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# Formulating a Hypothesis

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### Formulating a Hypothesis

1. 1. Formulate Hypothesis
2. 2. Introduction to the Scientific Method Use conclusions to develop a new hypothesis
3. 3. Variables• Variables are the building blocks of hypotheses that are held together by the “glue” of the relationship we are studying
4. 4. Variables and Variable Values Variables Variable Values• Types of Beer • Sam Adams, Bud, Corona• Hair Color • Blonde, Black, Brown, Red • A-E• Grades • 85, 101, 124, 199 (Dr.• IQ (As measured by the Dodge’s) Weschler) • 0-252• Attitudes towards People with Disabilities (As measured by the Modified Issues in Disability Scale)
5. 5. Understanding variables in light of their research use.• There are three characteristics of variables that are necessary considerations in most research; they are: – A. definition, – B. function, and – C. type of measurement (i.e., measurement scale)
6. 6. Variables: Definitions• An operational definition “assigns meaning to a construct or a variable by specifying the activities or “operations” necessary to measure it...It is a specification of the activities of the researcher in measuring the variable or manipulating it.• Types of operational definitions are: – (a) measured, “which describes how a variable will be measured” and includes the source of the data (e.g., a specific standardized instrument or author developed questionnaire) – (b) experimental, which “spells out the details of the investigators manipulation of the variable” (e.g., the specific details and procedures of the intervention or treatment).
7. 7. Variables: Definitions Cont.• Hypothesis: Rewards increase punctuality.• The variables are rewards and punctuality.• A definition of rewards might be: Giving out candy and soda during the first five minutes of class. Depending on the design, this might be an experimental definition.• A definition of punctuality could be the number of minutes after 2:00 that the person arrived as recorded by the class timekeeper.
8. 8. Variables: Functions• Variables have different functions. These functions are most frequently related to – (a) presumed causality and to – (b) the purposes of the inquiry.
9. 9. Presumed Causality• A. Variable functions related to presumed causality include independent and dependent. – Independent variable: “…is the factor that is manipulated or controlled by the researcher” – A variable that is “independent of the outcome being measured. More specifically…[it is] what causes or influences the outcome”. • Note that classification variables can also be independent variables. • Also referred to as Explanatory Variables
10. 10. Variables: Function Cont.– Dependent variable: “is a measure of the effect (if any) of the independent variable • The term dependent implies “it is influenced by the independent variable. • Response variable or output. The factor that is observed or measured to determine the effect of the independent variable. • Dependent Variables are also referred to as Outcome Variables
11. 11. Variables: Function Cont.• B. Variable functions related to the purposes of inquiry. – We introduce control variables to remove their influence from the relationship of the other variables,
12. 12. Variables: Measurement Scales• There are two different scales for measurement of variables. 1. Variables can be: continuous or categorical AND 2. Variables can be nominal, ordinal, interval, or ratio
13. 13. Variables: Measurement Scales Cont. 1. Continuous or Categorical – Continuous variables have an ordered set of values within a certain range. Values between two points (e.g., 4 and 5) on the range actually mean something. In other words, if a person scored 4.5, they scored more than someone who scored 4 and less than someone who scored 5. – Categorical variables (i.e., discrete variables) are measured in categories. An observation is either in a category or it isnt. There is no meaningful “in between” option. For example, cars might be categorized as domestic or imported. Categories must be mutually exclusive and exhaustive.
14. 14. Variables: Measurement Scales Cont. 1. Nominal, Ordinal, Interval, or Ratio – Nominal: Names, classes, or symbols designating unique characteristics - simple classification, no order. – Ordinal: Assignment of numbers of symbols indicates order of relationship. Order only is indicated; there is no indication of amount. For example if an ordinal scale used the numbers from 1 to 6, one could say that 6 was greater that 3, but one could not say that it was twice the value of 3. Further the value of 4.5 would have no meaning in such a scale. Rank order data is an example of ordinal data.
