title: Planned missing-data designs in experience-sampling research: Monte Carlo simulations of efficient designs for assessing within-person constructs.
abstract: Experience-sampling research involves trade-offs between the number of questions asked per signal, the number of signals per day, and the number of days. By combining planned missing-data designs and multilevel latent variable modeling, we show how to reduce the items per signal without reducing the number of items. After illustrating different designs using real data, we present two Monte Carlo studies that explored the performance of planned missing-data designs across different within-person and between-person sample sizes and across different patterns of response rates. The missing-data designs yielded unbiased parameter estimates but slightly higher standard errors. With realistic sample sizes, even designs with extensive missingness performed well, so these methods are promising additions to an experience-sampler's toolbox.
authors.affiliation: Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, USA, p_silvia@uncg.edu.
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Silvia
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Kwapil
authors.forename: Molly A
authors.initials: MA
authors.lastname: Walsh
authors.forename: Inez
authors.initials: I
authors.lastname: Myin-Germeys
mesh_terms: D035841:Behavioral Research / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D009010:Monte Carlo Method; D012107:Research Design; D018401:Sample Size