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A METHOD FOR CONSTRUCTING LIKERT SCALES

Jamal Munshi, Sonoma State Univesity, 1990
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Semantic Scales

Semantic scales like the Likert, the Stapel, and the Semantic Differential, are used for constructing most instruments used in business research. The precision and accuracy of these instruments determine the extent to which measurement errors can be avoided and are therefore of great concern to business researchers. An instrument with low precision can cause Type II errors by failing to detect real effects while those with low accuracy may detect spurious effects that do not exist.

It has long been recognized that the precision (reliability) and the accuracy (validity) of verbal instruments are determined to a large degree by the design and construction of the scales (Thurstone 1928). In this study, we focus our attention on the Likert scale and consider three characteristics of semantic scales with respect to scale quality.

The principal objective of this research is to develop a method for determining the number of choices that should be used. The related questions of the assumption of equal intervals and of symmetry are also addressed. We then offer a method for constructing semantic scales that we believe will reduce measurement error. Other scale characteristics such as the length of the scale, the semantics of the scale, balanced versus unbalanced construction, and bipolar versus unidimensional scales are also considered but are not the subject of this report.

Likert himself, in his original paper, did not consider the number of choices to be an important issue (Likert 1932) stating only that "If five alternatives are used, it is necessary to assign values from one to five with the three assigned to the undecided position." It is implied that the actual number of choices may be left to the tastes of individual researchers. In practice researchers often do assign the number of choices arbitrarily according to personal taste or past convention.

By choices we mean the number of alternatives that are to be included in the scale. There are at least three aspects of this issue. In one view, typified by that of Green and Rao (1970) and of Lehmann and Hulbert (1972), the response evoked by the scale statement is assumed to be continuous and linear and therefore can be subdivided into any number of infinitesimal and equal intervals between which the subject are not indifferent. According to this logic, the error imposed by a scale with a fixed number of choices is simply the round-off error committed when the assumed continuous response is converted into a scale choice. In this model, the precision of the scale necessarily increases with the number of choices used and the induced measurement error asymptotically approaches zero. Therefore no extremum can exist.

Neither Green and Rao nor Lehmann and Hulbert tested these ideas empirically. Instead they presented an analysis using simulated data that verifies the mathematical intuition that as the number of choices is increased the rounding errors decrease; at first rapidly and then more slowly. The maximum rounding error is always half the distance between adjacent choices. As the number of choices increases, this distance shrinks geometrically. The authors contend that optimality in this case is determined by cost considerations. Fewer choices are more economical and easier to use. The cost of additional choices must be justified by an increase in precision. Since additional scale choices offer diminishing marginal precision, it is posited that at some point the marginal increase in precision will not outweigh the additional cost. It is at that point that an optimum can be said to exist. However, Green and Rao agree with Murphy and Likert (1938) that the optimum number of alternatives is likely to depend on the application and the number of items in the instrument.

But the value of the simulation studies suffers from the lack of empirical data to support the assumptions. In particular, there is no evidence to support the implicit condition that subjects can differentiate between an infinite number of choices on a continuous scale. Studies by other psychologists (Miller 1956) record the contrary finding that the number of levels of any variable that can be internalized is not only finite but small. And the present study shows that, given a continuous scale, subjects do not respond over a uniform distribution but in predictable clusters.

Empirical Measures of Consistency

An alternate approach to the determination of an optimum number of choices is that of raw empiricism. Rather than assume a continuous true response, these studies test instruments which have anywhere from two to eleven scale choices.The quality of these instruments is then assessed directly using various measures. Early studies used split half reliability as a criterion of scale quality. In later works, the measure of quality has included Cronbach Alpha reliability, test-retest reliability, concurrent validity, predictive validity, and combinations of these.

One of the earliest such studies is by Symonds (1924) who used split-half reliability measures to discover that coarser scales have lower consistency. He suggested increasing the number of choices as a means of increasing the instrument reliability implying that reliability always increases with the number of scale choices. He did not directly address the issue of optimality but hinted that cost considerations could limit the number of choices used. ("Coarser scales are more readily used than finer scales.")

The earliest work suggesting the existence of an optimum number of choices independent of cost is that of Pemberton (1933). Given Symonds hypothesis that fewer scales points are more desirable than more and Symonds finding that finer scales have higher reliability than coarser scales, Pemberton sought to find "how coarse a scale we could use without lowering reliability" below a minimum acceptable value".

