<p>I have been provided datasets from two questionnaires, with the aim of exploring any relationships between the factors measured by one, and factors measured by the other. One questionnaire looks at personality (big 5 type stuff), while the other looks at self-assessed capability (self-efficacy). Note, however, that I am performing exploratory research - I'm looking for correlations, rather than suggesting causation.</p>

<p>The questionnaires each feature approximately 60 questions, which fit within a number of dimensions. I didn't construct the questionnaires, so, as part of the analysis I looked at the correlation coefficients of scores between dimensions within each questionnaire, before looking at the correlations of dimensions between questionnaires (I hope I'm making sense!).</p>

<p>What I've found is that almost all the dimensions within the questionnaire measuring self-efficacy correlate with each other, to a strong degree (more than ±0.5) significant at the 0.01 level.</p>

<p>My experience in questionnaire design and analysis is limited, however, this result seems instinctively bad. Having done some reading around the issue, I have come across "multicollinearity", however, as I am not performing regression (because I'm not looking for causation), I don't think this is the appropriate term. Similarly, I have come across the term "factor loading", but, again, I'm not doing factor analysis here, so I don't think this is applicable.</p>

<p>My, albeit naive, understanding is that either the factors are all measuring the same (or a very similar) thing (an issue with construct validity, maybe?), or the factors are all different, but strongly and significantly related (in which case, is this an issue?)</p>

<p>My overall question is this: Am I correct in this understanding / is there a way (or multiple ways) to explain what's going on here? And, for bonus points, does this issue (if it even is an issue) have a technical term?</p>

<p>As always, many thanks in advance!</p>

CC BY-SA 3.0
2016-03-31T14:39:14.570Z
Maybe this is related to what you're talking about: https://en.wikipedia.org/wiki/Internal_consistency
mrt
CC BY-SA 3.0
2016-03-31T14:54:44.510Z
Internal consistency seems to pertain to situations where you _want_ several items to measure the same variable. I have an almost opposite situation - a number of items measuring what are meant to be discriminant variables, yet all the variables are significantly correlated.
McKenzie
<measurement><reliability>
How to interpret strong, significant correlations between dimensions on a measure?
McKenzie
# How to interpret strong, significant correlations between dimensions on a measure?

(Asked by: username_0 on 2016-03-31)

I have been provided datasets from two questionnaires, with the aim of exploring any relationships between the factors measured by one, and factors measured by the other. One questionnaire looks at personality (big 5 type stuff), while the other looks at self-assessed capability (self-efficacy). Note, however, that I am performing exploratory research - I'm looking for correlations, rather than suggesting causation.

The questionnaires each feature approximately 60 questions, which fit within a number of dimensions. I didn't construct the questionnaires, so, as part of the analysis I looked at the correlation coefficients of scores between dimensions within each questionnaire, before looking at the correlations of dimensions between questionnaires (I hope I'm making sense!).

What I've found is that almost all the dimensions within the questionnaire measuring self-efficacy correlate with each other, to a strong degree (more than ±0.5) significant at the 0.01 level.

My experience in questionnaire design and analysis is limited, however, this result seems instinctively bad. Having done some reading around the issue, I have come across "multicollinearity", however, as I am not performing regression (because I'm not looking for causation), I don't think this is the appropriate term. Similarly, I have come across the term "factor loading", but, again, I'm not doing factor analysis here, so I don't think this is applicable.

My, albeit naive, understanding is that either the factors are all measuring the same (or a very similar) thing (an issue with construct validity, maybe?), or the factors are all different, but strongly and significantly related (in which case, is this an issue?)

My overall question is this: Am I correct in this understanding / is there a way (or multiple ways) to explain what's going on here? And, for bonus points, does this issue (if it even is an issue) have a technical term?

As always, many thanks in advance!

**Tags:** measurement, reliability

> **Comments:**
> **username_1**: Maybe this is related to what you're talking about: https://en.wikipedia.org/wiki/Internal_consistency  
> **username_0**: Internal consistency seems to pertain to situations where you _want_ several items to measure the same variable. I have an almost opposite situation - a number of items measuring what are meant to be discriminant variables, yet all the variables are significantly correlated.  

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## 0 answers

