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Psych Research Pt 4
Experiments, Correlations, Parametrics
| Question | Answer |
|---|---|
| Quasi-Experiment | research technique that does NOT have random assignment, but in which the researcher manipulates the IV |
| Investigations | research technique that does NOT allow researcher to infer causation; no random assignment |
| Experiment | a study that has random assignment and the researcher manipulates the IV |
| Theory | explanation for a phenomenon that can be falsified and that involves entities that cannot be directly observed |
| Hypothesis | a tentative statement about the relationship between observable variables |
| Can't Prove the Null Hypothesis | impossible to prove that the IV has no effect on the DV |
| Random Assignment | occurs when every subject has an equally likely chance to be assigned to each IV level/to each treatment order |
| Random Selection | occurs when every member of the population to which we would like to generalize the results has an equally likely chance of being chosen to participate in the study |
| Internal Validity | the extent to which your research provides a valid test of the relationship between the IV and DV |
| External Validity | the extent to which the results of your research can be applied outside the research situation |
| Alpha | the probability of making a Type I error given that our experiment found an effect of the IV on the DV |
| Beta | the probability of making a Type II error given that our experiment failed to find an effect of the IV on the DV |
| Levels of the IV | specific values of the IV that the researcher uses in a study |
| Mean v. Median | mean more affected by extreme scores but more consistent and easier to calculate |
| Frequency Distribution | a graph showing the # of times each score occurred in a data set |
| Normal Distribution | symmetrical, bell-shaped distribution |
| Positively Skewed Distribution | distribution w/ a few extreme high scores |
| Negatively Skewed Distribution | distribution w/ a few extreme low scores |
| Advantage of Experimental Approach | allows researcher to infer a causal relationship between the IV and DV |
| How is the logic of experimentation ruined by confounds? | if you have a confound in an experiment, then it is impossible to know whether it was the IV or confound that influenced the DV |
| Correlation Co-Efficient | a statistic used to indicate the direction and strength of the inner relationship between two variables measured on an interval/ratio scale |
| Possible Values of a Correlation Co-Efficient | -1 (perfect negative) to 1 (perfect positive) |
| Positive Relationship of Correlation | as one increases, the other increases |
| Negative Relationship of Correlation | as one increases, the other decreases, or vice versa |
| Why Correlation Cannot Determine Causation | 1) unclear which variable is cause and which is effect (directionality problem) 2) 3rd variable problem |
| Mathematical Limitations of Correlations | 1) poor at capturing non-linear relationships 2) greatly affected by extreme scores 3) can be lowered by range restriction |
| How to Find Main Effects in a Table or Graph | Table: average each level of the IVs - if they are the same, then no reliable ME Graph: middle of each line - if they are the same, then no reliable ME |
| How to Find Interaction in a Table or Graph | Table: subtract - if they are the same, then no reliable interaction Graph: if lines interact, then reliable interaction |
| Parametric Statistical Tests | require assumptions about the distraction of the DV in order to obtain correct p-values; symmetrically distributed |
| Advantages of Non-Parametric Statistics | less restrictive and often easier to calculate than parametric tests |
| Disadvantages of Non-Parametric Statistics | tend to be overly conservative (type II errors) and no non-parametric test exists for some research designs (ex: factorials) |
| Participant Observation | a naturalistic observation in which the researcher becomes a member of the group being observed |
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