The PISQ-IR: considerations in scale scoring and development
- 914 Downloads
This paper provides a detailed discussion of the psychometric analysis and scoring of a revised measure of sexual function in women with pelvic floor disorders (PFD): the Pelvic Organ Prolapse Incontinence Sexual Questionnaire, IUGA-Revised (PISQ-IR).
Standard tools for evaluating item distributions, relationships, and psychometric properties were used to identify sub-scales and determine how the sub-scales should be scored. The evaluation of items included a nonresponse analysis, the nature of missingness, and imputation methods. The minimum number of items required to be answered and three different scoring methods were evaluated: simple summation, mean calculation, and transformed summation.
Item nonresponse levels are low in women who are sexually active and the psychometric properties of the scales are robust. Moderate levels of item nonresponse are present for women who are not sexually active, which presents some concerns relative to the robustness of the scales. Single imputation for missing items is not advisable and multiple imputation methods, while plausible, are not recommended owing to the complexity of their application in clinical research. The sub-scales can be scored using either mean calculation or transformed summation. Calculation of a summary score is not recommended.
The PISQ-IR demonstrates strong psychometric properties in women who are sexually active and acceptable properties in those who are not sexually active. To score the PISQ-IR sub-scales, half of the items must be answered, imputation is not recommended, and either mean calculation or transformed sum methods are recommended. A summary score should not be calculated.
KeywordsSexual function questionnaire Pelvic organ prolapse Urinary incontinence Anal incontinence Psychometric analysis Scale development
This study was reviewed and approved by the University of Minnesota IRB #0908 M70626. This study was funded by the International Urogynecological Association. University of Minnesota, Grant Award Number CON000000021500, Todd H Rockwood, PhD, PI.
Conflict of interest
- 1.Rogers RG et al. (2013) A revised measure of sexual function in women with pelvic floor disorders (PFD); the Pelvic Organ Prolapse Incontinence Sexual Questionnaire, IUGA-revised (PISQ-IR). Int Urogynecol J. doi: 10.1007/s00192‐012‐2020‐8
- 2.Campbell DT, Russo MJ (2001) Social measurement. Sage, Thousand Oaks, p 509Google Scholar
- 3.Campbell DT, Overman ES (1988) Methodology and epistemology for social science: selected papers. University of Chicago Press, Chicago, p 609Google Scholar
- 4.Cole DA, Howard GS, Maxwell SE (1981) Effects of mono-versus multiple operationalization in construct validation efforts. J Consult Clin Psychol 49(3):393–405Google Scholar
- 5.Shepard RN, Romney AK, Nerlove SB (1972) Multidimensional scaling; theory and applications in the behavioral sciences, vol. 1: Theory. Seminar Press, New YorkGoogle Scholar
- 6.Kruskal JB, Wish M (1993) Multidimensional scaling. Sage, Beverly HillsGoogle Scholar
- 7.Summers GF (1970) Attitude measurement. Rand McNally, Chicago, p 568Google Scholar
- 8.Nunnally JC, Bernstein IH (1994) Psychometric theory, 3rd edn. McGraw-Hill Series in Psychology. McGraw-Hill, New York, p 752Google Scholar
- 9.Gorsuch RL (1974) Factor analysis. Saunders, Philadelphia, p 370Google Scholar
- 10.Jackson DJ, Borgatta EF (1981) Factor analysis and measurement in sociological research: a multi-dimensional perspective. Sage Studies in International Sociology. Sage, Beverly Hills, p 313Google Scholar
- 11.McKnight PE (2007) Missing data: a gentle introduction. Methodology in the social sciences. Guilford Press, New York, p 251Google Scholar
- 12.Gorsuch RL (1983) Factor analysis, 2nd edn. L. Erlbaum Associates, Hillsdale, p 425Google Scholar
- 13.Cronbach LJ (1951) Coefficient alpha and the internal structure of tests. Pscyhometrika 16:197–334.Google Scholar
- 14.Allison PD (2002) Missing data. Sage University papers: Quantitative Applications in the Social Sciences. Sage, Thousand Oaks, p 93Google Scholar
- 15.Little RJA, Rubin DB (2002) Statistical analysis with missing data, 2nd edn. Wiley Series in Probability and Statistics. Wiley, Hoboken, p 381Google Scholar
- 16.Groves RM, Dillman DA, Eltinge JL, Little RJA (2002) Survey nonresponse. Wiley Series in Survey Methodology. Wiley, New York, p 500Google Scholar
- 18.Howell DC (2007) The treatment of missing data. In: Outhwaite W, Turner SP (eds) The Sage handbook of social science methodology. Sage, LondonGoogle Scholar
- 19.Hox J, De Leeuw E, Dillman DA (2008) International handbook of survey methodology. Lawrence Erlbaum, PhiladelphiaGoogle Scholar
- 21.Little RJA, Rubin DB (2002) Statistical analysis with missing data, 2nd edn. Wiley, HobokenGoogle Scholar
- 22.Madow WG, Olkin I, Rubin DB (1983) Incomplete data in sample surveys. Academic Press, New York, pp 1–2Google Scholar
- 23.Van Ginkel JR, van der ark LA, Sijtsma K (2007) Multiple imputation of item scores in test and questionnaire data, and influence on psychometric results. Multivariate Behav Res 42(2):387–414Google Scholar
- 24.Spector PE (1992) Summated rating scale construction: an introduction. Sage University Papers: Quantitative Applications in the Social Sciences, no. 07-082. Sage, Newbury Park, p 72Google Scholar
- 25.Ware JEJ, Kosinski M, Dewey JE (2000) How to score version 2 of the SF-36 health survey. QualityMetric, LincolnGoogle Scholar
- 26.Dillman DA, Smyth JD, Christian LM (2009) Internet, mail, and mixed-mode surveys: the tailored design method, 3rd edn. Wiley, HobokenGoogle Scholar
- 28.Krosnick JA, Narayan S, Smith WR (1996) Satisficing in surveys: initial evidence. New Directions for Program Evaluation 70:29–77Google Scholar
- 29.Snider JG, Osgood CE (1969) Semantic differential technique; a sourcebook. Aldine, Chicago, p 681Google Scholar
- 30.Shrive FM, Stuart H, Quan H, Ghali WA (2006) Dealing with missing data in a multi-question depression scale: a comparison of imputation methods. BMC Med Res Methodol 6(57):1–10Google Scholar
- 32.Wonnacott TH, Wonnacott RJ (1981) Regression, a second course in statistics. Wiley Series in Probability and Mathematical Statistics. Wiley, New York, p 556Google Scholar
- 33.Blalock HM (1974) Measurement in the social sciences: theories and strategies. Aldine, Chicago, p 464Google Scholar