Reliability And Validity Assessment Essay ⋆ Political Science Essay Examples ⋆ EssayEmpire

In social science research, the terms reliability and validity refer to the accuracy of measurement. Inaccurate measurements may lead to erroneous or artificial conclusions or inferences.

Reliability

Reliability refers to consistency and uniformity of measurements across multiple administrations of the same instrument. More simply, there should exist some measure of equivalence and consistency in repeated observations of the same phenomenon. Thus, the more consistent the results across repeated measures, the higher the level of reliability. Conversely, the less consistent the results across repeated measures, the lower the level of reliability. Reliability cannot be computed precisely because of the impossibility of the calculation of variances and needs to be estimated.

Reliability Assessment

In political science, there are four primary methods for assessing the reliability of empirical measurements: the test-retest, alternative-form, split-halves, and internal consistency methods.

The test-retest method, or retest method, involves administering the same test of the same phenomenon to the same sample at two different times. If the measurements of both tests are consistent, the measure has high reliability. This method is not without its limitations. Some phenomena, for instance, are measurable only at one particular time, rendering the test-retest method impossible to conduct. Additionally, the test-retest method may suggest unreliable measurements not because the measure itself is unreliable, but because the phenomenon being studied may have changed in the interval between the two tests, often times as a result of reactivity, or the actual act of testing, which may itself inspire a change in the phenomenon.

The alternative-form method is similar in principle to the test retest method; however, a different mode of test is administered to measure the phenomenon at the second instance. Thus, two parallel but different measures are used instead of measuring the phenomenon using the same measure at each instance. Using two different measures reduces the risk of reactivity. As with the test-retest method, there is risk that the phenomenon itself may change between tests, artificially deflating the perceived reliability of measurements. Furthermore, the alternative-form method poses a unique challenge in that the tests need to be different yet parallel to yield comparable results.

The split-halves method affords the researcher the ability to conduct two measures of the same phenomenon at the same time, eliminating the risk of reactivity or the phenomenon itself changing over time. The researcher groups the items being measured into equivalent halves and administers the same test to each and then compares the results. If the results are consistent, the measurements demonstrate a high level of reliability. The split-halves method has its limitations, particularly as there are multiple possibilities as to how the items in each test can be grouped into halves. If the two halves are not identical, there is a high likelihood that the two tests may result in a different measurement on the basis of the halves being nonequivalent.

Finally, reliability can be assessed using the internal consistency method. This method, like the split-halves method, allows the researcher to measure a phenomenon at a single point in time. Moreover, like the alternative-form method, internal consistency is based on different measures of the same phenomenon; however, this method measures consistency and correlations among multiple similar items on the same test, thereby eliminating the need to administer two different tests or to split the items being measured into two groups. Therefore, it is implicit that similar items measuring the same phenomenon be included in a single test. If similar measures produce similar scores or illustrate a strong correlation, the measures demonstrate high internal consistency and are highly reliable.

Validity 1449

Validity refers to how well an instrument actually measures what it is designed to measure, or the degree to which a measurement procedure captures a theoretical concept being measured. A measur ing instrument that measures what it purports to measure is said to be valid. Conversely, an instrument that does not measure what it is designed to measure is said to be invalid. However, it is important to note that an instrument may be valid for measuring one particular type of phenomenon but be completely invalid for measuring another. Therefore, when assessing the validity of a given measure, a researcher does not evaluate the measuring instrument itself, but the measuring instrument in relation to its ultimate purpose

Types Of Validity And Validity Assessment

Disciplines define validity in different terms. In political science, there are four fundamental types of validity. The first is known as face validity, which refers to how well an instrument appears on its face to measure the phenomenon it purports to measure. When there is reason to question the correlation between a measuring instrument and the phenomenon it is designed to measure, an instrument lacks face validity. As such, when an instrument seems not to be relevant to the phenomenon it is measuring, or if there is a weak link between the measure and the concept that is being researched, the measure lacks face validity. Therefore, the assessment of face validity is a product of a researcher’s deductive reasoning and judgment based on the appearance that a given measure is valid.

Content validity is similar to face validity in that it predicates individual judgment, but it is often more difficult to assess, as it evaluates the extent to which an instrument adequately measures all aspects of a given concept or domain. As a result, the assessment of content validity necessitates the identification of all facets of a phenomenon and then determines the extent to which a measurement reflects and measures each facet. This is particularly difficult when the phenomenon in question is complex or multidimensional, as an instrument is only content valid when it measures all aspects of a given domain. However, if all aspects of a phenomenon are not sufficiently or adequately defined and enumerated, a measure cannot be content valid.

The third type of validity is construct validity, the degree to which a measure of a variable corresponds to the theoretical framework of a concept. An instrument is considered to be construct valid when it corresponds to the measure of a related concept. To determine construct validity, a researcher must first identify a potential relationship between two concepts, establish a measure for each, then examine the strength of the relationship between the pair. If the relationship is significant, a measure is construct valid. If no relationship is determined, the measure lacks construct validity, as at least one or the measures must not accurately reflect or measure the phenomenon.

The fourth way to assess validity is known as interitem association. It relies on using multiple measures of the same concept to determine validity. Similar to the internal consistency method, it evaluates the reliability of a measure by using more than one measure of a concept within a single measurement instrument. A consistency within outcomes demonstrates the validity of the overall measurement instrument.

Bibliography:

Alwin, Duane, F., and Jon A. Krosnick. “The Reliability of Survey Attitude Measurement: The Influence of Question and Respondent Attributes.” Sociological Methods and Research 20, no. 1 (1991): 139–181
Bohrnstedt, George W. “Reliability and Validity Assessment in Attitude Research.” In Attitude Measurement, edited by Gene F. Summers, 80–99. Chicago: Rand McNally, 1970.
Carmines, Edward G., and Richard A. Zeller. Reliability and Validity Assessment. Beverly Hills, Calif.: Sage, 1979.
Johnson, Janet Buttolph, and H.T. Reynolds. Political Science Research Methods. With Jason D. Mycoff. 6th ed.Washington, D.C.: CQ Press, 2008.
King, Gary, Robert O. Keohane, and Sidney Verba. Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton, N.J.: Princeton University Press, 1994.
Zeller, Richard A., and Edward G. Carmines. Measurement in the Social Sciences: The Link between Theory and Data. New York: Cambridge University Press, 1980.

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