Forms of Theories in Quantitative Research (Researchers state their theories in research proposals)

Forms of Theories in Quantitative Research

Researchers state their theories in research proposals in several ways, such as a series of hypotheses, if-then logic statements, or visual models. First, some researchers state theories in the form of interconnected hypoth­eses. For example, Hopkins (1964) conveyed his theory ol influence pro­cesses as a series of 15 hypotheses. Some of the hypotheses are as follows (these have been slightly altered to remove the gender-specific pronouns):

1.         The higher one’s rank, the greater one’s centrality.

2.        The greater one’s centrality, the greater one’s observability.

3.        The higher one’s rank, the greater one’s observability.

4.        The greater one’s centrality, the greater one’s conformity.

5.        The higher one’s rank, the greater one’s conformity.

6.        The greater one’s observability, the greater one’s conformity.

7.        The greater one's conformity, the greater one’s observability, (p. 51) If the frequency of interaction between two or more people increases, the degree of their liking for one another will increase, and vice versa... Persons who feel sentiments of liking for one another will express those sentiments in activities over and above the activities of the external system and these activities may further strengthen the sentiments of liking.

The more frequently persons interact with one another, the more alike in some respects both their activities and their sentiments tend to become, (pp. 112, 118,120)

Third, an author may present a theory as a visual model. It is useful to translate variables into a visual picture. Blalock (1969,1985,1991) advo­cated causal modeling and recast verbal theories into causal models so that a reader could visualize the interconnections of variables.

Two simpli­fied examples are presented here. As shown in Figure 3.1, three indepen­dent variables influence a single dependent variable, mediated by the influence of two intervening variables.

A diagram such as this one shows the possible causal sequence among variables leading to modeling through the path analysis and more advanced analyses using multiple measures of variables as found in structural equation modeling (see Kline, 1998). At an introductory level, Duncan (1985) provided useful suggestions about the notation for constructing these visual causal diagrams:

1.      Position the dependent variables on the right in the diagram and the independent variables on the left.

2.     Use one-way arrows leading from each determining variable to each variable dependent on it.

3.      Indicate the strength of the relationship among variables by inserting valence signs on the paths. Use positive or negative valences that pos­tulate or infer relationships.

4.      Use two-headed arrows connected to show unanalyzed relation­ships between variables not dependent upon other relationships in the model.

More complicated causal diagrams can be constructed with additional notation. This one portrays a basic model of limited variables, such as typically found in a survey research study.

A variation on this theme is to have independent variables in which control and experimental groups are compared on one independent variable in terms of an outcome (dependent variable). As shown in Figure 3.2, two groups on variable X are compared in terms of their influence on Y, the dependent variable. This design is a between-groups experimental design (see Chapter 8). The same rules of notation previ­ously discussed apply. 

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