Planned missing-data designs in analysis of change.

Graham, John W.; Taylor, Bonnie; Cumsille, Patricio

doi:10.1037/10409-011

Cited by 105 publications

(119 citation statements)

References 18 publications

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“…Both Graham et al (2001) and Mistler and Enders (2012) only examined power for a single parameter in latent growth models. In the present study we investigated the efficiency of a range of other model parameters in latent growth curves, including factor loadings, means and variances of the latent intercepts and slopes, and covariances between slopes.…”

Section: Wave-level Planned Missingnessmentioning

confidence: 99%

“…An additional benefit of this kind of design is that it can drastically reduce the cost of data collection compared to a design where all participants are measured at each time point. Graham et al (2001) compared the cost efficiency of wave-level missingness designs to those with complete data at every wave, holding the total number of data points collected equal (i.e., they compared missing data designs to complete data designs with a smaller sample size).…”

Section: Wave-level Planned Missingnessmentioning

confidence: 99%

“…Planned missingness can be applied to many complex models resulting in no added bias and minimal power loss. In the present paper, we apply both 3-form planned missingness, where participants are assigned to miss a subset of items at every time point (Hansen et al, 1988;Graham, Taylor, Olchowski, & Cumsille, 2006;Graham, Hofer, & MacKinnon, 1996;Graham, Hofer, & Piccinin, 1994) and wave missingness, where participants are assigned to miss a subset of measurement occasions (Graham, Taylor, & Cumsille, 2001) to simulated latent growth curve data. We model latent growth curves of two constructs, allowing the intercepts and slopes to covary.…”

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confidence: 99%

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Planned missing designs to optimize the efficiency of latent growth parameter estimates

Rhemtulla

Fan

et al. 2014

International Journal of Behavioral Development

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Planned missing designs to optimize the efficiency of latent growth parameter estimates Rhemtulla, M.; Jia, F.; Wu, W.; Little, T.D. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. AbstractWe examine the performance of planned missing (PM) designs for correlated latent growth curve models. Using simulated data from a model where latent growth curves are fitted to two constructs over five time points, we apply three kinds of planned missingness. The first is item-level planned missingness using a 3-form design at each wave such that 25% of data are missing. The second is wave-missingness such that each participant is missing up to 2 waves of data. The third combines both forms of missingness. We find that 3-form missingness results in high convergence rates, little parameter estimate or standard error bias, and high efficiency relative to the complete data design for almost all parameter types. In contrast, wave missingness and the combined design result in dramatically lowered efficiency for parameters measuring individual variability in rates of change (e.g., latent slope variances and covariances), and bias in both estimates and standard errors for these same parameters. We conclude that wave missingness should not be used except with large effect sizes and very large N.Keywords: Planned Missing Designs, Latent Growth Curves, Three-Form Design, Wave Missingness, Longitudinal Planned Missingness. GROWTH CURVE PM DESIGNS 3Planned missing data designs allow researchers to reduce the testing burden on participants, leading to higher-quality data with less unplanned missingness and smaller fatigue and practice effects (Harel, Stratton, & Aseltine, 2012). Planned missingness can be applied to many complex models resulting in no added bias and minimal power loss. In the present paper, we apply both 3-form planned missingness, where participants are assigned to miss a subset of items at every time point (Hansen et al., 1988;Graham, Taylor, Olchowski, & Cumsille, 2006;Graham, Hofer, & MacKinnon, 1996;Graham, Hofer, & Piccinin, 1994) and wave missingness, where participants are assigned to miss a subset of measurement occasions (Graham, Taylor, & Cumsille, 2001) to simulated latent growth curve data. We model latent growth curves of two constructs, allowing the interce...

