5