Some properties of ipsative, normative, and forced-choice normative measures.

Hicks, Lou E.

doi:10.1037/h0029780

Cited by 421 publications

(331 citation statements)

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“…Because all ipsative scale scores have to sum to a constant, their covariances with any external measure will sum to zero (Clemans, 1966;Hicks, 1970). This means that criterionrelated validity of an ipsative instrument will be distorted because any positive covariances with the external variable have to be compensated by some negative covariances, and vice versa.…”

Section: Distorted Criterion-related Validitymentioning

confidence: 99%

“…Therefore, many have argued that ipsative scores make sense when comparing relative strength of traits within one individual, but they do not provide information on absolute (normative) trait standing, thereby making comparisons between individuals meaningless (e.g. Closs, 1996;Hicks, 1970;Johnson, Wood & Blinkhorn, 1988). For instance, Hicks (1970) discusses how a group of scientist may show a low mean score on the Aesthetic scale simply because their mean score on the Theoretical scale has to be very high.…”

Section: Problems Of Ipsative Datamentioning

confidence: 99%

“…Since ipsative data produces a non-invertible covariance matrix, factor analysis cannot be applied to it. Moreover, factor analytic methods introduce the concept of error, and many authors have argued that it is not clear what error means when the data is ipsative (Hicks, 1970;Meade, 2004). Despite being a simple data reduction technique, PCA is still useful for direct comparison of principal sources of variance and illustrating similarities and differences between the four sets of CCSQ scores.…”

Section: Ordering Of Respondents: Correlations Across Scores Despitementioning

confidence: 99%

“…Closs, 1996;Hicks, 1970;Johnson, Wood & Blinkhorn, 1988). For instance, Hicks (1970) discusses how a group of scientist may show a low mean score on the Aesthetic scale simply because their mean score on the Theoretical scale has to be very high. This, however, does not mean that the scientists are less aesthetic than other people are -such conclusions simply cannot be made based on ipsative data.…”

Section: Problems Of Ipsative Datamentioning

confidence: 99%

“…Clemans, 1966; to sample-based empirical illustrations (e.g. Hicks, 1970;Johnson, Wood, & Blinkhorn, 1988;Tenopyr, 1988;Closs, 1996;Meade, 2004). The classical test theory (CTT) approach, which works reasonably well with Likert items, performs poorly when applied to forced-choice items.…”

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confidence: 99%

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How IRT can solve problems of ipsative data in forced-choice questionnaires.

Brown

Maydeu-Olivares

2013

Psychological Methods

154

185

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In multidimensional forced-choice (MFC) questionnaires, items measuring different attributes are presented in blocks, and participants have to rank-order the items within each block (fully or partially). Such comparative formats can reduce the impact of numerous response biases often affecting single-stimulus items (aka, rating or Likert scales). However, if scored with traditional methodology, MFC instruments produce ipsative data, whereby all individuals have a common total test score. Ipsative scoring distorts individual profiles (it is impossible to achieve all high or all low scale scores), construct validity (covariances between scales must sum to zero), criterion related validity (validity coefficients must sum to zero), and reliability estimates.We argue that these problems are caused by inadequate scoring of forced-choice items, and advocate the use of item response theory (IRT) models based on an appropriate response process for comparative data, such as Thurstone's Law of Comparative Judgment. We show that by applying Thurstonian IRT modeling (Brown & Maydeu-Olivares, 2011), even existing forcedchoice questionnaires with challenging features can be scored adequately and that the IRTestimated scores are free from the problems of ipsative data. Assessments of personality, social attitudes, interests, motivation, psychopathology and well-being largely rely on respondent-reported measures. Most such measures employ the socalled single-stimulus format, where respondents evaluate one question (or item) at a time, often in relation to a rating scale (i.e. Likert-type items). Because the respondents rate each item separately from other items, they make absolute judgments about the extent to which the item describes their personality, attitudes, etc. Simple to answer and score and therefore popular with test takers and test users, the single-stimulus format makes several assumptions about the respondents' rating behaviors that are often unrealistic. For instance, the use of rating scales relies on the assumption that respondents interpret category labels in the same way. This assumption is very rarely tested in practice, but research available on the issue suggests that interpretation and meaning of response categories vary from one respondent to another (Friedman & Amoo, 1999). Furthermore, individual response styles may vary (Van Herk, Poortinga & Verhallen, 2004) so that some respondents avoid extreme categories (central tendency responding), whereas others prefer them (extreme responding). Sometimes respondents tend to agree with both positive and negative statements as presented (acquiescence bias).Another common problem is getting respondents to differentiate between ratings they give to single-stimulus items. When rating another person's attributes or behavior (as in the 360-degree feedback), respondents commonly give either high or low ratings on all behaviors (halo/horn effect) depending on whether they judge the person to score high or low on a single important dimension. Typically, respon...

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Section: Distorted Criterion-related Validitymentioning

confidence: 99%

Section: Problems Of Ipsative Datamentioning

confidence: 99%

Section: Ordering Of Respondents: Correlations Across Scores Despitementioning

confidence: 99%

Section: Problems Of Ipsative Datamentioning

confidence: 99%

mentioning

confidence: 99%

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How IRT can solve problems of ipsative data in forced-choice questionnaires.

Brown

Maydeu-Olivares

2013

Psychological Methods

154

185

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The process of value-meaning construction: A cross-sectional study

Menezes

Campos

1997

Eur. J. Soc. Psychol.

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A cross-sectional study within Portuguese adolescents, young adults and adults is presented in an attempt to explore the process of value-meaning construction. A model of values psychological structure developed by Bilsky (1987, 1990) is evaluated within a sample of 600 subjects. Such a model presupposes the existence of a universal organization of values on 10 content domains or motivational types. Schwartz's value survey was used to measure subjects' value priorities. Results reveal the validity of the predefined motivational types on the three samples although there are some structural variations between the samples. Such variations may be attributed to the development characteristics of each group. Cognitive-developmental theory emerges as an adequate framework for the analysis of results, assuming the active role of the subject on the process of constructing personal-meaning-theories out of reality.

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Measuring Personality and Psychopathology

Morey

2012

Handbook of Psychology, Second Edition

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Some properties of ipsative, normative, and forced-choice normative measures.

Cited by 421 publications

References 49 publications

How IRT can solve problems of ipsative data in forced-choice questionnaires.

How IRT can solve problems of ipsative data in forced-choice questionnaires.

The process of value-meaning construction: A cross-sectional study

Measuring Personality and Psychopathology

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