The Analysis Factor

Statistical Consulting, Resources, and Statistics Workshops for Researchers

Factor Analysis

Factor Analysis: A Short Introduction, Part 5–Dropping unimportant variables from your analysis

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by Maike Rahn, PhD

When are factor loadings not strong enough?

Once you run a factor analysis and think you have some usable results, it’s time to eliminate variables that are not “strong” enough. They are usually the ones with low factor loadings, although additional criteria should be considered before taking out a variable.

As a rule of thumb, your variable should have a rotated factor loading of at least |0.4| (meaning ≥ +.4 or ≤ –.4) onto one of the factors in order to be considered important. [Read more…] about Factor Analysis: A Short Introduction, Part 5–Dropping unimportant variables from your analysis

Factor Analysis: A Short Introduction, Part 1

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by Maike Rahn, PhD

Why use factor analysis?

Factor analysis is a useful tool for investigating variable relationships for complex concepts such as socioeconomic status, dietary patterns, or psychological scales.

It allows researchers to investigate concepts that are not easily measured directly by collapsing a large number of variables into a few interpretable underlying factors.

What is a factor?

The key concept of factor analysis is that multiple observed variables have similar patterns of responses because they are all associated with a latent (i.e. not directly measured) variable. [Read more...] about Factor Analysis: A Short Introduction, Part 1

Confusing Statistical Term #6: Factor

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Factor is confusing much in the same way as hierarchical and beta, because it too has different meanings in different contexts.  Factor might be a little worse, though, because its meanings are related.

In both meanings, a factor is a variable.  But a factor has a completely different meaning and implications for use in two different contexts. [Read more…] about Confusing Statistical Term #6: Factor

Can Likert Scale Data ever be Continuous?

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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 agreement with a statement, such as 1=Strongly Agree to 5=Strongly Disagree. It can be a 1 to 5 scale, 0 to 10, etc.

The issue is that despite being made up of numbers, a Likert scale item is in fact a set of ordered categories.

One camp maintains that as ordered categories, the intervals between the scale values are not equal. Any mean, correlation, or other numerical operation applied to them is invalid. Only nonparametic statistics should be used on Likert scale data (i.e. Jamieson, 2004).

The other camp maintains that while technically the Likert scale item is ordered, using it in parametric tests is valid in some situations. For example, Lubke & Muthen (2004) found that it is possible to find true parameter values in factor analysis with Likert scale data, if assumptions about skewness, number of categories, etc., were met. Likewise, Glass et al. (1972) found that F tests in ANOVA could return accurate p-values on Likert items under certain conditions.

Meanwhile, the debate rages on.

What is a researcher with integrity supposed to do? In the absence of a definitive answer, these are my recommendations:

  1. Understand the difference between a Likert type item and a Likert Scale. A true Likert scale, as Likert defined it, is made up of many items, which all measure the same attitude. But many people use the term Likert Scale to refer to a single item. Confusion about what a Likert Scale is, no doubt, has contributed to the debate.
  2. Proceed with caution. Research the consequences of using your procedure on Likert scale data from your study design. The fact that everyone uses it is not sufficient justification. There are some circumstances and procedures for which it is more egregious than others.
  3. At the very least, insist that the item have at least 5 points (7 is better), that the underlying concept be continuous, and that there be some indication that the intervals between points are approximately equal. Make sure the other assumptions (normality & equal variance of residuals, etc.) be met.
  4. When you can, run the nonparametric equivalent to your test. If you get the same results, you can be confident about your conclusions.
  5. If you do choose to use Likert data in a parametric procedure, make sure you have strong results before making claims. Use a more stringent alpha level, like .01 or even .005, instead of .05. If you have p-values of .001 or .45, it’s pretty clear what the result is, even if parameter estimates are slightly biased. It’s when p-values are close to .05 that the effect of bending assumptions is unclear.
  6. Consider the consequences of reporting inaccurate results. Will anyone ever read your paper? Will your research be published? Will it be used to shape public policy or affect practices? The answers to these questions can inform the seriousness of potential problems.

References:
Carifio, J. & Perla, R. (2007). Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their Antidotes. Journal of Social Sciences, 2, 106-116. http://thescipub.com/PDF/jssp.2007.106.116.pdf

Glass, Peckham, and Sanders (1972). Consequences of failure to meet assumptions underlying the analyses of variance and covariance, Review of Educational Research, 42, 237-288.

Jamieson, S. (2004). Likert scales: how to (ab)use them. Medical Education, 38, 1212-1218.

Lubke, Gitta H.; Muthen, Bengt O. (2004). Applying Multigroup Confirmatory Factor Models for Continuous Outcomes to Likert Scale Data Complicates Meaningful Group Comparisons. Structural Equation Modeling, 11, 514-534.