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Measurement

Key Terms

? ²âÁ¿ £¨Measurement£© ¸ÅÄ£¨Conceptualization£© ? ²Ù×÷»¯£¨Operationalization£© ά¶È£¨Dimension£© ? Ö¸±ê£¨Indicator£© Á¬Ðø±äÁ¿ £¨ Continuous Variables £© ? ÀëÉ¢±äÁ¿£¨ Discrete Variables £© ¶¨Àà²âÁ¿ £¨Nominal Measurement) ? ¶¨Ðò²âÁ¿£¨Ordinal Measurement£© ¶¨¾à²âÁ¿£¨Interval Measurement£© ? ¶¨±È²âÁ¿£¨Ratio Measurement£© Introduction

Measurement means careful, deliberate observations of the real world for the purpose of describing objects and events in terms of the attributes composing a variable. We could have quantitative measurement and qualitative measurement. However, most measurement in social research is qualitative measurement. Conceptualization and Operationalization

Conceptualization is the mental process whereby fuzzy and imprecise notions (concepts) are made more specific and precise. A conceptual definition is a definition in abstract, theoretical terms. It refers to other ideas or constructs. There is no magical way to turn a construct into a precise conceptual definition. It involves thinking carefully, observing directly, consulting with others, reading what others have said, and trying possible definitions. Conceptualization is the refinement and specification of abstract concepts, and operationalization is the development of specific research procedures that will result in empirical observations representing those concepts in the real world.

Operationalization, the process of specifying exact meaning, involves describing the indicators we¡¯ll be using to measure our concept and the different aspects of the concept, called dimension. Social scientists, then, can measure anything that¡¯s real; through conceptualization and operationalization, they can even do a pretty good job of measuring things that¡¯s aren¡¯t. Measurement

Levels of measurement is an abstract but important and widely used idea. It states that some measures are at a higher or more refined level, and others are crude or less precisely specified. Continuous variables have an infinite number of values or attributes that flow along a continuum. The values can be divided into many smaller increments; in mathematical theory, there is an infinite number of increment.

Discrete variables have a relatively fixed set of separate values or variable attributes. Instead of a smooth continuum of values, discrete variables contain distinct categories. The appropriate level of measurement for a variable depends on (1) how a construct is conceptualized and (2) the type of indicator or measurement that a researcher uses. The level of measure limits the statistical measures can be used. A wide range of powerful statistical procedures are available for the higher levels of measurement, but the types of statistics that can be used with the lowest levels are very limited.

Comparing two people in terms of a ratio variable, then, allows us to conclude (1) whether they are different( or the same), (2)whether one is more than the other, (3) how much they differ, and (4)what the ratio of one to another is.

Certain quantitative analysis techniques require variables that meet certain minimum levels of measurement.

More precisely, you should anticipate drawing research conclusions appropriate to the levels of measurement used in your variables. Discrete variables are nominal and ordinal, whereas continuous variables can be measured at the interval and ratio level. A ratio-level measure can be turned into an interval, ordinal, or nominal level.

The level of measurement you will seek, then, is determined by the analytical uses you¡¯ve planned for a given variable. Keeping in mind that some variables are inherently limited to a certain level. If a variable is to be used in a variety of ways, requiring different levels of measurement, the study should be designed to achieve the highest level required. Again, you need not necessarily measure variables at their highest level of measurement. In general, if it is necessary to use ordinal measurement, use at least five ordinal categories and obtain many observations.

Reliability and validity are central issues in all measurement. Both concern connecting measures to constructs. Reliability and validity are salient because constructs are often ambiguous, diffuse, and not directly observable. Perfect reliability and validity are virtually impossible to achieve. Rather, they are ideals researchers strive for

In the abstract, reliability is a matter of whether a particular technique, applied repeatedly to the same object, yields the same result each time. In conventional usage, validity refers to the extent to which an empirical measure adequately reflects the real meaning of the concept under consideration.

If you think of measurement as analogous to repeatedly shooting at the bull¡¯s-eye on a target, you¡¯ll see that reliability looks like a ¡°tight pattern¡±, regardless of where the shots hit, because reliability is a function of consistency. Validity, on the other hand, is a function of shots being arranged around the bull¡¯s eye. The failure of reliability in the figure is randomly distributed around the target; the failure of validity is systematically off the mark.

Clearly, we want our measures to be both reliable and valid. However, a tension often arises between the criteria of reliability and validity, forcing a trade-off between the two. To some extent, this dilemma explains the persistence of two quite different approaches to social research: quantitative techniques such as surveys and experiments on the one hand, and qualitative methods such as field research and historical studies on the other. In the simplest generalization, the former methods tend to be more reliable, the latter more valid.