Pemberton studied five to eleven point bipolar scales with a sample of 450 students. Using test-retest reliability to assess scale quality he found, contrary to Symonds, that the reliability did not always increase with the number of scale points but rose to a maximum of 0.82 at seven intervals and thereafter decreased. This suggests that an optimality exists purely on the basis of reliability without recourse to the economy of coarseness.

Murphy and Likert (1938) extended the concept of scale quality by including the number of items and the number of choices in an overall measure of instrument quality. They suggested that the split half reliability is a function of the total number of choices in the instrument. The total number of choices is computed as the intervals per scale times the number of items. The implication is that the optimum number of intervals in a scale does not exist independent of the number of items but that a joint optimum for both variables could be found on the basis of instrument reliability.

They showed that when the total number of items was reduced from 44 to 36, the loss of split-half reliability could be reversed by increasing the number of scale choices from 3 to 5. They explain that this is due to the fact that the total number of answer choices were actually increased from 132 (44 times 3) to 180 (36 times 5). The split half reliability rose from 0.88 to 0.94 as a result.

This notion, that the number of scale points can be balanced against the number of items to achieve a given reliability, was confirmed by Bendig (1954) and again in very similar studies by Komorita (1963) and Komorita and Graham (1965). These authors decomposed the Likert scale into a polarity component (agree or disagree) and an intensity component (degree of agree or disagree) to determine the role played by intensity in determining scale quality.

Using split-half and Cronbach Alpha reliability as measures of consistency they found that when the number of items is large, the intensity component was unimportant. This implies that the internal consistency is not affected by the number of choices beyond the minimum of two needed to establish the polarity. Peabody (1962) also reported the dominance of the directional component of the Likert scale over the intensity component. He proposed that his findings justified a two-point or dichotomous agreement scale of either agree or disagree. This hypothesis was later supported by Komorita and others.

The findings of Bendig, Peabody, and Komorita were generalized by Jacoby and Matell (1971) and Matell and Jacoby (1972) to include other measures of scale quality. They confirmed the dominance of dichotomy in determining reliability and the relative irrelevance of additional scale choices to assess intensity. The significant contribution of the Jacoby and Matell studies was to show that the irrelevance also applied to test-retest reliability, concurrent validity, and predictive validity.

Contrary to the findings of Pemberton, these studies do not posit the existence of an optimum number of choices based on scale quality alone. They predict an asymptotically increasing function but show that the increase in quality beyond two or three choices may not be warranted by the additional cost and complexity of the instrument and of the data gathering process.

A New Approach to Optimality

In a broad sense, the quality of a scale describes its ability to faithfully reflect the attitude or opinion to be measured. A significant part of this construction involves the semantics of the scale - the adjectives, words, phrases, and sentence construction that will elicit the correct internal response in the subject. This aspect of scale construction is strictly outside the scope of this study.

In this study we assume that the correct response has already been internally generated in the subject. It now remains for the instrument designer to provide the subject with the most suitable vehicle for its expression; and produce data in a form that can be readily collected by normal survey methods and easily analyzed by conventional statistical procedures. Within this framework, the important question for scale designers is therefore; How is the information being internalized by the subject?

More specifically, we would like to know; whether the degree of agreement is continuous or discrete and if it is discrete we would like to know the number of discrete levels being used; whether there is a neutral point; whether the negative and positive responses on either side of the neutral are viewed as symmetrical; and whether the discrete levels are at equal distances. If the distances are not equal we would like to know what these distances ought to be. The answers to these questions will allow us to construct a response scale that closely matches the internal response of the subject and therefore reduces measurement error that might have been caused by forcing a subject to express his feelings through an inadequate scale.

The question of continuity has been addressed by Miller (1956) who has postulated that the internal process is discrete and that the number of discrete steps involved is "seven plus or minus two" . Other psychologists (for example Simon and Newell 1971) have largely supported this view. It may therefore be inappropriate to assume that the attitude and opinions scales are internalized in a continuum. The implication is that construction of precise scales should be attempted only after the correct number of intervals has been discovered and further, that the correct or optimal number should be based not on cost/benefit analyses but on the information processing characteristics of the human subjects.

If too few choices are provided on the scale, then the subject may wish to select a non-existent choice in between two consecutive choices. Since such a choice does not exist, the response will likely be randomly assigned to either of the adjacent existing choices. Clearly, such a random assignment of choices will lower the correlation between items that are, in truth, highly correlated. The loss in correlation helps to explain the improvement in reliability with finer scale construction found by the black-box empiricists mentioned above. On the other hand, if the scale has too many choices, then the subject will be indifferent between two or more consecutive choices that represent a single internal level. Being forced to select only a single choice rather than a range, the subject randomly assigns the response to one of the choices within this range. Once again, the random assignment reduces correlation and all measures of reliability and consistency. Pembertons empirical results show that this may indeed be the case.

