That is not necessarily a problem, depending on your goals. If the factor makes sense, two items may be enough. One issue may be that the "scale" consisting of only two items may not have a very high reliability (due to the scale being so short).

Hi,

I would tend to twist Christan's sentence "if the factor make sense, two items may be enough" into "if the two items make sense (i.e., are valid reflections of the same underlying phenomenon), then using two items is enough" :)

For statistical identification of the error variances, one loading and the latent variance, however, the latent should be related (via structural effects or covariances) with other latent or observed variables.

All the best,

Holger

Dear Enrique López Ramírez,

When conducting a factor analysis, having a factor with only two items can pose some challenges. Typically, it is recommended to have at least three items per factor to ensure reliability and stability of the factor.

Having only two items in a factor can make it difficult to assess the factor's internal consistency and interpretability. With a limited number of items, the factor may not adequately capture the underlying construct it is intended to measure.

In such cases, you have a few options to consider:

1. Explore alternative measurement options: If possible, you could try to identify or develop additional items that conceptually align with the factor and include them in your analysis. This would help increase the reliability and validity of the factor. However, be cautious and ensure that the additional items are appropriate and relevant to the construct you are measuring.

2. Merge factors: If the factor with only two items seems conceptually similar to another factor, you could consider combining them into a single factor. This can help consolidate the factor structure and improve interpretability. However, it is essential to ensure that the combined factor still captures a coherent construct and maintains internal consistency.

3. Consider the context and theoretical basis: Evaluate the significance and relevance of the factor with two items in the specific context of your study. If it aligns with the theoretical framework and is essential to your research objectives, you may choose to retain it. However, make sure to acknowledge the limitations associated with the small number of items and exercise caution when interpreting the results.

4. Assess reliability and validity: Even with only two items, you can still assess the internal consistency of the factor using measures like Cronbach's alpha or other reliability coefficients. Additionally, examine the factor loadings and assess whether they are significant and meet acceptable thresholds.

Ultimately, the decision on how to handle a factor with only two items should be based on careful consideration of the specific circumstances, the nature of the construct being measured, and the goals of your analysis. It is always advisable to consult with domain experts or experienced researchers in your field to obtain additional insights and guidance tailored to your study.

Dear Govindasamy Karuppasamy

with all the respect, I would like to respond to some things you said.

1) What do you mean with the factor having more reliability and stability? Reliability is of concern in observed variables which contain random error. A latent variable may have some timely stability but I guess that is not what you mean. Perhaps you mean the model stability and convergence problems but I would assume that having two valid and substantial indicators of a latent factor will not have this problem.

I just did a small Monte Carlo Simulation with either two strong indicators (with loadings of .8) and the same model with four strong indicators). The latent variable was supposed to affect another latent variable (with in both conditions having four strong indicators): The result showed no differences in convergence rates, bias of the latent effect or its standard deviations across 200 repetetions.

Perhaps I miss something but I would still stress that having two substantial and valid indicators is better than having 3, 4 or even more that in most cases are a misspecification. The reason for the latter is simply that I doubt that you can generate 4 indicators that truly measure the same latent variable.

2) you further say:

"Having only two items in a factor can make it difficult to assess the factor's internal consistency and interpretability. With a limited number of items, the factor may not adequately capture the underlying construct it is intended to measure."

This confuses the meaning of a latent variable (or one of its forms, the common factor) with formative constructs, or simply multidimensional constructs. A latent or factor is singular/one-dimensional representation of a phenomenon. Thus, it has no internal consistency or something you have to "capture". If the phenonmenon has several dimensions, sides, facets etc., each of them is a factor itself. This may be an error to capture the breadth of the phenomenon (or construct if you like) by the adequate NUMBER of factors but not imply any internal complexity of the factor (as there is none).

3) With regard to your recommmendation #1. No disagreement here. The problem is this does not help her in the current state.

4) With regard to #2: If the 2 items measure latent A and 2 other items measure latent B *in reality* than "combining them" is a simple misspecifcation which hopefully will detected by the model test.

5) With regard to #4 again this strange notion of a factor's internal consistency. The *indicators* have to have internal consistency as this is a strict implictation if a) both indicators measure the same latent and b) there is no substantial measurement error. Both assumptions are called "essential tau-equivalence" and are a requirement of Cronbach's alpha to work properly as a means to estimate the *indicator's* reliability. And yes, if both indicators are valid and strong (on which all of my talk your rests), than she should check that, however far more important is the model as this is a prerequisite of the whole estimation of internal consistency as a means to evalute the reliability.

I hope you don't misinterprete my intention beyond all of that. These issues come around again and again (especially on the "breadth of factors") and create a lot of confusion.

All the best,

Holger

Thank you very much for your answer! Holger Steinmetz Govindasamy Karuppasamy Christian Geiser

See also my Youtube video on this topic:

https://youtu.be/KpkRdpTacEk