The following multi-item scale measures the technical sophistication of a product line.
Measuring Satisfaction Using the Oualtrics Question Library
Access to Qualtrics is included with this book. Use the Qualtrics question library to electronically develop the following satisfaction scales.
1. Five-point Likert (type) balanced scale
2. Five-point Likert (type) unbalanced scale
3. Seven-point Likert (type) balanced scale
4. Seven-point semantic differential scale
S. Five-point smiling faces scale
A multi-item scale should be evaluated for accuracy and applicability.V As shown in Figure 95, this involves an assessment of reliability, validity, and generalization of the scale. Approaches to assessing reliability include test-retest reliability, altemative-forrns reliability, and internal consistency reliability. Validity can be assessed by examining content validity, criterion validity, and construct validity
Before we can examine reliability and validity, we need an understanding of measurement accuracy, because it is fundamental to scale evaluation
As was mentioned in a measurement is a number that reflects some characteristic of an object. A measurement is !!0! the true value of the characteristic of interest but rather an observation of it. A variety of factors can cause measurement error, which results in the measurement or observed score being different from the true score of the characteristic being measured (see Figure’ 9_6)_The true score model provides a framework for understanding the accuracy of measurement. According to this model,
Xc = the observed score or measurement
XT = the true score of the characteristic
Xs = systematic error
XR = random error
Note that the total measurement error includes the systematic error, Xs’ and the random error. XR, Systematic error affects the measurement in a constant way, It represents stable factors that affect the observed score in the same way each time the measurement is made, such as mechanical factors (see Figure 9,6), Random error, on the other hand. is not constant. It represents transient factors that affect the observed score in different ways each time the measurement is made, such as transient personal or situational factors. The distinction between systematic and random error is crucial to our understanding of reliability and validity,
Reliability refers to the extent to which a scale produces consistent results if repeated measurements are madeP Systematic sources of error do not have an adverse impact on reliability, because they affect the measurement in a constant way and do not lead to inconsistency, In contrast, random error produces inconsistency, leading to lower reliability, Reliability can be defined as the extent to
which measures are free from random error. XR, If XR = O. the measure is perfectly reliable.
Reliability is assessed by determining the proportion of systematic variation in a scale. This is done by determining the association between scores obtained from different administrations of the scale. If the association is high. the scale yields consistent results and is therefore reliable. Approaches for assessing reliability include the test-retest. alternative-forms, and internal consistency methods
The simplest measure of internal consistency is split-half reliability. The items on the scale are divided into two halves and the resulting half scores are correlated. High correlations between the halves indicate high internal consistency. The scale items can be split into halves based on odd- and even-numbered items or randomly. The problem is that the results will depend on how the scale items are split. A popular approach to overcoming this problem is to use the coefficient alpha.
The coefficient alpha, or Cronbach’s alpha, is the average of all possible split-half coefficients resulting from different ways of splitting the scale items. This coefficient varies from 0 to I, and a value of 0.6 or less generally indicates unsatisfactory internal consistency reliability. An important property of coefficient alpha is that its value tends to increase with an increase in the number of scale items. Therefore, coefficient alpha may be artificially. and inappropriately, inflated by including several redundant scale items.26 Another coefficient that can be employed in conjunction with coefficient alpha is coefficient beta. Coefficient beta assists in determining whether the averaging process used in calculating coefficient alpha is masking any inconsistent items.
Some multi-item scales include several sets of items designed to measure different aspects of a multidimensional construct. For example, store image is a multidimensional construct that includes quality of merchandise, variety and assortment of merchandise, returns and adjustment policy, service of store personnel, prices, convenience of location,layout of the store, and credit and billing policies. Hence, a scale designed to measure store image would contain items measuring each of these dimensions. Because these dimensions are somewhat independent, a measure of internal consistency computed across dimensions would be inappropriate. However, if several items are used to measure each dimension, internal consistency reliability can be computed for each dimension.
The Technology Behind Technology Opinion Leadership
In a study of technology adoption,opinion leadership was measured using the following 7-point Liken-type scales (I = strongly agree, 7 = strongly disagree
1. My opinions on hardware/software products seem not to cj>untwith other people.
2. When other people choose to adopt a hardware/software product, they turn to me for advice.
3. Other people select hardware/software products rarely based on what I have suggested to them.
4. I often persuade other people to adopt the hardware/software products that I like.
5. Other people rarely come to me for advice about choosing hardware/software products.
6. I often influence other people’s opinions about hardware/software product
The alpha value for opinion leadership was 0.88, indicating good internal consistency. It was found that early adopters of technology products tend to be younger males who are opinion leaders, seek novel information, and have a lot of computer experience. Information technology companies like Microsoft need to ensure positive reactions from early product adopters and should focus marketing efforts on these individuals in the new product introduction stage