1 / 11

Measurement and Scaling

Measurement and Scaling. By: Dr Manoj Kuamr. Measurement and Scaling.

arminda
Download Presentation

Measurement and Scaling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Measurement and Scaling By: DrManojKuamr

  2. Measurement and Scaling • In the field of business research, a researcher tries to gather information through administering questionnaire. In business research the researcher have to measure the information on certain scale as per requirement of the research problem. • Measurement of physical properties is not a complex deal, whereas measurement of psychological properties require a careful attention of a researcher. • What should be measured is of utmost important. (FIVE POINT SCALE)

  3. Precise measurement in business research requires a careful conception careful conceptual definition, and a system of consistent rules of assigning scores and numbers (Zikmund, 2007).

  4. Scale of Measurement • Nominal Scale: When data are labels or names an used to identify the attribute of an element, the nominal scale is used. • Ordinal Scale: In addition to nominal level data capacities, ordinal scale can be used to rank or order objects. • Interval Scale: In internal level measurement, the difference between the two consecutive number is meaningful. • Ratio scale: Ratio level of measurements posses all the properties of data with meaningful ratio of two values. Scale of Measurement

  5. The Criteria for Good Measurement

  6. Validity • Validity is the ability of an instrument to measure what it is designed to measure. • It sound simple that a measure should measure what is supposed to measure but has a great deal of difficulty in real life. • For example, behaviour of employee to measure consumer satisfaction in a big mall is a validity issue. • It is always possible that behaviour of employee is not a determinant of consumer satisfaction rather various other factors such as pricing policies, discount policy, parking facility, quality of the product etc. may be responsible for consumer satisfaction. • Hence, the measure designed to measure consumer satisfaction from employee behaviour may not be a valid measurement tool.

  7. Content Validity • The content validation includes, but not limited to, careful specification of constructs, review of scaling procedures by content validity judges, and consultation with experts and the members of the population. • Sometimes, it is also referred as face validity. • In fact, content validity is a subjective evaluation of the scale for its ability to measure what it supposed to measure. • As it is subjective in nature, it alone is not a sufficient evaluation criterion.

  8. Criterion Validity • The criterion validity is the ability of the variable to predict the key variable or criteria. (Lehmann et al., 1998). • It involves the determination of whether the scale is able to perform up to the expectation with respect to the other variables or criteria. • Criterion variables may include demographic and psychographic characteristics, attitudinal and behavioral measure or scales obtained from other scales (Malhotra, 2004). • In accordance with the time sequence, the criterion validity is classified as a concurrent validity and a predictive validity.

  9. Concurrent Validity: if the data collected from the scale to be evaluated and the data collected on criterion variable are executed at the same time and are shown to be valid, then it has concurrent validity. • Predictive Validity: if the new measure is able to predict some future events, then the predictive validity is said to be established. For example, the consumer satisfaction measuring instrument is predictive valid if it followed by sales in near future.

  10. Construct Validity • Construct validity is the initial concept, notion, question, or hypothesis that determines which data are to be generated and how they are to be gathered (Golafshani, 2003). • To evaluate the construct validity, both the theory and the measuring instrument are considered. • Convergent Validity: A convergent validity is established new measure correlates or converges with the other similar measure. • Discriminant Validity: A discriminant validity is established new measuring instrument has low correlation or non-converges with the measure of dis-similar concept.

More Related