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Factor Analysis

Can Likert Scale Data ever be Continuous?

by Karen Grace-Martin  51 Comments

A very common question is whether it is legitimate to use Likert scale data in parametric statistical procedures that require interval data, such as Linear Regression, ANOVA, and Factor Analysis.

A typical Likert scale item has 5 to 11 points that indicate the degree of something. For example, it could measure agreement with a statement, such as 1=Strongly Disagree to 5=Strongly Agree. It can be a 1 to 5 scale, 0 to 10, etc. [Read more…] about Can Likert Scale Data ever be Continuous?

Tagged With: ANOVA, continuous variable, Factor Analysis, Likert Scale, linear regression, Model Assumptions, Nonparametric statistics

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Correlated Errors in Confirmatory Factor Analysis

by Jeff Meyer  3 Comments

Latent constructs, such as liberalism or conservatism, are theoretical and cannot be measured directly.

But we can represent the latent construct by combining a set of questions on a scale, called indicators. We do this via factor analysis.

Often prior research has determined which indicators represent the latent construct. Prudent researchers will run a confirmatory factor analysis (CFA) to ensure the same indicators work in their sample.

[Read more…] about Correlated Errors in Confirmatory Factor Analysis

Tagged With: Confirmatory Factor Analysis, error term, Factor Analysis, latent variable, Model Fit

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  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
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Member Training: Matrix Algebra for Data Analysts: A Primer

by Karen Grace-Martin  4 Comments

If you’ve been doing data analysis for very long, you’ve certainly come across terms, concepts, and processes of matrix algebra.  Not just matrices, but:

  • Matrix addition and multiplication
  • Traces and determinants
  • Eigenvalues and Eigenvectors
  • Inverting and transposing
  • Positive and negative definite

[Read more…] about Member Training: Matrix Algebra for Data Analysts: A Primer

Tagged With: Factor Analysis, linear, matrix, mixed model, multivariate analysis

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Measurement Invariance and Multiple Group Analysis

by Jeff Meyer  2 Comments

Creating a quality scale for a latent construct (a variable that cannot be directly measured with one variable) takes many steps. Structural Equation Modeling is set up well for this task.

One important step in creating scales is making sure the scale measures the latent construct equally well and the same way for different groups of individuals.

[Read more…] about Measurement Invariance and Multiple Group Analysis

Tagged With: Confirmatory Factor Analysis, Exploratory Factor Analysis, Factor Analysis, Structural Equation Modeling

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How Big of a Sample Size do you need for Factor Analysis?

by Karen Grace-Martin  2 Comments

Most of the time when we plan a sample size for a data set, it’s based on obtaining reasonable statistical power for a key analysis of that data set. These power calculations figure out how big a sample you need so that a certain width of a confidence interval or p-value will coincide with a scientifically meaningful effect size.

But that’s not the only issue in sample size, and not every statistical analysis uses p-values.

[Read more…] about How Big of a Sample Size do you need for Factor Analysis?

Tagged With: Factor Analysis, p-value, rules of thumb, sample size

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Why Adding Values on a Scale Can Lead to Measurement Error

by Jeff Meyer  4 Comments

Whenever you use a multi-item scale to measure a construct, a key step is to create a score for each subject in the data set.

This score is an estimate of the value of the latent construct (factor) the scale is measuring for each subject.  In fact, calculating this score is the final step of running a Confirmatory Factor Analysis.

[Read more…] about Why Adding Values on a Scale Can Lead to Measurement Error

Tagged With: Confirmatory Factor Analysis, Exploratory Factor Analysis, Factor Analysis, Structural Equation Modeling

Related Posts

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  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
  • Member Training: Confirmatory Factor Analysis

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