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)
journalpapers.org- 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.
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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