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I have an experimental study with a list of demographic and related questions and in order to identify data from participants that were potentially just answering the questions at random (to get through them more quickly I would assume), I've included two very similar 7-point likert scale questions at different points in the survey. My assumption would be that since the questions are reflective, the answers participants should give will be at least somewhat similar between the two questions (eg, it should be very unlikely that a participant answers 7 to one question yet 1 to the other).

I haven't yet collected the data, however I would like to have a method for determining which sets of data are suspicious (might be considered for exclusion in analysis) based on these control questions. One method might be to simply determine where the data fit on a Gaussian distribution. However, I think that the limited discriminating power of a 7-point scale would make this an improper test. My other idea was to do a cluster analysis on the data, looking for five groups: three along the line of correlation (between the questions), and two to examine unusually high/low and low/high values. I thought this could provide better suggestions for which data sets might be unusual since it wouldn't use somewhat arbitrary comparisons, it would only use the data given.

I'd really appreciate any suggestions for a better method, or improvements I could make as well as any comments toward more "standard" practices in this area, since I'm somewhat new to research.

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  • $\begingroup$ I don't know what your control questions are, but you might want to consider that questions that (to you) have a similar meaning, might not appear so similar to your subjects. Also, there might be positioning effects (priming) related to preceding questions. You should test your questionaire with attentive subjects in a closely monitored setting and see if the control questions actually score equally. If there is even a slight variance in this test of your test, you should be extremely careful how you interpret a larger variance in a situation you do not monitor closely. $\endgroup$
    – user1196
    Commented Mar 10, 2013 at 16:32

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You seem to be concerned with reliability, and more specifically internal reliability. Internal reliability is the degree to which different questions are measuring the same construct. This concept is used often in psychology and is usually measured using Cronbach's alpha. However, it is typically used to measure the reliability of a test, and not the reliability of an individual.

As Jeromy Anglim points out, I think it's important to consider the goal here. Using a two question Likert scale is probably not good enough to reliably detect outliers: What if the respondent checked all '4s' on a 7-point Likert scale? Reversing the scale would have no effect.

One alternative approach is to employ an instructional manipulation check (Oppenheimer et al., 2009). The gist of the technique is to trap participants into answering a question in a specific way that they could only have done by reading the instructions carefully. Here is an example from a survey administered by Facebook:

enter image description here

While this technique may throw out a few good participants, it will almost certainly raise the signal-to-noise ratio of your data by only including participants who followed instructions and read questions before answering.

Another tried and true technique is to use a computer-administered test and look at reaction times. You may be able to throw out a few responses (or whole participants) by simply looking for outliers in response time that are below the mean.

Oppenheimer, D. M., Meyvis, T., & Davidenko, N. (2009). Instructional manipulation checks: Detecting satisficing to increase statistical power. Journal of Experimental Social Psychology, 45(4), 867-872.

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  • $\begingroup$ "Internal reliability tests the degree to which different questions are measuring the same construct" does not seem quite right. You can have 2 underlying dimensions and have high Cronbach's alpha. See: psycnet.apa.org/journals/pas/8/4/350 $\endgroup$
    – RJ-
    Commented Mar 11, 2013 at 5:14
  • $\begingroup$ @RJ that means Cronbach's alpha might not be measuring internal reliability, not that the definition of internal reliability is wrong. According to the paper you cite, "Internal consistency refers to the interrelatedness of a set of items" which seems in line with what I am saying. $\endgroup$
    – Jeff
    Commented Mar 11, 2013 at 5:47
  • $\begingroup$ I am taking issue mainly with "measuring the same construct". The paper also points out that "measuring the same construct" is different from "inter-relatedness" which is what Cronbach's alpha is measuring. $\endgroup$
    – RJ-
    Commented Mar 11, 2013 at 5:50
  • $\begingroup$ Ah, perhaps I should change the wording to "Internal reliability is the degree to which..." and "Cronbach's alpha tests..." I can see how my definition is not in line with what Cronbach's alpha is testing, but still think it's an accurate description of what internal reliability is. $\endgroup$
    – Jeff
    Commented Mar 11, 2013 at 5:58
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    $\begingroup$ The FB example is rather problematic. The text under "Almost done" does not visually relate to the following two questions, and the meaning of "almost done" does not signal relevant instructions. I would never in my life read it, and it took me a good minute to understand the nature of this example! This would only work if the instructions where placed between the question heading and the question. $\endgroup$
    – user1196
    Commented Mar 11, 2013 at 13:45
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Preventing random responding: An important first step is to think about ways to prevent random responding from occurring in the first place. A few ideas include: administer the survey face to face; have an experimental invigilator present; communicate the importance of the research to participants and the importance of participants taking the research seriously; use financial remuneration.

