A Measure of the Influence of Demand Characteristics

Rubin, Paolini, and Crisp’s (2010) Perceived Awareness of the Research Hypothesis (PARH) scale is a quick and convenient quantitative method for measuring the potential influence of demand characteristics in psychology research situations. It can help to refute the idea that research results are due to demand characteristics in the research situation.

Demand characteristics are "the totality of cues which convey an experimental hypothesis to the subject" (Orne, 1962, p. 779). If participants become aware of the research hypotheses, then they may respond in a way that they believe will confirm the hypotheses in order to be "good" participants and not "ruin" the research (Orne, 1962; for a review, see
Strohmetz, 2008). These unnatural responses compromise the ecological validity of the research. To address the extent to which this may be a problem in their research, researchers may ask participants to complete Rubin et al.’s (2010) PARH scale near the end of their research session.

The PARH Scale

Your Thoughts About the Research

Please indicate how much you agree or disagree with each of the following statements:

1. I knew what the researchers were investigating in this research.
2. I wasn’t sure what the researchers were trying to demonstrate in this research.
3. I had a good idea about what the hypotheses were in this research.
4. I was unclear about exactly what the researchers were aiming to prove in this research.

  • Responses are made on a 7-point Likert-type scale (1 = strongly disagree,7 = strongly agree).
  • Items 2 and 4 are reverse scored during coding.
  • Rubin et al. (2010) found that the PARH scale has good internal consistency (αs = .77 & .81).


Indirect postexperimental feedback questions require research participants to describe the research hypotheses in response to open-ended prompts such as "What did you think this research was all about?" The problem with this indirect approach is that participants may be unclear about exactly what is being asked and, consequently, they may provide vague and uninformative answers (Page, 1973). In contrast, more direct questions about the research hypothesis have the potential to reveal the hypothesis to participants and result in false positive responses: Participants may indicate that they were aware of the hypothesis when, in fact, that awareness was only present after they read the feedback questions (e.g., Berkowitz, 1971). The PARH scale strikes a balance between these two concerns: It asks clear and direct questions about participants' awareness of the research hypotheses, but it does so without specifying the hypothesis.

he PARH scale is also innovative in using closed-ended items and a quantitative response format. This approach has three advantages:
  1. It is quicker and easier for participants to respond to closed-ended items that assess their understanding of the research hypothesis than it is for them to describe the actual hypotheses in an open-ended response format. Consequently, there is likely to be less missing data using the PARH approach.
  2. It is quicker and easier for researchers to collate, analyse, and interpret the PARH scale's closed-ended, quantitative data than it is for them to analyse and interpret the textual responses of the open-ended, qualitative method.
  3. The PARH scale taps participants' implicit and intuitive understanding of the research hypotheses. Consequently, it provides a more subtle and sensitive measure of the awareness of research hypotheses than approaches that require participants to provide an explicit description of the research hypotheses.

Data Analysis

Scores from the PARH scale can be used in a number of ways to test the possibilty that research results are the product of demand characteristics:

1. Identify positive outliers on the PARH scale: Positive outliers on the PARH scale (i.e., +3.00 or +2.50 standard deviations from the PARH mean) represent participants who are relatively confident that they are aware of the research hypotheses. Researchers can perform their statistical analyses with and without these outliers in order to demonstrate that their general pattern of research results is unaffected by these participants’ responses.

2. Perform a one sample t test on the mean PARH value: If participants’ mean PARH score is significantly below the scale midpoint (i.e., < 4.00) or not significantly different from this midpoint, then researchers can claim that, in general, their participants reported feeling unclear about the research hypotheses.

3. Perform correlation or regression analyses that include the PARH scale and the dependent variable(s): Nonsignificant relationships between PARH scores and the dependent variable(s) indicate that participants’ reported awareness of the research hypotheses is unrelated to their responses on the dependent variable(s).

4. Include the PARH scale as a covariate in analyses: If research effects persist while controlling for PARH scores, then they are likely to occur over and above the influence of any demand characteristics.

My colleagues and I have used the PARH scale to investigate the influence of demand characteristics in our own research (Meleady, Hopthrow, & Crisp, 2013;Rubin, 2011; Rubin, Paolini, & Crisp, 2011; Rubin, Paolini, & Crisp, 2013; Rubin & Paolini, 2014). Several independent researchers have also used the PARH scale in their research studies (Fuchs, Schreier, van Osselae, 2015; Prior & Sargent-Cox, 2014Poon & Chen, 2014; Su, 2014). Finally, several researchers have recommended the PARH scale (Allen & Smith, 2012; Ata, Thompson, & Small, 2013; Morrison, Madden, Odum, Friedel, & Twohig, 2014).


The PARH scale is free to use for research purposes. No permission is required. However, the scale may not be reproduced for commercial purposes.

Hard Copy

Please click here to download a hard copy of the scale for use in paper-and-pencil surveys.

Referencing the PARH Scale

The PARH scale was first used in a journal article that investigated bias against migrants. To refer to the PARH scale in published work, please use the following reference:

Rubin, M., Paolini, S., & Crisp, R. J. (2010). A processing fluency explanation of bias against migrants. Journal of Experimental Social Psychology, 46, 21-28. doi: 10.1016/j.jesp.2009.09.006

A self-archived version of this journal article is available here.


Berkowitz, L. (1971). The "weapons effect", demand characteristics, and the myth of the compliant subject. Journal of Personality and Social Psychology, 20, 332-338.

Orne, M. (1962). On the social psychology of the psychology experiment: With particular reference to demand characteristics and their implications. American Psychologist, 17, 776 783.

Page, M. M. (1973). On detecting demand awareness by postexperimental questionnaire. The Journal of Social Psychology, 91, 305-323. 

Strohmetz, D. B. (2008). Research artifacts and the social psychology of psychological experiments. Social and Personality Psychology Compass, 2, 861-877.