Symmetry and Equal Distances

Various authors have shown that the response to verbal scales are subject to framing error. For example Kahneman and Tversky (1984) find that the response to the same statement can vary depending on whether it is stated as a positive (90% chance of surviving an illness) or a negative (10% chance of dying from the illness). This served as an experimental verification of effects detected by empirical researchers Worcester and Burns (1975). Worcester and Burns conducted a field test in which they found asymmetric responses to positive and negative framing of the same verbal cue. A more generalized finding of questionnaire framing effects was reported by Wildt and Mazis (1978) in which they report effects not only of the semantics of the items but of the location of the phrase in relation to the endpoints of the scale. These findings indicate that the assumption of symmetry normally implicit in scale construction may not be valid.

The questions of scale construction being pursued here are "How many scale choices should be used and where should these choices be placed on the scale?". The second question raises the issue of distance. The use of equal intervals is controversial and it has been argued that unless the equality of the intervals can be experimentally verified, scale data can only be treated as ordinal and a measurement error is introduced when they are used as interval data (Martilla and Garvey 1975).

Although Labovitz (1970) has argued that the magnitude of such measurement errors is small when the number of items in a single summated score is large, it still remains a quest of business research to improve the precision and accuracy of the research instruments by reducing this error (Ashton 1990). One way to reduce this error and render Likert scale data more `interval like is to determine the true distances between scale choices so that they may be appropriately weighted.

The present study offers a method by which the true distances can be discovered. It is proposed that the use of this method will result in scales in which the number of choices and their placement on the scale more closely reflect the response structure being elicited. It is further proposed that the use of scales constructed in this manner will reduce errors of measurement and interpretation that can be introduced by an inappropriate number of choices and their arbitrary placement on the scale.

Methodology

The method of discovery being proposed contrasts with the method of testing used in previous research that presented subjects with scales containing a selected and discrete number of equally and symmetrically spaced choices. In the discovery method being proposed,we present the subject with only a line allowing her, in essence, to construct her own scale. By averaging the choices and distances chosen over the entire sample, we can determine whether there is a consensus as to the existence and nature of a discrete semantic scale.

The questionnaire used is shown in the appendix. It contains eight items that were extracted from an inventory of items currently under development at Sonoma State University. The semantics of these items has not been widely tested and validated and this lack of validation is of some concern in this study. Item inventory reliability is important because we seek to test the scale structure independent of semantics. We intend to investigate semantics further after a scale has been selected.

The five-point scale normally used with such items was replaced with a straight line which measured 76 mm in length. The subjects were asked to place a tick mark anywhere on this line to indicate the strength of agreement with the corresponding statement. The distance from the left end of the line to the mark is taken as being proportional to the degree of agreement. This means that a mark at the extreme left end denotes absolute disagreement while one at the extreme right end means complete agreement.

Two hundred and ten business travellers waiting for flights to SE Asia were used as subjects in this study. To increase the variety of responses so that all parts of the response line will be used the eight items were divided into two groups of four bipolar subsets. The subjects were aware that they were participating in a study but were informed of the actual nature of the experiment only after they had completed the questionnaire.

The full sample consisted of 1615 marked lines. The distance from the left end of the line to the tick mark in each marked line was carefully measured with a ruler to within half a millimeter. An analysis of the distribution of these distances was then undertaken to determine whether the entire length was used in a homogeneous and random manner or whether there existed any cluster patterns. A uniform or unimodal distribution would indicate that a true response over a continuous scale exists. However, if the distribution is multi-modal it would indicate discrete regions of response.

The number of these distinct clusters of responses may be interpreted as a consensus on the number of scale points identified by the subjects. Moreover, since the respondents are not forced to choose among pre-assigned equal-interval scale points, the distances between these clusters may also be interpreted. Specifically, if these clusters do not occur at equal intervals or in a symmetrical arrangement, then we would have reason to suspect that the use of equal interval and symmetrical scales will introduce measurement errors. Assignment of scale weights according to the distances of these clusters would more accurately represent interval data than would equal interval scales. This method is somewhat similar to that employed by Didow, Keller, and Barksdale (1985). In their procedure, weights are iteratively assigned to Likert scale points until a maximum discrimination is obtained where one is hypothesized. The consequence is a likelyhood of Type I errors since the instrument is designed to reject Ho. However, the method being proposed here is independent of the hypothesis to be tested by the instrument..

