QUANTITATIVE THEORY USE (Before discussing quantitative theories)

Discussing quantitative theories

journalpapers.org - Before discussing quantitative theories, it is important to understand variables and the types that are used in forming theories. A variable refers to a characteristic or attribute of an individual or an organization that can be measured or observed and that varies among the people or organization being studied.

This variance means that scores in a given situation fall into at least two mutually exclusive categories (Thompson, 2006). Psychologists prefer to use the term construct (rather than variable), which carries the connotation more of an abstract idea than a specifically defined term.

However, social scientists typically use the term variable, and it will be employed in this discussion. Variables often measured in studies include gender: age: socioeconomic status (SES): and attitudes or behaviors such as racism, social control, political power, or leadership.

Several texts provide detailed discussions about the types of variables one can use and their scales of measurement (e.g., Isaac & Michael, 1981; Keppel, 1991; Kerlinger, 1979; Thompson, 2006; Thorndike, 1997). Variables are distin¬guished by two characteristics: (a) temporal order and (b) their measure¬ment (or observation).

Temporal order means that one variable precedes another in time. Because of this time ordering, it is said that one variable affects or causes another variable: though a more accurate statement would be that one variable probably causes another.

When dealing with studies in the natu¬ral setting and with humans, researchers cannot absolutely prove cause and effect (Rosenthal & Rosnow, 1991), and social scientists now say that there is “probable causation”

Temporal order means that quantitative researchers think about variables in order from “left to right" (Punch, 2005) and order the variables in purpose statements, research questions, and visual models into the left-to-right, cause-and-effect type presentations. 

Thus, see the following: 

1.    Independent variables are those that (probably) cause, influence, or latest outcomes. They hey are also called treatment, manipulated, antecedent, or predictor variables.

2.    Dependent variables are those that depend on the independent vari¬ables, they are the outcomes or results of the influence of the independent variables. Other names for dependent variables are criterion, outcome, effect, and response variables.

3.    Intervening or mediating variables stand between the independent and dependent variables, and they mediate the effects of the independent variables on the dependent variable. For example, if students do well on a research methods test (dependent variable), results may be due to (a) their study preparation (independent variable) and/or (b) their organization of study ideas into a framework (intervening variable) that influenced their performance on the test. The mediating variable, the organization of study, stands between the independent and dependent variables in the probable causal link.

4.    Moderating variables are independent variables that affect the direc­tion and/or the strength of the relationship between independent and dependent variables (Thompson, 2006). These moderating variables are new variables constructed by a researcher by taking one variable and mul­tiplying it by another to determine the joint impact of both on the depen­dent variable (e.g., age X attitudes toward quality of life-impacting self-esteem). These variables are typically found in experiments.

5.    Two other types of variables are control variables and confounding vari­ables. Control variables play an active role in quantitative studies. These are a special type of independent variable that researchers measure because they potentially influence the dependent variable.

Researchers use statisti­cal procedures (e.g., analysis of covariance [ANCOVA]) to control for these variables. They may be demographic or personal variables (e.g., age or gender) that need to be “controlled” so that the true influence of the inde­pendent variable on the dependent can be determined.

Another type of variable, a confounding (or spurious) variable, is not actually measured or observed in a study. It exists, but its influence cannot be directly detected. Researchers comment on the influence of confounding variables after the study has been completed, because these variables may have operated to explain the relationship between the independent variable and dependent variable, but they were not or could not be easily assessed (e.g., a confound­ing variable such as discriminatory attitudes).

In the quantitative research study, variables are related to answering a research question (e.g., “How does self-esteem influence the formation of friendships among adolescents?") or to make predictions about what the researcher expects the results to show. These predictions are called hypotheses "Individual positive self-esteem expands the number of trends of adolescents.”)

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