Topics 1 & 2
Defining concepts and specifying differences

Topic 3
Significance of problem

Topic 4
Feasibility

Topic 5
Judging quality

Topic 6
Types of hypotheses

Topic 7
Study variables
Activity #1

Topic 8
Definitions of variables
Activity #2

Week 3 Assignment

References

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TOPIC 6:
Types of research hypotheses

 Objectives 6: Differentiate between the types of hypotheses (simple versus complex, non-directional versus directional, associative versus causal, and statistical versus research).

Research hypotheses identify the:

• Number of variables included in the study

• Type of hypothesized relationship, of which there are four categories:

• Associative versus causal hypothesized relationship-
• Associative relationships involve variables that change together. Associative relationships are also directional or nondirectional. For example:
• X is related to (r/t) Y and Z - predicts a relationship
• As X increases, Y increases - predicts a positive relationship (because they vary in the same direction)
• As X decreases, Y decreases - predicts a positive relationship (because they vary in the same direction)
• As X increases, Y decreases, or as X decreases, Y increases - predicts a negative relationship (because they vary in the opposite directions)
• Causal relationships predict that one variable, the independent variable, causes another variable, which is the dependent variable.
• The independent variable, also called the treatment or experimental variable, is manipulated by the researcher to cause an effect on the dependent variable
• The dependent variable, also called the outcome or criterion variable, is measured before and after manipulation of the independent variable to estimate the effect of the independent variable.
• Example - Compared to non-participants, subjects participating in a program of meditation (independent variable) will report less perceived stress (dependent variable).

• Simple versus complex hypothesized relationship -
• A simple hypothesis states the relationship (associative versus causal) between two variables, the independent variable and the dependent variable.
• A complex hypothesis predicts the relationship between three or more variables. The variables may include:
• A single independent variable and two or more dependent variables.
• Example - Compared to non-participants, subjects participating in a program of meditation (independent variable) will report less perceived stress (a dependent variable) and will have lower blood pressure readings (a dependent variable)
• Two or more independent variables and one, or more, dependent variable(s).
• Example -Compared to non-participants, subjects participating in a program of meditation and/or a program of regular physical activity (two independent variables) will report less perceived stress (a dependent variable) and will have lower blood pressure readings (a dependent variable).

• Directional versus nondirectional hypothesized relationship -
• A directional hypothesis is also an associative hypothesis, and states the direction of the predicted relationship
• Positive
• As X increases, Y increases
• As X decreases, Y decreases
• Negative
• As X increases, Y decreases
• As X decreases, Y increases
• A nondirectional hypothesis predicts that there will be a relationship, but this type hypothesis does not predict the direction.
• X is related to Y
• All causal hypotheses predict the direction of the relationship. The direction is usually apparent by the use of terms such as less, more, increase, decrease, greater, higher, lower, etc.
• An example -Compared to non-participants, subjects participating in a program of meditation, twice a week or four times a week (two independent variables - twice a week meditation or four times a week) will report less perceived stress (a dependent variable) and will have lower blood pressure readings (a dependent variable).

• Null versus research hypothesis
• The null hypothesis (Ho) is also called the statistical hypothesis.
• It states that there is no relationship between variables.
• It is used for statistical testing and interpreting the results of statistical analyses.
• It can be simple or complex and associative or causal.
• It may not always be stated, but it is implied, because it is the opposite of the research hypothesis.
• An example - There is no difference in measures of perceived stress (a dependent variable) and blood pressure (a dependent variable) between subjects who do and do not participate in a program of meditation (independent variable).
• The research hypothesis (HA or H1) is also called the alternative hypothesis, since it is the opposite of the null hypothesis.
• It states that there is a relationship between variables.
• It can be simple or complex, associative or causal, and directional or nondirectional.
• There are several examples of research hypotheses in the preceding illustrations.