Analysis and Results

Figure 1 shows the distribution of all 1615 distances. The distribution shows where the responses are concentrated and, equally important, where they are conspicuously sparse. A visual analysis suggests that the responses are in fact clustered around a number of identifiable points on the line. It is significant that these scale points were not assigned by the researcher but selected independently by a number of different subjects. The distribution is heavily loaded on the ends; 369 of the 1615 points, or 23 percent of all distances, are either at 0 mm or 61 mm - the two extremes. These marks are easily distinguished from all others as a separate class of marks and their position in the line is not in question. It is also clear that these marks do not represent a failure to understand the directions because the same respondent has placed marks at the ends and also at various distances within the line.

Since their true position is not in question, it is not deemed necessary to subject the end points to statistical analyses and we assume that the end points represent two of the scale points needed. It now remains for statistical analysis to extract the remaining scale points from the 1246 remaining points that are placed at some distance along the line. Cluster analysis was used to determine whether the points can be grouped. A non-hierarchical method of clustering (SAS FASTCLUS) using sorted data, equally spaced initial seeds, and the DRIFT option was applied to test two through eleven clusters. The drift option forces recomputation of cluster means each time an observation is assigned to a cluster.

The number of clusters studied, two through eleven, covers the entire range of the number of scale points investigated by previous researchers. Table 2 summarizes the results of the cluster analysis. For each number of clusters, the table shows the r-squared value and details of the computation of an F-statistic described below. The r-squared value is the ratio of the sum of squares explained by the clusters to the total sum of squares. This is shown in equation form below (SS = sum of squares). R-squared = (Total SS - Within cluster SS) / (Total SS) The within cluster SS can be considered to be analogous to an error sum of squares or SSE. Thus the higher the r-squared, the tighter the clusters.

Table 1 shows a significant explanatory power of the simple dichotomy. More than 75% of the total sum of squares in the data can be explained by two just clusters. This finding is consistent with previous research such as that of Bendig, Komorita, and Peabody, that postulated a low power of the intensity component of the Likert scale compared to the simple polarity. On the other hand, it is apparent in examining Table 1 that SSE rapidly declines when the number of clusters is increased beyond two. If only two choices are used, the additional sum of squares that can be explained by three or more clusters will be considered to be random measurement error and will tend to reduce the power of an instrument constructed with these scales.

We hypothesize at this point that the sample of 1246 distances was drawn from a population of only Omega distinct values but that in the process of drawing these values, some measurement error was introduced so that the sample values are multimodally distributed around these Omega distances. We would like to discover the value of Omega. The 1246 data points actually consist of 180 distinct distances. It is obvious that if 180 clusters are selected (i.e. Omega=180), r-squared will be identically unity since the error sum of squares will be exactly zero. For any number of clusters less than 180, a finite amount of variance within the clusters will be introduced. Conversely, as the number of clusters is increased from two to eleven, r-squared increases. The increase in r-squared is rapid at first but then flattens out. The flattening of the r-squared curve suggests that there exists an optimal number of clusters beyond which the improvement in r-squared is not statistically significant. The next section describes a criterion for selecting Omega, the number of clusters to use.

A Method for Selecting the Number of Clusters

The differences between the cluster means and the distances within that cluster can be viewed as residuals in regression terminology. If the sampling distribution of the cluster residuals are normally distributed then the mean square error terms will be chi-square distributed and their ratio will be F-distributed. The F-distribution can be used to construct tests for determining the required number of clusters; that is, to test any value of Omega. Using the definition of r-squared presented we can express the error sum of squares as, SSE = SST * (1 - R2 ). The degrees of freedom, µ, associated with a given number of clusters, p, is p-1. Using these equations, we can now set up a statistic to test the difference in explanatory power between two number of clusters p1 and p2 where p2 > p1 as F = [ (SSE1 - SSE2 )/(µ1 - µ2 ) ] / [ SSE2 / µ2 ] The null hypothesis (Ho) is that the sum of squares explained by p1 clusters is the same as that explained by p2 clusters. Under conditions of the null hypothesis, this ratio of chi-squares will be F-distributed with numerator degrees of freedom of (µ1 - µ2) and denominator degrees of freedom of µ2.

