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In order to conduct any research study, some form of measurement must be used. The most common forms of measurement used in park recreation and leisure services research are explained below.

Variable: Any characteristic that can take have more than one form or value. Examples of a variable would be gender and height. Gender has two groups or variables, height has numerous groups or variables.

Measurement: The assignment of numbers on the basis of variation.

Score: Means a number.

Three Types of Variables Independent Dependent Control

Independent Variables

Independent variables are also described as predictor, input, manipulated, treatment, stimulus, intervention, experimental, or moderating variables. The independent variable is presumed to cause, affect, or influence the outcome measures of research study.

The research method creates a situation where subjects are exposed to a condition or problem, which is called the independent measure. The subject's response to the condition or problem is the dependent variable.

An example is a study to determine whether age, sex, and occupation (independent variables) positively or negatively affect attitudes toward leisure time (dependent variable).

Dependent Variables

Dependent variables represent the effect or influence of the independent variable. They are sometimes referred to as outcome, output, or response variables. They are "dependent" in that the outcome depends on the effects of the variables being manipulated.

Control Variables

Control variables are also called background or classification variables because they need to be controlled, held constant, or randomized so that their effects are neutralized or accounted for during the study.

Extraneous variables can be controlled or ruled out using the following processes:

1. Random assignment of subjects,

2. The ability to manipulate the instrument or test,

3. The time when the measurements of the dependent variable occur ( randomized data collection times are necessary), and

4. Which groups are measured.

Scales or Levels of Measurement

The scales of measurement are listed below from the least to the most matching with the real number system. Nominal Ordinal Interval Ratio

Numbers mean different things in different situations. Numbers are assigned to objects according to rules. You need to distinguish clearly between the thing you are interested in and the number that symbolizes or stands for the thing. For example, you have had lots of experience with the numbers 2 and 4. You can state immediately that 4 is twice as much as 2. That statement is correct if you are dealing with numbers themselves, but it may or may not be true when those numbers are symbols for things.

The statement is true if the numbers refer to apples; four apples are twice as many as two apples. However, the statement is not true if the numbers refer to the order that runners finish in a race. Fourth place is not twice anything in relation to second place-not twice as slow or twice as far behind the first-place runner. The point is that the numbers 2 and 4 are used to refer to both apples and finish places in a race, but the numbers mean different things in those two situations.

Nominal Level

The nominal scale, is not a scale at all in the usual sense. In the nominal scale, numbers are used simply as names and have no real quantitative value. It is the scale used for qualitative variables. Numerals on sports uniforms are an example; here, 45 is different from 32, but that is about all we can say. The person represented by 45 is not "more than" the person represented by 32, and certainly it would be meaningless to try to add 45 and 32.

Designating different colors, different sexes, or different political parties by numbers will produce nominal scales. With a nominal scale, you can even reassign the numbers and still maintain the original meaning, which is only that things with different numbers are different. Of course, all things that are alike have the same number.

Ordinal Level

The ordinal scale, has the characteristic of the nominal scale (different numbers mean different things) plus the characteristic of indicating "greater than" or "less than." In the ordinal scale, the object with the number 3 has less or more of something than the object with the number 5. Finish places in a race are an example of an ordinal scale. The runners finish in rank order, with "1" assigned to the winner, "2" to the runner-up, and so on. Here, 1 means less time than 2. Other examples of ordinal scales are house numbers.

Interval Level

The interval scale has properties of both the ordinal and nominal scales, plus the additional property that intervals between the numbers are equal. "Equal interval" means that the distance between the things represented by "2" and "3" is the same as the distance between the things represented by "3" and "4."

 The centigrade thermometer is based on an interval scale. The difference in temperature between 10° and 20° is the same as the difference between 40° and 50°. The centigrade thermometer, like all interval scales, has an arbitrary zero point. With interval data, we have one restriction: we may not make simple ratio statements. We may not say that 100° is twice as hot as 50° or that a person with an IQ of 60 is half as intelligent as a person with an IQ of 120. In park, recreation and leisure services, the most common form of interval measurement is a Likert Scale.

Ratio Level

The ratio scale has all the characteristics of the nominal, ordinal, and interval scales, plus one: it has a true zero point, which indicates a complete absence of the thing measured. On a ratio scale, zero means "none." Height, weight, and time are measured with ratio scales. Zero height, zero weight, and zero time mean that no amount of these variables is present. With a true zero point, you can make ratio statements like "16 kilograms is four times heavier than 4 kilograms." VALIDITY AND RELIABILITY

Researchers depend on various types of validity to verify the effectiveness of measurement procedures used in the research methodology.

Five Types of Validity External Content Face Construct Criterion

External Validity

External validity refers to the extent that a research finding can be generalized beyond the research condition to a larger population.

Content Validity

Content validity deals with the accuracy with which an instrument measures the factors or situations under study. For example, if the "content" being elicited is familiarity with a certain area of knowledge, then content validity is an estimate of how accurately the questions asked tend to elicit the information sought. This is the most common form of validation used in leisure research.

Face Validity

Face validity is based upon the subjective judgment of the researcher or a panel of experts. It involves two questions the researcher must answer:

(1) Is the instrument measuring what it is supposed to measure?

(2) Is the sample adequately representative of the behavior or trait being measured?

Construct Validity

A construct is any concept, such as honesty, that cannot be directly observed or isolated. Construct validation pertains to the degree to which the construct itself is actually designed to be measured.

A procedure has been developed by Campbell and Fisk (1959) known as the Multitrait-Multimethod Matrix Method. It makes use of the traits of convergence and discriminability.

Convergence examines the effect of various methods of measuring a construct. Different methods of measurement of the same construct should "converge" in their results.

Discriminability means that the measuring instrument should be able to discriminate, or differentiate, the construct being studied from other, similar constructs.

Criterion Validity

Criterion validity usually employs two measures of validity; the second is a check against the accuracy of the first measure. The essential component in criterion validity is a reliable and valid criterion - a standard against which to gauge the results of the instrument that is doing the measuring. The data of the measuring instrument (e.g., test scores) should correlate highly with equivalent data of the criterion scores. Specifically, does the individual test score predict the probable behavior on a second variable (criterion-related measure)? Reliability

Research should be valid and reliable. Reliability is based on the repeatability or replicability of results. The results of a research study should be the same when repeated with a similar setting and subjects. The three types of reliability checks are: Test-Retest Reliability Alternative or Equivalent Form Reliability Internal Comparison or Consistency Reliability

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