Number Of Response Categories Research Articles

Analysing multiattribute ranking data: Joint and conditional approaches

Rankings, in contrast to ratings, eliminate effects of individual differences in scale usage and avoid arbitrary definitions regarding the number of response categories and category labels. However, despite the appeal and popularity of this technique, few methods are available for the analysis of rankings on several attributes. This paper presents extensions of Thurstonian and logistic models for the joint analysis of multiattribute ranking responses and for conditional analyses where rankings on one of the attributes are modelled as a function of the rankings on the other attributes. These extensions are based on two approaches proposed to account for associations among the ranking responses. Empirical applications of the Thurstonian and logistic ranking models indicate that one of the two approaches appears particularly promising for the analysis of multiattribute ranking data.

The Effects of Nonnormality and Number of Response Categories on Reliability

A computer simulation was designed to determine whether increases in reliability would be obtained for situations in which an observed categorical distribution, such as that for a Likert scale, was deliberately constructed to match the shape of the underlying distribution of the trait being measured. Results indicated that reliability did increase with the degree of similarity between the underlying and observed distributions. Reliability also increased with the level of correlation among the variables and with the number of scale points. However, maximum gains were reached with 5 or 7 scale points, after which reliability values leveled off.

Loglinear multidimensional IRT models for polytomously scored items

A loglinear IRT model is proposed that relates polytomously scored item responses to a multidimensional latent space. The analyst may specify a response function for each response, indicating which latent abilities are necessary to arrive at that response. Each item may have a different number of response categories, so that free response items are more easily analyzed. Conditional maximum likelihood estimates are derived and the models may be tested generally or against alternative loglinear IRT models.

Properties of a Latent Trait Reliability Coefficient

This article takes as its point of departure a coefficient for gauging the reliability of composites based on a fitting of a logit model with normal prior to dichotomous items pertaining to social attitudes (abortion) and/or life feelings (alienation). The effect of item locations on that coefficient relative to that of item slopes is analyzed by means of simulated data. The sampling variation to which that coefficient is subject is investigated by Monte Carlo methods and by sampling theory; procedures for taking that variation into account are presented and illustrated. Discussion includes remarks on the following topics: sample size, sample design, missing responses, number of response categories, time-dependent reliability, group differences in reliability, random versus fixed effects.

Information Transmission in the Survey Interview: Number of Response Categories and the Reliability of Attitude Measurement

This paper examines the relation between the number of response categories used to measure attitudes in survey interviews and the reliability of such attitude measurements. I review and criticize the hypothesis that reliability increases with the information carrying capacity of a response scale. I also review the literature on the relationship between the number of scale points and measurement reliability. This leads to a set of predictions regarding the relationship between the number of scale points and reliability of measurement, which I then examine using results obtained from three-wave panel studies conducted by the General Social Survey and the National Election Study. Reliability estimates were obtained via several procedures (LISREL, EQS, and LISCOMP) employing a vari-

The effect of varying the number of response categories in a likert-type scale on response rate

The effect of varying the number of response categories in a likert-type scale on response rate

Number of Response Categories and Statistics on a Teacher Rating Scale

Each of six forms of a 10-item teacher evaluation rating scale, having 2, 3, 4, 5, 6, or 7 response categories per form, was administered to over 100 students at a Pacific Coast college. Means of item responses increased linearly and item variances increased curvilinearly with number of response categories. Internal consistency reliability coefficients (alpha) of total scores did not change systematically with increases in the number of response categories.

反応の多様性をもつ図形の言語的範疇化に及ぼす提示時間の効果

Subjects were required to categorize verbally the figures presented tachistoscopically with an increasing amount of the exposure duration, 2 through 3000ms. The figures were all ambiguous in that they had response multiplicity. With the increase of duration, the number of cognitive responses increased to a certain point, but, in some figures, it tended to decrease as the duration increased further. This suggested the existence of some conflict in the categorization process as well as the presence of some reorganization in the processing of the sensory information. The number of response categories as an index of response multiplicity and the subject's rating of confidence in cognitive response as an index of S-R compatibility were also examined.

The effects of verbal meaning and response categorisation on the fragmentation of steadily fixated patterns.

Studies of steady visual stimulation have suggested that the subjective meaning of a stimulus may determine the form and degree of its perceptual fragmentation. This was tested for steady fixation by presenting a letter of the alphabet ("L") in a sequence of other letters which were either normally oriented or inverted. No evidence was found for greater stability in the former case. An investigation of the categorisation of subjective disappearances showed that, although the stability of pattern components was not determined by reporting these singly or in conjunction, the disappearances of the whole pattern were significantly increased by reporting only these as opposed to reporting disappearances of each component. Response to stimulated subjective disappearances also showed that the effect was due to the nature rather than the number of response categories. In the final experiment all subjects fixated a rotated letter, after half had received pretraining in the recognition of rotated letters. No effects of the pretraining were apparent, for either level of response categorisation, and it was concluded that the processes underlying subjective disappearances in steady fixation are not modified by the meaningfulness of a stimulus. The involvement of central processes was, however, indicated by the effects of response categorisation and the influence of simulated disappearances on pattern stability in a trial subsequent to their presentation.

Task structure variables affecting concept identification

A mismatch between the number of values of the relevant and the irrelevant dimensions (when there is more than one irrelevant dimension) in a stimulus population enhances concept identification, probably because of a stimulus isolation effect. A mismatch between the number of values of the relevant dimension and the number of response categories, producing a form of S-R incompatibility, inhibits performance. Attentional and hypothesis testing processes appear to underlie both of these effects.

Response reliability and attitude change —Supplement to response errors and biased information

In the previous paper*, response error is treated in the form of response reliability represented by a probabilistic model in the case where no attitude change exists at two time points. Here, the author considers the problem of estimation of probabilities of atti tude change represented by a simple Markov process**, which is very popular, in the case where response error exists which is represented by a probabilistic model of reliability found in the previous paper. For simplicity, let the number of response categories be three, q-, _ , (see pp. 213-214 in the previous paper*). Let a reliability matrix be P. P is represented by the matrix the elements of which are p~j, i, j = + , • The number of response pairs -k+ , b • etc. obtained by test-retest are m~j, i, 3"= -k, • --. n~ is the number of true i responses, i J r , • -where n n+q-n• n being the size of sample. The equations which hold in the mean (expectation) are formally

Information transmission in spectral color naming

The concepts of information theory are applied in an analysis of color naming data where various numbers of response categories are permitted for the description of 21 spectral stimuli. The results are summarized by en empirical equation, T(x, y) = (N’−1)0.325−0.0017N’ where T(x, y) is the information transmitted through the subject (in bits) and N’ is the number of response categories used by the S. This function has a maximum value close to 2.6 bits. It is suggested that 28 response categories, corresponding to seven steps between adjacent hues of the color circle, constitute a good choke for experiments of this type.

Speed of classification of words into superordinate categories

The speed of classifying words into defined superordinate categories was examined. The classification test was chosen in order to differentiate between the effects of stimulus and response diversity upon the speed of information processing. Word lists were presented which differed with respect to the number of possible response categories, e.g., colors; and with respect to the number of available examples per category, e.g., red, green, or blue. Classification time was found to increase primarily with response diversity, i.e., with the number of possible response categories; and only secondarily with the number of available examples per category, i.e., stimulus diversity. The latter factor, however, becomes more important as the number of response categories is increased.