That said, there are situations where participants do not take a study seriously responding randomly for example. This seems to be particularly an issue when collecting data online.

General approach: My overall approach to this is to develop multiple indicators of problematic participation. I'll then assign penalty points to each participant based on the severity of the indicators. Participants with penalty points above a threshold are excluded from analyses.

The choices of what is problematic depends on the type of study:

  • If a study is performed in a face to face setting, the experimenter can take notes recording when participants engage in problematic behaviour.
  • In online survey style studies I record reaction time for each item. I then see how many items are answered more quickly than the person could conceivably read and respond to the item. For example, answering a personality test item in less than about 600 or even 800 milliseconds indicates that the participant has skipped an item. I then count up the number of times this occurs, and set a cut-off.
  • In performance based tasks, other participant actions may imply distraction or not taking the task seriously. I'll try to develop indicators for this.

Mahalanobis distance is often a useful tool to flag multivariate outliers. You can further inspect the cases with the largest values to think about whether they make sense. There is a bit of an art in deciding which variables to include in the distance calculation. In particular, if you have a mix of positively and negatively worded items, carelessness is often indicated by a lack movement between the poles of a scale as you move from positively to negatively worded items.

In general, I also often include items at the end of the test asking the participant whether they took the experiment seriously.

Discussion in the Literature

Osborne and Blanchard (2010) discuss random responding in the context of multiple choice tests. They mention the strategy of including items that all participants should answer correctly. To quote

These can be content that should not be missed (e.g., 2+2=__), behavioral/attitudinal questions (e.g., I weave the fabric for all my clothes), non-sense items (e.g., there are 30days in February), or targeted multiple-choice test items [e.g., “How do you spell ‘forensics’?” (a) fornsis, (b) forensics, (c) phorensicks, (d) forensix].

References

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  • $\begingroup$ For surveys, how do you use an "invigilator" or "take notes recording when participants engage in problematic behaviour," without violating the anonymity of the participant? $\endgroup$
    – Ryan Lang
    Commented Mar 10, 2013 at 8:51
  • $\begingroup$ @RyanLang Anonymity is preserved as long as there is no identifying information attached to the data. You may well note information about the subject, such as uncommon behavior, to make your data more meaningful. E.g. taking note that a subject appeard intoxicated might help explain their slow reaction times and better help you decide to exclude the data. Consider that usually data is not collected by the same person that evaluates it, and both might be different from the person designing a study. I would go so far as to say that it is a must to note down anything uncommon about a proband. $\endgroup$
    – user1196
    Commented Mar 10, 2013 at 16:39
  • $\begingroup$ What you must usually keep separate from the data are names, addresses, birth dates etc., that are more or less unique to a person. You may even collect these within your data, if they are necessary for your research, but you have to be extremely careful with this information and delete it as soon as it is no longer used. Usually your ethics commission will decide if they allow the collection of this information within your data. (This is German law. The law of other countries will most certainly be different.) $\endgroup$
    – user1196
    Commented Mar 10, 2013 at 16:43
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    $\begingroup$ @Jerome Managing the attitude of subjects towards test taking is an important part in test design. Good practices are: (1) creating interest in the subjects by providing an engaging explanation ("story") and, if possible, a relevant outcome (e.g. display or discuss results that they would like to know); (2) be friendly (this can and needs to be done in online surveys, too); (3) create short tests that don't tire or bore your subjects; (4) make your test visually appealing and easy to "parse"; (5) ask your grandmother, if she understands your questions; (6) don't pay for participation $\endgroup$
    – user1196
    Commented Mar 10, 2013 at 16:54
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This is not directly an answer to your question but, in line with my comments to another answer, my main advice would be “don't worry about it”.

Jeromy Anglim's tips are all good but I am still unconvinced that this is an important issue for most people. Since you are new to research, there are probably dozens of other things you should worry about.

Furthermore, if you do see evidence that there is a problem (extremely short response times, contradictory answers, large number of respondent providing absurd answers to open-ended questions), I would argue that you should first step back and ask yourself if what you are asking is reasonable (Do the task make sense? Can people be expected to have an opinion about the topic you are investigating? Are you demanding too much effort?) rather than trying to sort out “bad” respondents.

If you really want to dig into the subject and look up some literature, another name for this phenomenon is “satisficing”. “Response set” is a related idea that might be of interest.

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  • $\begingroup$ agreed, this should definitely be a first step before "correcting" the "problem" $\endgroup$
    – Jeff
    Commented May 11, 2013 at 21:29

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