Showing papers by "Joop J. Hox published in 2014"

Multilevel and SEM Approaches to Growth Curve Modeling

Abstract: Growth curve models are used to model development explicitly as a function of time. Two techniques are commonly used to construct and analyze growth curve models: structural equation modeling, with distinct measurement occasions represented by separate variables, and multilevel regression modeling, with different measurement occasions represented as nested within individuals. Both approaches are essentially the same, but they differ in the way they can be extended into models that are more complex. An example is given, and estimation methods and software are briefly discussed. Keywords: growth curve modeling; longitudinal data; multilevel model; structural equation model

Analyzing indirect effects in cluster randomized trials. The effect of estimation method, number of groups and group sizes on accuracy and power.

Abstract: Cluster randomized trials assess the effect of an intervention that is carried out at the group or cluster level. Ajzen’s theory of planned behaviour is often used to model the effect of the intervention as an indirect effect mediated in turn by attitude, norms and behavioural intention. Structural equation modelling (SEM) is the technique of choice to estimate indirect effects and their significance. However, this is a large sample technique, and its application in a cluster randomized trial assumes a relatively large number of clusters. In practice, the number of clusters in these studies tends to be relatively small, e.g. much less than fifty. This study uses simulation methods to find the lowest number of clusters needed when multilevel SEM is used to estimate the indirect effect. Maximum likelihood estimation is compared to Bayesian analysis, with the central quality criteria being accuracy of the point estimate and the confidence interval. We also investigate the power of the test for the indirect effect. We conclude that Bayes estimation works well with much smaller cluster level sample sizes such as 20 cases than maximum likelihood estimation; although the bias is larger the coverage is much better. When only 5 to 10 clusters are available per treatment condition even with Bayesian estimation problems occur.

A Meta-Analysis of Experiments on the Effectiveness of Incentives in Organizational Surveys

Abstract: The use of monetary and non-monetary incentives for increasing response is considered a proven and widely used method in surveys of individuals or households. This applies not only to mail, but also to face-to-face and telephone surveys. Experimental research shows that the technique is also effectively used in non-official surveys of organizational populations, not only to increase unit response rates but also to improve response completeness, response quality and speed and even attitude towards the survey sponsor, without negative influence on bias. Nevertheless the use of incentives is typically not applied in official business surveys. We report the meta-analysis of 34 experimental studies that implemented a monetary or non-monetary incentive in order to increase response rates in an establishment survey. The included studies comprise a variety of survey methods, sample frames, survey topics, research organizations, population types, industries, respondent types, countries, data types, and both voluntary and mandatory surveys. We report the mean effect size of the use of incentives and the mediating effects of the study features based on the meta-analytic method of inverse variance weighted regression using random effects maximum likelihood estimation.