Quantitative Research About Likert Scales

A scale is basically a continuous spectrum or series of categories and has been defined as any series of items that are arranged progressively according to value or magnitude, into which an item can be placed according to its quantification.

Likert Scales originally being developed from 1946 to 1970 by a sociologist, Rensis Likert at the University of Michigan. It was being used initially as psychological attitudes that can be measured from qualitative standpoints into quantitative perspectives on a proper metric scale. For example, inches or degree Celsius true measurement scales. It is the most widely used scale in survey research that interchangeably with rating scale even though they are not synonymous. Respondents will answer based on their level of agreement to a specific statement. It was a bipolar scale running from one extreme through a neutral point to the opposite extreme. Subjects are asked to give the feedback and response based on five-point, seven-point or ten-point scale. It can measure agreement or disagreement on specific statements or questions. It also can be called as summative scales.

Bandura (1986) suggested that the best way to measure self-efficacy is to assess both magnitude and strength. In the past, authors such as Lee and Bobko (1994) believed that Likert scales did not follow Bandura’s recommendation. However, a Likert-scale measure of self-efficacy should provide an approximation to a traditional measure.

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Based on Tittle 1967, The Likert Scale is the most widely used method of scaling in the social sciences today. Perhaps this is because they are much easier to construct and because they tend to be more reliable than other scales with the same number of items. A true Likert scale creates a single scale from all of the items, then rescales each question according to the overall scale score for each response to each item.

Likert Scales vs Likert Items

The Likert Scale is a sum of responses on several Likert Items. It can be illustrated by using horizontal line by which a subject indicates his or her response by circling or checking tick-marks). In order to avoid any confusion on both, it is better, to reserve the term Likert scale to apply to the summated scale, and Likert item to refer to an individual item.

On a survey or questionnaire, a typical Likert item usually takes the following format:

Strongly disagree

Disagree

Neither agree nor disagree

Agree

Strongly agree

The final average score represents overall score of attitudes toward the subject matter. Example of Likert Scale and Likert Items are as follow:

Strongly Agree

Agree

Neither

Disagree

Strongly Disagree

If the price of raw materials fell firms would reduce the price of their food products.

1

2

3

4

5

Without government regulation the firms would exploit the consumer.

1

2

3

4

5

Most food companies are so concerned about making profits they do not care about quality.

1

2

3

4

5

The food industry spends a great deal of money making sure that its manufacturing is hygienic.

1

2

3

4

5

Food companies should charge the same price for their products throughout the country

1

2

3

4

5

Semantic scales

This type of scale makes extensive use of words rather than numbers. Respondents describe their feelings about the products or brands on scales with semantic labels. When bipolar adjectives are used at the end points of the scales, these are termed semantic differential scales. The semantic scale and the semantic differential scale are illustrated as follows:

Semantic and semantic differential scales

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The bipolar adjectives used, for instance would employ such terms as Good-Bad, Strong-Weak, Hot-Cold. The semantic differential scale is used to assess respondents’ attitudes toward a particular brand, advertisement, object or individual. The responses can be plotted to obtain a good idea of their perceptions. This is treated as an interval scale.

The Theory of Scale Types

Stevens (1946, 1951) proposed that measurements can be classified into four different types of scales. These are shown in the table below as: nominal, ordinal, interval, and ratio.

Nominal Scale

A nominal scale is one that allows the researcher to assign subjects to certain categories or groups. For example, if we refer to gender, the answer is one either male or female. These two groups can be assigned code numbers 1 and 2. These numbers serve as simple and convenient category labels with no intrinsic value, other than to assign respondents to one of two non-overlapping or mutually exclusive categories. Note that the categories are also collectively exhaustive. In other words, there is no third category into which respondents would normally fall. Thus, nominal scale categorizes individuals or objects into mutually exclusive and collectively exhaustive groups. The information that can be generated from nominal scaling is to calculate the percentage (or frequency) of males and females in our sample of respondents. In addition, nominal scales focus on only requiring a respondent to provide some type of descriptor as the raw response. Nominal Scale is always used for obtaining personal data such as gender or department in which one works, where grouping of individuals or objects is useful.

Example:

Please indicate your current marital status.

__Married __ Single __ Single, never married __ Widowed

Ordinal Scale

An ordinal scale not only categorizes the variables in such a way as to denote differences among the various categories, it also rank-orders the categories in some meaningful way. With any variable for which the categories are to be ordered according to some preference, the ordinal scale would be used. The preference would be ranked (e.g from best to worst; first to last) and numbered 1,2, and so on. It allows the respondent to express “relative magnitude” between the raw responses to a question. Ordinal Scale is used to rank the preferences or usage of various brands of a product by individual and to rank order individuals, objects, or events.

Example:

Which one statement best describes your opinion of an Intel PC processor?

__ Higher than AMD’s PC processor

__ About the same as AMD’s PC processor

__ Lower than AMD’s PC processor

The ordinal scale helps the researcher to determine the percentage of respondents who consider interaction with others as most important, those who consider using a number of different skills as most important, and so on. For example, such knowledge might help in designing jobs that would be seen as most enriched by the majority of the employees.

