A more sophisticated technique for evaluating convergent and discriminant validity is the multi-trait multi-method (MTMM) approach. For instance, if there are two raters rating 100 observations into one of three possible categories, and their ratings match for 75% of the observations, then inter-rater reliability is 0.75. Inter-rater reliability is assessed to examine the extent to which judges agreed with their classifications. For instance, if you have a ten-item measure of a given construct, randomly split those ten items into two sets of five (unequal halves are allowed if the total number of items is odd), and administer the entire instrument to a sample of respondents. As an example, if you have a scale with six items, you will have fifteen different item pairings, and fifteen correlations between these six items.  Rev. Please note: your email address is provided to the journal, which may use this information for marketing purposes. An example of an unreliable measurement is people guessing your weight. This type of validity is called translational validity (or representational validity), and consists of two subtypes: face and content validity. Figure 7.3. Research Methodology Overview • Scaled-response questions (e.g., Likert scale, semantic differential scale)– An important issue in designing measurement instrument is the measurement property of the instrument, which includes reliability and validity. validation at the production scale. Table 7.2. Note here that the time interval between the two tests is critical. A third source of unreliability is asking questions about issues that respondents are not very familiar about or care about, such as asking an American college graduate whether he/she is satisfied with Canada’s relationship with Slovenia, or asking a Chief Executive Officer to rate the effectiveness of his company’s technology strategy – something that he has likely delegated to a technology executive. However, it is not possible to anticipate which subject is in what type of mood or control for the effect of mood in research studies. We call the property of having appropriate relationships with other variables construct validity. The correlation in observations between the two tests is an estimate of test-retest reliability. Next, scales and balances are found in dispensing areas to weigh components according to predefined formula- tions. Design/methodology/approach. Ambiguous items that were consistently missed by many judges may be reexamined, reworded, or dropped. If two measures have comparable face, content, and construct validity the more repeatable one may be preferred for the study of a given population. Hence, reliability and validity are both needed to assure adequate measurement of the constructs of interest. This work aims to translate and validate the Body Esteem Scale (BES, Mendelson, Mendelson, & White, 2001) in an Italian sample and to evaluate its reliability and dimensionality. Note that reliability implies consistency but not accuracy. For instance, respondents in a nicer mood may respond more positively to constructs like self-esteem, satisfaction, and happiness than those who are in a poor mood. If it appears to be correct, we call this face validity. These factors should ideally correspond to the underling theoretical constructs that we are trying to measure. If the measure is interval or ratio scaled (e.g., classroom activity is being measured once every 5 minutes by two raters on 1 to 7 response scale), then a simple correlation between measures from the two raters can also serve as an estimate of inter-rater reliability. However, if we were to suggest how many books were checked out of an office library as a measure of employee morale, then such a measure would probably lack face validity because it does not seem to make much sense. Validating scales and indexes. Validity , often called construct validity, refers to the extent to which a measure adequately represents the underlying construct that it is supposed to measure. While translation validity examines whether a measure is a good reflection of its underlying construct, criterion -related validity examines whether a given measure behaves the way it should, given the theory of that construct. Our criterion becomes agreement with another indirect measurement. Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale‐Up Model Validation and Missing Data on a Micro‐Bioreactor System. A test method must be shown to be fit for purpose so that a facility's customers can have confidence in the results produced by its application. ... a large‐scale data set is compared to data from a scale‐down model. A phobia scale which asked about fear of dogs, spiders, snakes, and cats but ignored height, confined spaces, and crowds would not do this. Unlike random error, which may be positive negative, or zero, across observation in a sample, systematic errors tends to be consistently positive or negative across the entire sample. A measure can be reliable but not valid, if it is measuring something very consistently but is consistently measuring the wrong construct. One of the primary sources is the observer’s (or researcher’s) subjectivity. Figure 7.2. In the previous example of the weight scale, if the weight scale is calibrated incorrectly (say, to shave off ten pounds from your true weight, just to make you feel better! Cardiac stroke volume, for example, can be measured only indirectly. A literature review may also be helpful in indicator selection. Sometimes, reliability may be improved by using quantitative measures, for instance, by counting the number of grievances filed over one month as a measure of (the inverse of) morale. 3 As with all measurements, we have to decide whether it measures what we want it to measure, and how well. Finally, a measure that is reliable but not valid will consist of shots clustered within a narrow range but off from the target. Data collected is tabulated and subjected to correlational analysis or exploratory factor analysis using a software program such as SAS or SPSS for assessment of convergent and discriminant validity. Verification as separate activity. 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