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Section: Wave-level Planned Missingnessmentioning

confidence: 99%

Section: Wave-level Planned Missingnessmentioning

confidence: 99%

mentioning

confidence: 99%

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Planned missing designs to optimize the efficiency of latent growth parameter estimates

Rhemtulla

Fan

et al. 2014

International Journal of Behavioral Development

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“…Prior disadvantages of the SEM approach regarding the presence of missing data have long been overcome by new developments (Enders & Bandalos, 2001;Graham, Taylor, & Cumsille, 2001;Wothke, 2000;Yuan & Bentler, 2000), which indirectly allows SEM to treat data with individually varying observation times.…”

Section: Estimation and Testingmentioning

confidence: 99%

Predicting Random Level and Seasonality of Hotel Prices: A Latent Growth Curve Approach

Coenders¹,

Espinet²,

Sáez³

2003

Tourism Analysis

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This article examines the effect on price of different characteristics of holiday hotels in the sun-andbeach segment, under the hedonic function perspective. Monthly prices of the majority of hotels in the five major tourist regions in the Spanish continental Mediterranean coast were gathered from May to October 1999, from the tour operator catalogues. Hedonic functions are specified as random-effect models and parameterized as structural equation models with two latent variables, a random peak season price and a random width of seasonal fluctuations. This model can be considered to be a latent growth curve model applied to seasonality rather than growth. Characteristics of the hotels were used as predictors of both latent variables in models fitted for each region separately. Hotel category, region, distance to the beach, availability of parking place, and room equipment have an effect on both peak price and seasonality. Three-star hotels have the highest seasonality and hotels located in the southern regions the lowest. This can be explained by a warmer climate in autumn in the southern regions. The model is fitted to the pooled data of all regions and expanded to include the effect of climate on prices. Hedonic functionsHotel pricing Seasonality Latent growth curve models Random-effect models the country such as culture, history, gastronomy, landscape, and monuments.The relevant attributes of holiday hotels include, among others, category, services and facilities available to guests (e.g. swimming pool, garden, television in the room), attributes of the region in which they are located and, given the particular segment aimed at, likely climate.The aim of this article is to study the effect on prices of the different characteristics or attributes of a holiday hotel in the sun-and-beach segment in Spain. This country constitutes the second tourist destination worldwide (Departament d'Indústria, 1999) and nowadays attracts mostly sun-seeking tourists, though the latest policies of both the industry and the government tend to foster other assets of

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“…Planned Missing Protocols Two of the most effective planned missing protocols are the multiform design and the two-method design (Graham et al 2001(Graham et al , 2006Mistler and Enders 2012). The multiform design derives its name from the fact that multiple questionnaire protocols are created.…”

Section: Planned Missing Data Designsmentioning

confidence: 99%

Core Principles of Life Course Health Development Methodology and Analytics

Little

2017

Handbook of Life Course Health Development

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The complexity of life course health development research demands rigorous methodology and analytics. Advanced techniques, improved estimation algorithms, and user-accessible software/hardware developments signal what I see as a paradigm shift in how life science research will be conducted now and in the future. Although techniques such as structural equation modeling (SEM), multilevel modeling (MLM), and mixture distribution modeling have permeated the research agenda in many fields, the capability of these tools has hardly been fully utilized. The craft of life course health development methodology and analytics requires dedication, sophistication, and a knack for molding the analysis tool to the research question at hand. For life course health development research to be at its most effective, utilizing the expertise of collaborative teams is essential. The days of one person being both expert theoretician and methodologist are waning. Partnering with dedicated experts on the various issues that I have outlined here will bring the needed sophistication and knack to execute research at its finest and most impactful levels. In this chapter, I highlight many of the issues that life course health development researchers will need to address and practices to adopt.

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Planned missing-data designs in analysis of change.

Cited by 105 publications

References 18 publications

Planned missing designs to optimize the efficiency of latent growth parameter estimates

Planned missing designs to optimize the efficiency of latent growth parameter estimates

Predicting Random Level and Seasonality of Hotel Prices: A Latent Growth Curve Approach

Core Principles of Life Course Health Development Methodology and Analytics

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