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sample size

Member Training: Model Building Approaches

by TAF Support

There is a bit of art and experience to model building. You need to build a model to answer your research question but how do you build a statistical model when there are no instructions in the box? 

Should you start with all your predictors or look at each one separately? Do you always take out non-significant variables and do you always leave in significant ones?

[Read more…] about Member Training: Model Building Approaches

Tagged With: centering, interaction, lasso, Missing Data, Model Building, Model Fit, Multicollinearity, overfitting, Research Question, sample size, specification error, statistical model, Stepwise

Related Posts

  • What Is Specification Error in Statistical Models?
  • Member Training: The LASSO Regression Model
  • Steps to Take When Your Regression (or Other Statistical) Results Just Look…Wrong
  • Overfitting in Regression Models

Member Training: Power Analysis and Sample Size Determination Using Simulation

by guest contributer Leave a Comment

This webinar will show you strategies and steps for using simulations to estimate sample size and power. You will learn:
  • A review of basic concepts of statistical power and effect size
  • A simulation-based approach to power analysis
  • An overview of how to implement simulations in various popular software programs.
[Read more…] about Member Training: Power Analysis and Sample Size Determination Using Simulation

Tagged With: ANOVA, effect size, mediation, mixed model, Path Analysis, Power Analysis, quantitative research, sample size, simulation

Related Posts

  • Member Training: A Gentle Introduction to Bootstrapping
  • Member Training: Matrix Algebra for Data Analysts: A Primer
  • Member Training: Generalized Linear Models
  • Member Training: The Fundamentals of Sample Size Calculations

Four Common Misconceptions in Exploratory Factor Analysis

by guest contributer Leave a Comment

by Christos Giannoulis, PhD

Today, I would like to briefly describe four misconceptions that I feel are commonly perceived by novice researchers in Exploratory Factor Analysis:

Misconception 1: The choice between component and common factor extraction procedures is not so important.

In Principal Component Analysis, a set of variables is transformed into a smaller set of linear composites known as components. This method of analysis is essentially a method for data reduction.

[Read more…] about Four Common Misconceptions in Exploratory Factor Analysis

Tagged With: common factor analysis, communality, EFA, eigenvalue, Exploratory Factor Analysis, oblique rotation, orthogonal rotation, PCA, principal axis factor analysis, principal component analysis, rotation, sample size, simple structure

Related Posts

  • In Factor Analysis, How Do We Decide Whether to Have Rotated or Unrotated Factors?
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  • How To Calculate an Index Score from a Factor Analysis

Member Training: The Fundamentals of Sample Size Calculations

by Karen Grace-Martin 3 Comments

Sample size estimates are one of those data analysis tasks that look straightforward, but once you try to do one, make you want to bang your head against the computer in frustration. Or, maybe that’s just me.

Regardless of how they make you feel, they are super important to do for your study before you collect the data.

[Read more…] about Member Training: The Fundamentals of Sample Size Calculations

Tagged With: accuracy, data collection, effect size, sample size, sample size estimates, scientific interest, testing

Related Posts

  • Member Training: Power Analysis and Sample Size Determination Using Simulation
  • Member Training: Statistical Rules of Thumb: Essential Practices or Urban Myths?
  • Member Training: A Gentle Introduction to Bootstrapping
  • Member Training: Interpretation of Effect Size Statistics

Member Training: Statistical Rules of Thumb: Essential Practices or Urban Myths?

by Karen Grace-Martin Leave a Comment

There are many rules of thumb in statistical analysis that make decision making and understanding results much easier.

Have you ever stopped to wonder where these rules came from, let alone if there is any scientific basis for them? Is there logic behind these rules, or is it propagation of urban legends?

In this webinar, we’ll explore and question the origins, justifications, and some of the most common rules of thumb in statistical analysis, like:

[Read more…] about Member Training: Statistical Rules of Thumb: Essential Practices or Urban Myths?

Tagged With: effect size, reliability, results, rules of thumb, sample size, Statistical analysis

Related Posts

  • Member Training: Interpretation of Effect Size Statistics
  • Member Training: Determining Levels of Measurement: What Lies Beneath the Surface
  • Member Training: Power Analysis and Sample Size Determination Using Simulation
  • Member Training: The Fundamentals of Sample Size Calculations

Five things you need to know before learning Structural Equation Modeling

by guest contributer 1 Comment

By Manolo Romero Escobar

If you already know the principles of general linear modeling (GLM) you are on the right path to understand Structural Equation Modeling (SEM).

As you could see from my previous post, SEM offers the flexibility of adding paths between predictors in a way that would take you several GLM models and still leave you with unanswered questions.

It also helps you use latent variables (as you will see in future posts).

GLM is just one of the pieces of the puzzle to fit SEM to your data. You also need to have an understanding of:
[Read more…] about Five things you need to know before learning Structural Equation Modeling

Tagged With: Missing Values, normality assumption, outliers, sample size, SEM, Structural Equation Modeling

Related Posts

  • The Four Models You Meet in Structural Equation Modeling
  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
  • Structural Equation Modeling: What is a Latent Variable?

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