Substituting SSE = SST (1-R2), and canceling the SST term which appears in the numerator and denominator, we get F = [ ( R22 - R12 ) / R22 ] * [ µ2 / ( µ1 - µ2 ) ] For hypothesis testing, this F-statistic is then compared with the value at alpha = 0.05, numerator degrees of freedom = (µ1 - µ2), and denominator degrees of freedom = µ2. If the F-statistic computed exceeds this value, then we reject the null hypothesis and conclude that p2 clusters explains significantly more of the total sum of squares than does p1 clusters.

The F-statistics are shown in Table 1. In line 1, three clusters are compared with two clusters yielding an F-value of 6.04. Since this is less than the critical value of 18.51 we fail to reject Ho and find insufficient evidence that three clusters are better than two. This finding is inconsistent with that of Jacoby and Matell who proposed Likert scales with three alternatives.

Next we redefine our full model to have four clusters and once again compare the full model with the reduced model. The reduced model is still the case with only two clusters. We are unable to reject Ho at the 5% level. However, at p=5 clusters, the F-statistic is larger than the critical so we reject Ho and replace the dichotomous model with p=5 as our current best model. Comparisons of p=6 through 11 with p=5 in Table 1 shows that the improvement in explanatory power of the additional clusters is not statistically significant at the 5% level. Thus, we keep p=5 as the optimal number of clusters and our best unbiased estimate of Omega.

Table 2 shows the location of the cluster means for the five-cluster case. Figure 2 is a graphical depiction of a Likert scale implied by Table 2. It consists of seven choices more or less symmetrically distributed. The important difference between this scale and a traditional 5-point Likert scale is the inclusion of the two end-points. The construction suggests that the respondents make a small but unambiguous distinction between strong agreement and complete agreement. This aspect is missing in conventional Likert scales.

The seven points may be therefore be labeled as - Absolutely disagree, Strongly disagree, Disagree, Indifferent, Agree, Strongly agree, and Completely agree. Although the scale is remarkably symmetrical, the distances between scale points are not equal. The distance between complete agreement and strong agreement is only a third of that between simple agreement and strong agreement. Moreover, the distance from the neutral point to simple agreement is 25% larger than that between simple agreement and strong agreement. These distances imply that Likert data from scales with equal distance may only be treated as ordinal and not as interval data. Conversely, if a scale is constructed according to Figure 2 and if the data are weighted according to the distances shown, the values generated will have more of the properties of interval data. The implication that the respondents view degrees of agreement in seven levels is consistent with the theory of Miller (1956) and the findings of Pemberton (1933).

Conclusions and Implications

The data suggest that a Likert scale with seven points according to the distances shown in Figure 2 would generate data that can be used as interval data with a lower measurement error and a correspondingly higher precision when compared with 5-point equal interval scales. The placement of the seven points on the scale is surprisingly symmetrical but not at equal distances.

An important feature of the proposed scaling method is contained in the four points at and near the ends. We can conclude from these clusters that the subjects make a distinction between strong agreement and unqualified support and between strong disagreement and and complete rejection. A standard Likert scale would not allow them to express this opinion.

The number of scale points to be used and their placement are likely to depend on the semantics of the instrument. This study proposes a method by which Likert scales may be tailored for any given instrument and semantics. The method consists of performing a pretest using unmarked lines as scales, measuring the distances marked by the respondents, and using cluster analysis to determine the best placement of scale points for the actual study. The tailor-made scale is likely to produce an instrument of higher precision than that constructed with arbitrary placement of scale points.

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TABLE 1

F-Test to Determine the Number of Clusters

Full Full Full Full Reduced Reduced Reduced Reduced Change Change F Fc
p R-sq SSE df p R-sq SSE dfSSE df
3 9379 621 2 2 7504 2496 1 1875 1 6.04 18.51
4 9603 397 3 2 7504 2496 1 2099 2 7.93 9.55
5 9725 275 4 2 7504 2496 1 2221 3 10.77 6.59
6 9799 201 5 5 9725 275 4 74 1 1.84 6.61
7 9846 154 6 5 9725 275 4 121 2 2.36 5.14
8 9879 121 7 5 9821 179 4 58 3 1.12 4.35
9 9902 98 8 5 9821 179 4 81 4 1.65 3.84
10 991981 9 5 9821 179 4 98 5 2.18 3.48
11 9933 67 10 5 9821 179 4 112 6 2.79 3.22

TABLE 2

Centroid Data for the Five Cluster Case

Cluster Number Cluster Mean Standard Deviation Number of Points
2 6.43 4.22 291
1 23.41 5.87 175
4 48.35 6.11 158
5 73.78 6.12 244
3 92.76 4.68 378

The cluster means and standard deviations are shown as percent of the distance from the left end of the scale