1. Define measurement, and, in so doing, explicate the meaning and significance of numerals, assignments, and rules.

Measurement refers to the procedure that entails assigning numbers or symbols to a characteristic of an object by following the prescribed set of rules (Frankfort-Nachmias & Nachmias, 2015). Concepts used in the definition of measurement include assignments, numerals, and rules. Numerals entail symbols that are of the form such as 1, 2, 3…or I, II, III. Most individuals assume that a numeral is a number; we should understand that numerals do not have specific quantitative meaning. Therefore, the responsibility of the researcher is to give it a meaning. Numerals are useful in identifying persons, phenomena, countries or objects (Frankfort-Nachmias & Nachmias, 2015). Moreover, it is possible to arrive at numbers by using numerals to designate exact quantitative meaning. The assignment is a task; however, when representing economic or political phenomena, it is part of the process. With rules, the researcher must follow certain guidelines when assigning numbers or numerals to a phenomenon (Frankfort-Nachmias & Nachmias, 2015). For instance, a rule might include “assign a numeral to a person.”

1. Explain the significance of isomorphism in understanding measurement.

Isomorphism entails the similarity of the structure of one organization to another (Frankfort-Nachmias & Nachmias, 2015). We can determine the isomorphism of a measurement procedure by not only considering the extent of congruence between the numerical system involved and empirical reality, but we should also consider the concept being measured.

1. What is an indicator? Give examples. How are indicators selected?

In most cases, scientists do not use actual concepts rather they rely on using different and diverse indicators of concepts. Complete ideas include hostility, power, motivation and democracy; however, researchers cannot observe such concepts directly (Frankfort-Nachmias & Nachmias, 2015). Therefore, researchers must measure the empirical, observable behaviors to determine the degree of the presence of these concepts.

1. What is a measurement error?

Perfect measures portray the real differences that exist in properties. Measure indicate illusory differences and real difference since they are rarely perfect (Frankfort-Nachmias & Nachmias, 2015). Therefore, measurement errors refer to the differences in scores as a result of anything other than real differences.

1. Define and explain the four levels of measurement.

Levels of measurement include interval, nominal, ration and ordinal. First, nominal level entails using names, labels, numbers or symbols to classify observations or objects. Equal phenomena are classified in one category (Frankfort-Nachmias & Nachmias, 2015). Therefore, the reason phenomena are in a different category is that they are not equal. Measurement solely functions to classify objects and phenomena.

A researcher can measure variables at the ordinal level when they portray some relation to each other. Similarly, at the ordinal level, variables can be ordered from the highest to the lowest. The symbols used to designate relations of variables include > (greater than) and <(less than) (Frankfort-Nachmias & Nachmias, 2015). These symbols provide the degree within the classification.

With interval level, the distance between variables does have a meaning. The distance between attributes represents fixed measurements.

Regarding the ratio level, one can construct a meaningful fraction since there is an absolute zero. An example of a ratio variables is weight (Frankfort-Nachmias & Nachmias, 2015). Moreover, the other characteristic of ratio level is that of internal equivalence within a category. This relation implies that one variable can be smaller or greater than another.

1. Define validity and discuss the three basic kinds of validity.

Validity refers to the degree to which a measurement tool exhibits accuracy by measuring what it claims to measure (Frankfort-Nachmias & Nachmias, 2015). The nature of variables of study results in the problem of the validity. The basic kinds of validity include the following.

First, content validity implies that the tool of measurement does not leave out anything that is relevant to the phenomena under study since all variables being measured are covered.

Second, empirical validity describes the association between a measuring tool and a measured outcome (Frankfort-Nachmias & Nachmias, 2015). For instance, one may compare the student’s score on a test with his/her school grade.

Construct validity encompasses the extent to which a researcher can legitimately make inferences from the operationalization in the investigation to the theoretical constructs of which the study was based on establishing whether the measuring instrument is logical and empirically relates to the concepts being used.

1. What are the similarities and differences between face validity and sampling validity?

Face validity entails the extent to which an assessment appear to do what it intends to measure. The investigator performs a subject evaluation to determine the appropriateness of the tool for measuring the concept.

With sampling validity, the researcher creates an exhaustive list (Frankfort-Nachmias & Nachmias, 2015). The list includes a population of the concepts under investigation.

1. Define empirical validity and explain the technique of predictive validity.

Empirical validity describes the association between a measuring tool and a measured outcome (Frankfort-Nachmias & Nachmias, 2015). The assumption of scientists is that with a valid measuring instrument, the outcome due to the use of the tool and the association among the measured variables should be identical.

When estimating predictive validity, researchers assess the anticipated results against an external criterion. Researchers then compare the outcome of their measuring instruments with those from other instruments with respect to the external criterion.

1. Define and discuss reliability. Provide illustrative examples.

Reliability is defined as the degree to which a system or measuring tool consistently perform the required function (Frankfort-Nachmias & Nachmias, 2015). Variable errors are detected when performing measuring procedures, and they tend to occur inconsistently between observations. For instance, if you use a ruler to measure the length of a chair at two points in time and the results seem slightly different, then the measuring instrument has produced variable errors.

1. Explain the concept of generalizability. Provide illustrative examples.

The concept refers to the degree to which findings from a research based on a sample can be said to represent those from the natural settings (Frankfort-Nachmias & Nachmias, 2015). For instance, when determining the percentage of people who supports Democratic Party in the U.S., the researcher will have to survey people who represent the population as a whole.

Reference

Frankfort-Nachmias, C., & Nachmias, D. (2015). Research methods in the social sciences (8th ed.). New York, NY: Worth Publishers, a Macmillan Education Company.

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