As of now, we can see the difference between nominal and ordinal whereby ordinal can give more information and depth perspective as compared to nominal. However, the ordinal does not give any indication of the magnitude of the differences among the ranks. This deficiency is overcome by interval scaling.

A problem with ordinal scales is that the difference between categories on the scale is hard to quantify. For example, excellent is better than good but how much is excellent better?

Interval Scale

An interval scale allows us to perform certain arithmetical operations on the data collected from the respondents. Whereas the nominal scale allows us only to qualitatively distinguish groups by categorizing them into mutually exclusive and collectively exhaustive sets and the ordinal scale to rank-order the preferences, the interval scale lets us measure the distance between any two points on the scale. This helps us to compute the means and the standard deviations of the responses on the variables. In other words, the interval scale not only groups individuals according to certain categories and taps the order of these groups; it also measures the magnitude of the differences in the preferences among the individuals. In other words, interval scales demonstrate the absolute differences between each scale point. Interval Scale is used when responses to various items that measure a variable can be tapped on a five-point (or seven-point or any other number of points) scale, which can thereafter be summated across the items.

Interval scales allow comparisons of the differences of magnitude (e.g. of attitudes) but do not allow determinations of the actual strength of the magnitude

Ratio Scale

The ratio scale overcomes the disadvantage of the arbitrary origin point of the interval scale, in that it has an absolute (in contrast to an arbitrary) zero point, which is meaningful measurement point. Thus, the ratio scale not only measures the magnitude of the differences between points on the scale but also taps the proportions in the differences. Ratio scales allow for the identification of absolute differences between each scale point, and absolute comparisons between raw responses.

Example;

Please circle the number of children under 18 years of age currently living in your household.

0 1 2 3 4 5 6 7 (if more than 7, please specify ___.)

Practically, Ratio Scales are usually used in organizational research when exact numbers on objective (as opposed to subjective) factors are called for. Another good example is Johan scale of temperature. This scale has an absolute zero. Thus, a temperature of 300 Johan is twice as high as a temperature of 150 Johan.

SUMMARY ON TYPES OF SCALES

Type of Scale

Numerical Operation

Descriptive Statistics

Nominal

Counting

Frequency in each category, percentage in each category, mode

Ordinal

Rank Ordering

Median, range, percentile ranking

Interval

Arithmetic Operations on Intervals between numbers

Mean, standard deviation, variance

Ratio

Arithmetic Operations on actual quantities

Geometric mean, coefficient of variation

Scale Type

Permissible Statistics

Admissible Scale Transformation

Mathematical structure

nominal (also denoted as categorical)

Mode, Chi-Square

One to One (equality(=))

Standard set structure unordered

ordinal

Median, Percentile

Monotonic increasing (order (<))

Totally ordered set

interval

Mean, standard deviation, correlation, regression, analysis of variance

Positive linear (affine)

Affine line

ratio

All statistics permitted for interval scales plus the following: geometric mean, harmonic mean, coeeficient of variation, logarithms

Positive similarities (multiplication)

Field

These constitute a hierarchy where the lowest scale of measurement, nominal, has far fewer mathematical properties than those further up this hierarchy of scales. Nominal scales give data on categories; ordinal scales give sequences; interval scales begin to reveal the magnitude between points on the scale and ratio scales explain both order and the absolute distance between any two points on the scale.

RELIABILITY

The reliability of a measure indicates the extent to which it is without bias (error free) and hence ensures consistent measurement across time and across the various items in the instrument. In other words, the reliability of a measure is an indication of the stability and consistency with which the instrument measures the concepts and helps to access the goodness of a measure.

Stability of Measures

The ability of a measure to remain the same over time – despite uncontrollable testing conditions or the state of the respondents themselves – is indicative of its stability and low vulnerability to change in the situation. This attests to its goodness because the concept is stably measured, no matter when it is done. Two sets of stability are test-retest reliability and parallel-form reliability.

In test-retest reliability, the respondents will be tested at two different times using the same sets of scale item in order to determine the degree of similarity of the two measurements.

In alternative-forms reliability, the same scale is constructed and respondents being measured at two different times whereby each time using different form.

Internal consistency reliability determines the extent to which different parts of a summated scale are consistent in what they indicate about the characteristic being measured.

The Cronbach’s alpha or coefficient alpha, measuring the internal consistency which determines the level of correlation between a set of items as a group. It is not a statistical test but a coefficient of reliability or consistency.

VALIDITY

Validity is the ability of a scale or measuring instrument to measure what it is intended to measure (e.g. is absenteeism from work a valid measure of job satisfaction or is there other influences like a flu epidemic which is keeping employees from work).

Content validity is a subjective but it being used to determine on content of scale that correspond to measurement task.

Criterion validity reflects whether a scale performs as expected in relation to other variables selected (criterion variables) as meaningful criteria.

Construct validity addresses the question of what characteristic the scale is – measuring. Construct validity includes nomological, discriminant and convergent validity.

Convergent validity is the evaluation to which the scale correlates positively with other measures of the same construct.

Discriminant validity is the extent to which a measure does not correlate with other constructs from which it is supposed to differ.

Nomological validity is the extent to which the scale correlates in theoretically predicted ways with measures of different but related constructs.