• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
The Analysis Factor

The Analysis Factor

Statistical Consulting, Resources, and Statistics Workshops for Researchers

  • our programs
    • Membership
    • Online Workshops
    • Free Webinars
    • Consulting Services
  • statistical resources
  • blog
  • about
    • Our Team
    • Our Core Values
    • Our Privacy Policy
    • Employment
    • Collaborate with Us
  • contact
  • login

fixed effect

Multilevel, Hierarchical, and Mixed Models–Questions about Terminology

by Karen Grace-Martin  Leave a Comment

Multilevel models and Mixed Models are generally the same thing. In our recent webinar on the basics of mixed models, Random Intercept and Random Slope Models, we had a number of questions about terminology that I’m going to answer here.

If you want to see the full recording of the webinar, get it here. It’s free.

Q: Is this different from multi-level modeling?

A: No. I don’t really know the history of why we have the different names, but the difference in multilevel modeling [Read more…] about Multilevel, Hierarchical, and Mixed Models–Questions about Terminology

Tagged With: fixed effect, Fixed Factor, hierarchical linear model, mixed model, multilevel model, panel data, random effect, Random Factor

Related Posts

  • Specifying Fixed and Random Factors in Mixed Models
  • Confusing Statistical Term #10: Mixed and Multilevel Models
  • The Difference Between Random Factors and Random Effects
  • Mixed Models: Can you specify a predictor as both fixed and random?

Member Training: Meta-analysis

by guest contributer  Leave a Comment

Meta-analysis is the quantitative pooling of data from multiple studies. Meta-analysis done well has many strengths, including statistical power, precision in effect size estimates, and providing a summary of individual studies.

But not all meta-analyses are done well. The three threats to the validity of a meta-analytic finding are heterogeneity of study results, publication bias, and poor individual study quality.

[Read more…] about Member Training: Meta-analysis

Tagged With: effect size, fixed effect, meta-analysis, PRISMA guidelines, random effect

Related Posts

  • Member Training: Heterogeneity in Meta-analysis
  • Member Training: Power Analysis and Sample Size Determination Using Simulation
  • Member Training: Translating Between Multilevel and Mixed Models
  • Member Training: Analyzing Likert Scale Data

What Are Nested Models?

by Karen Grace-Martin  Leave a Comment

Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation.

This includes favorites like:

  • All Generalized Linear Models, including logistic, probit, beta, Poisson, negative binomial regression
  • Linear Mixed Models
  • Generalized Linear Mixed Models
  • Parametric Survival Analysis models, like Weibull models
  • Structural Equation Models

That’s a lot of models.

If you’ve ever learned any of these, you’ve heard that some of the statistics that compare model fit in competing models require [Read more…] about What Are Nested Models?

Tagged With: covariance parameters, deviance, fixed effect, likelihood ratio test, linear mixed model, linear model, Model Building, nested models

Related Posts

  • Member Training: Goodness of Fit Statistics
  • Mixed Models: Can you specify a predictor as both fixed and random?
  • Member Training: Hierarchical Regressions
  • Member Training: Types of Regression Models and When to Use Them

Mixed Models: Can you specify a predictor as both fixed and random?

by Karen Grace-Martin  20 Comments

One of the most confusing things about mixed models arises from the way it’s coded in most statistical software.  Of the ones I’ve used, only HLM sets it up differently and so this doesn’t apply.

But for the rest of them—SPSS, SAS, R’s lme and lmer, and Stata, the basic syntax requires the same pieces of information.

1.       The dependent variable

2.       The predictor variables for which to calculate fixed effects and whether those [Read more…] about Mixed Models: Can you specify a predictor as both fixed and random?

Tagged With: fixed effect, linear mixed model, random effect, Random Factor, Repeated Measures

Related Posts

  • Multilevel, Hierarchical, and Mixed Models–Questions about Terminology
  • Statistical Software Access From Home
  • Member Training: What’s the Best Statistical Package for You?
  • The Difference Between Random Factors and Random Effects

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Moderated Mediation, Not Mediated Moderation

Upcoming Workshops

    No Events

Upcoming Free Webinars

TBA

Quick links

Our Programs Statistical Resources Blog/News About Contact Log in

Contact

Upcoming

Free Webinars Membership Trainings Workshops

Privacy Policy

Search

Copyright © 2008–2023 The Analysis Factor, LLC.
All rights reserved.

The Analysis Factor uses cookies to ensure that we give you the best experience of our website. If you continue we assume that you consent to receive cookies on all websites from The Analysis Factor.
Continue Privacy Policy
Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Non-necessary
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
SAVE & ACCEPT