15. 15. Variables: Measurement Scales Cont. – Interval: This type of data has the same ordering properties as ordinal data and it also has equal, meaningful intervals and an arbitrary zero point. Therefore in an interval scale, 4.5 would be meaningful. – Ratio: This type of data has the same properties as interval data and also has an absolute zero point. In a ratio scale, 6 would be twice as much as 3.
16. 16. Variables: Measurement Scales Cont.• Relating the Two Scales• Categorical: Nominal (Ordinal?)• Continuous: (Ordinal?) Interval and Ratio• When planning data collection, ALWAYS TRY TO COLLECT DATA IN CONTINUOUS FORM (unless it really confounds your collection strategy). CONTINUOUS DATA CAN ALWAYS BE CATEGORIZED LATER IF DESIRED FOR ANALYSIS, BUT CATEGORICAL DATA CANNOT BE READILY TRANSFORMED INTO CONTINUOUS.• For example, instead of asking people to mark one of six age categories, one could simply ask their date of birth. So, why do we care about scales? Among other reasons, scales determine the type of statistics that can be used. Parametric statistics are only appropriate with interval or ratio data. Nonparametric statistics must be used with nominal and ordinal data.
17. 17. Levels of Variables Two Group ComparisonsTreatment Group Control Group (Exercise) (No Exercise)
18. 18. Levels and Factors• The most basic experimental design has two variables – Independent Variable – Dependent Variable• The independent variable has two Levels – Experimental Group (Usually receives treatment) – Control Group (Usually does not receive treatment) – A study can also have two different amounts of an independent variable• Example: A Randomized and Controlled study looking at the effects of exercise (Independent) on body fat (Dependent) – Group 1 exercises 3 times a week for 6 weeks – Group 2 does not exercise at all for three weeks Researchers will compare the body fat of those who exercise to those who do not.
19. 19. Levels and Factors Cont.• A grouping variable is called a “Factor”• The number of groups are called “Levels”• A 2 level variable design can be expanded to include as many levels as needed!
20. 20. Levels and Factors Cont. (4 Level Factor)Treatment 1 Treatment 2Treatment 3 Control
21. 21. Hypotheses andOperationalisation
22. 22. Operationalisation• The process of making a concept measurable
23. 23. Questions, operationalisation1. How could you make intelligence measurable?2. How could you make aggression measurable?
24. 24. Experimental hypothesis• Predicts differences in the measure of the dependent variable between the various conditions of the independent variable• 2-tailed hypothesis: Only predict a difference• 1-tailed hypothesis: Predict a particular direction in the difference (i.e. One group/condition will have a higher or lower score)
25. 25. Two tailed hypothesis• (Two tailed) There will be a difference in [the D.V.] between [condition A of the I.V.] and [condition B of the I.V.]• (Two-tailed) There will be a difference in I.Q. Scores between male subjects and female subjects
26. 26. One-tailed hypothesis• (One-tailed) There will be a decrease/increase in [the DV] in [condition A of the IV] compared to [condition B of the IV]• (One-tailed) There will be an increase in I.Q. Scores in female subjects than in male subjects.
27. 27. Null hypothesis• To be scientific every experimental hypothesis must be capable of being proven to be wrong. For this reason a null hypothesis is always proposed along with the experimental hypothesis• The null hypothesis states that there will be no significant difference between conditions/groups
28. 28. Example, null hypotheses(Two tailed) There will be no difference in I.Q. scores between male subjects and female subjects.(One tailed) There will be no increase/decrease in I.Q. scores between male subjects and female subjects.
29. 29. Accepting/rejecting null and experimental hypothesis• If there is a significant difference between the conditions/groups, the experimental hypothesis is accepted and the null hypothesis is rejected• If there is no significant difference between conditions/groups, the experimental hypothesis is rejected and the null hypothesis is accepted
30. 30. Questions, one-tailed, two-tailed and null hypotheses1. Based on the result of the Bandura study, do you think we should reject or accept our experimental hypothesis?2. Some studies have failed to find an effect of antidepressants on mood compared to placebo groups. For these studies, should the null hypothesis be rejected or accepted?