News and Updates
Growth curve models, whether estimated as a multilevel model (MLM) or a structural equation model (SEM), have become widely used in many areas of behavioral, health, and education sciences. The most common type of growth model defines a linear trajectory in which the time scores defining the slopes increment evenly for equally spaced repeated measures (e.g., values representing time are set to 0, 1, 2, 3, etc.). These values can be modified to allow for unequally spaced time assessments or to place the zero value at the beginning, middle, or end of the series, but the slope of the line always implies an equal change in the outcome per-unit change in time.
However, many constructs we study do not change linearly over time. Instead of equal change per-unit time, there is often differential change with respect to time. So there might be greater change earlier in time that then systematically slows (e.g., reading ability in young children), or the rate of change might increase positively but accelerate with the passage of time (e.g., substance use in adolescence), or the construct might slowly increase, peak, and then slowly decrease (e.g., heavy drinking in young adults). Regardless of particular form, it is critical that an appropriate nonlinear function be incorporated into the growth model to protect against making biased inferences about the nature of change over time. Fortunately, there are many options available to capture nonlinear change over time in growth models.Read More
Continuous distributions are typically described by their mean (central tendency), variance (spread), skew (asymmetry), and kurtosis (thickness of tails). A normal distribution assumes a skew and kurtosis of zero, but truly normal distributions are rare in practice. Unfortunately, the fitting of standard SEMs to non-normal data can result in inflated model test statistics (leading models to be rejected more often than they should) and under-estimated standard errors (leading tests of individual parameters to be accepted more often then they should be). There are a number of important issues that must be considered when addressing this in practice.Read More
This is one of the most common questions we receive and, unfortunately, there are no quick answers. However, there are some initial guidelines that can be followed when assessing the fit of an SEM. For most SEMs, the goal of the analysis is to define a model that results in predicted values of the summary statistics (sometimes called “moment structures” consisting of the variances, covariances, and means of the observed variables) that are as close as possible to the values that were observed in the sample. SEMs that result in a closer correspondence between the predicted and the observed summary statistics obtain a better fit compared to models that do not.Read More
At Curran-Bauer Analytics, we have long been committed to providing broad access to high-quality training opportunities for students in the social, behavioral and health sciences. We are thus very excited to announce a new three-day workshop, Introduction to Multilevel Modeling for Graduate Students, to be held in Chapel Hill, NC on May 29-31, 2019, at steeply reduced cost exclusively for students actively enrolled in a graduate or professional degree granting academic program. Students pay only a $100 processing fee to attend. Enrollment is limited to 35 individuals and participants will be selected by random lottery for admission if applications exceed this number. Pre-register by March 15th to enter the admission lottery. Click the link above for details and to pre-register.Read More
We’re pleased to announce our summer workshop schedule for 2019:
- May 13-17: Structural Equation Modeling
- May 20-24: Longitudinal Structural Equation Modeling
- June 3-7: Network Analysis
- June 10-14: Latent Class/Cluster Analysis and Mixture Modeling
- June 24-28: Multilevel Modeling
New this year: We have expanded Network Analysis from three to five days to allow more in-depth coverage, we now provide live demonstrations in R for Structural Equation Modeling and Longitudinal Structural Equation Modeling, and we have expanded our R demonstrations for Latent Class/Cluster Analysis and Mixture Modeling.
See our Training page for a general description of our teaching philosophy, links to course reviews and sample course notes.Read More