Shopping Cart

No products in the cart.

Help Desk: A Quant Methods Blog

How can I estimate statistical power for a structural equation model?

July 6, 2019
This is a question that often arises when using structural equation models in practice, sometimes once a study is completed but more often in the planning phase of a future study. To think about power, we must first consider ways in which we can make errors in hypothesis testing (Cohen, 1992). Briefly, the Type I error rate is the probability...
Keep Reading about How can I estimate statistical power for a structural equation model?

What are modification indices and should I use them when fitting SEMs to my own data?

June 10, 2019
This is a great question and is one that prompts much disagreement among quantitative methodologists. Nearly all confirmatory factor analysis or structural equation models impose some kind of restrictions on the number parameters to be estimated. Usually, some parameters are set to zero (and thus not estimated at all), but sometimes restrictions come in the form of equality constraints or...
Keep Reading about What are modification indices and should I use them when fitting SEMs to my own data?

Do you have any materials that demonstrate how to estimate structural equation models using lavaan in R?

April 25, 2019
This is a question we often hear, particularly from students and junior researchers who don’t have access to expensive commercial software for fitting structural equation models. It is possible to estimate a wide array of SEMs, ranging from simple path models to fully latent SEMs to growth curve models and beyond, using the lavaan package within R. For those who...
Keep Reading about Do you have any materials that demonstrate how to estimate structural equation models using lavaan in R?

How do you choose the best longitudinal data analytic method for testing your research questions?

April 2, 2019
We have worked with statistical models for longitudinal data for more than two decades and this remains a vexing question to us both. There are so many modeling options from which to choose that it is often overwhelming to know which statistical method to use when. This is further complicated by the ongoing refinement of existing models and the development...
Keep Reading about How do you choose the best longitudinal data analytic method for testing your research questions?

What is the difference between a growth model estimated as a multilevel model versus as a structural equation model?

February 19, 2019
This very common question reflects a great deal of unnecessary confusion about how to select a specific analytic approach for modeling longitudinal data. The general term “growth modeling” refers to a variety of statistical methods that allow for the estimation of inter-individual (or between-person) differences in intra-individual (or within-person) change. Often, the function describing within-person change is referred to as...
Keep Reading about What is the difference between a growth model estimated as a multilevel model versus as a structural equation model?

How can I define nonlinear trajectories in a growth curve model?

February 6, 2019
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...
Keep Reading about How can I define nonlinear trajectories in a growth curve model?

Can I estimate an SEM if the sample data are not normally distributed?

January 23, 2019
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...
Keep Reading about Can I estimate an SEM if the sample data are not normally distributed?

How do I know if my structural equation model fits the data well?

January 11, 2019
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”...
Keep Reading about How do I know if my structural equation model fits the data well?

Best Methods for Handling Missing Data in Intensive Longitudinal Designs

April 13, 2018
In nearly every discipline within the behavioral, health, and educational sciences, longitudinal data have become requisite for establishing temporal precedence and distinguishing inter-individual differences in intra-individual change. Whereas traditional longitudinal designs often obtained repeated assessments at monthly or even yearly intervals, recent advances in mobile technology have allowed for the collection of multiple assessments throughout a single day. These so-called...
Keep Reading about Best Methods for Handling Missing Data in Intensive Longitudinal Designs

Syntax for Computing Random Effect Estimates in SPSS

September 27, 2016
Many programs can be used to fit multilevel models. For instance, in our multilevel modeling summer workshop, we demonstrate three programs: SAS, SPSS, and Stata. Unlike SAS, Stata, and many other programs, however, SPSS does not currently offer the option to output estimates of the random effects. Obtaining estimates of the random effects can be useful for a variety of...
Keep Reading about Syntax for Computing Random Effect Estimates in SPSS

CenterStat's Help Desk is a blog in which Dan and Patrick respond to commonly asked questions about a variety of topics behavioral, educational, and health research including experimental design, measurement, data analysis, and interpretation of findings. The responses are intentionally brief and concise, and additional resources are provided such as recommended readings, provision of exemplar data and computer code, or links to other potential learning materials. Readers are welcome to submit their own questions for future Help Desk responses at [email protected].

Livestream Workshops

View Self-Paced Workshops

Free Introduction to Structural Equation Modeling

May 8-10, 2024
Instructors: Dan Bauer & Patrick Curran
Lecture Recordings: Lifetime Access
Evergreen Content: Materials Continually Updated

Latent Curve Modeling

May 22-24, 2024
Instructors: Dan Bauer & Patrick Curran
Lecture Recordings: Lifetime Access
Evergreen Content: Materials Continually Updated

Multilevel Models for Hierarchical Data

May 29-31, 2024
Instructors: Dan Bauer & Patrick Curran
Lecture Recordings: Lifetime Access
Evergreen Content: Materials Continually Updated

Machine Learning for Classification Problems

June 3-5, 2024
Instructor: Doug Steinley
Lecture Recordings: Lifetime Access
Evergreen Content: Materials Continually Updated

Multilevel Models for Longitudinal Data

June 3-6, 2024
Instructors: Dan Bauer & Patrick Curran
Lecture Recordings: Lifetime Access
Evergreen Content: Materials Continually Updated

Introduction to Mixture Modeling and Latent Class Analysis

June 10-11, 2024
Instructor: Dan Bauer
Lecture Recordings: Lifetime Access
Evergreen Content: Materials Continually Updated

Analyzing Intensive Longitudinal Data

June 10-14, 2024
Instructors: JP Laurenceau & Niall Bolger
Lecture Recordings: Lifetime Access

Modern Missing Data Analysis

June 12-14, 2024
Instructor: Craig Enders
Lecture Recordings: Lifetime Access
Evergreen Content: Materials Continually Updated