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Psych 301 Unit 1
| Question | Answer |
|---|---|
| Theory | An explanation for a phenomenon that can be falsified and that involves entities that cannot be directly observed |
| Hypothesis | A tentative statement about the possible relationship between observable variables |
| Falsifiability criterion for theories | for an explanation to be useful, it must be able to generate predictions. As such, there must be at least some hypothetical facts the theory cannot explain. |
| Modus ponens | If the theory predicts the hypothesis, and the theory is true, then the hypothesis is true. This is a valid argument but it is not useful to science because it assumes the theory is true. |
| Modus tollens | Method of disconfirming; if the theory predicts the hypothesis and the hypothesis is not true then the theory is not true. This is both valid and useful to science. Science works by using modus tollens to prove theories wrong can never prove theory true |
| Affirming the consequent | If the theory predicts the hypothesis and the hypothesis is true, the theory is true. This is invalid, logically impossible to prove a theory is true |
| Naturalistic observation | a research technique in which the researcher simply observes and describes behavior |
| Correlational approach | a research technique in which the researcher determines whether variables are related without manipulating the variables |
| When to use the correlational approach | 1. manipulating the variables would be difficult or impossible (age sex etc.) 2. manipulating the variables would be unethical |
| Independent variable | the variable for which the researcher chooses values |
| Dependent variable | the variable the researcher measures to determine the effects of the independent variable |
| Circumstances when the experimental approach cannot be used | 1. manipulating the variables would be difficult or impossible 2. manipulating the variables would be unethical |
| Causation | a causal relationship exists between two variables when a change in one results in a change in the other |
| Advantage of the experimental approach | Experiments allow us to determine if a causal relationship exists between two variables |
| Levels of the independent variable | The specific values of the independent variable the researcher chooses to use in an experiment |
| Between subjects design | Research design in which each subject is assigned to one level of the independent variable |
| Within subjects design | Research designs in which each subject is assigned to all levels of the independent variable |
| Experiment | A research technique that satisfies the following criteria: 1. Random assignment. 2. Researcher manipulates the independent variable |
| Random selection | Occurs when every member of the populations to which we would like to generalize our results has an equally likely chance to participate in the experiment |
| Random assignment | Once the participants have been chosen random assignment occurs when each participant has an equally likely chance to be assigned to each IV level (in a between subjects design) or to each treatment order (in a within subjects design). |
| Quasi-experiment | A research technique in which the researcher manipulates the IV but which does NOT have random assignment |
| Investigation | A research technique that does not allow a researcher to infer causation. Naturalistic observation, correlational approach, and Quasi-experiments are all investigations. |
| Frequency distribution | graph showing the number of times each score occurred in a data set. |
| Normal distribution | A symmetrical bell shaped distribution. Ex - height within sex. |
| Postively skewed distribution | A distribution with a few extreme high scores. Ex - annual income for adults, mean>median |
| Negatively skewed distribution | a distribution with a few extreme low scores. Ex - exam scores. mean<median |
| Mean | sum of all of the scores divided by the total number of scores. Affected far more by extreme scores than median, shows consistency across repeated samples, and easier to calculate. Inferential statistic. |
| Median | Middle score that occurs in the set. Put the scores in rank order from lowest to highest, the median is the middle score. Not effected by extreme scores, not consistent over repeated samples, difficult to calculate. Descriptive statistic. |
| Sum of Squares | SS= sigma (x-xbar)^2 |
| Standardized scores | Z scores - Allow comparisons to be made between scores measured on different scales by placing all scales on a common scale. A set of standardized scores always has mean=0 and standard deviation=1. |
| Properties of scales of measurement | 1. Identity 2. Magnitude 3. Equal intervals 4. Absolute zero |
| Identity | occurs when different entities receive different scores |
| Magnitude | occurs when the ordering of values reflects the ordering of the trait being measured |
| Equal intervals | occurs when a difference of one on the scale represents the same amount of the trait being measured everywhere on the scale |
| Absolute zero | occurs when zero on the scale represents a complete absence of the trait being measured |
| Types of scales of measurement | 1. Nominal scale 2. Ordinal scale 3. interval scale 4. ratio scale |
| Nominal scale | has only the identity property, no operations are meaningful |
| ordinal scale | has only the identity and magnitude properties, no operations are meaningful |
| interval scale | has only the identity, magnitude, and equal intervals properties; can be added and subtracted meaningfully but cannot be multiplied or divided by one another (can divide or multiply by scalar for example to get the average) |
| Ratio scale | has the identity, magnitude, equal intervals, and absolute zero properties; all operations are meaningful |
| Advantages of within subjects designs | allows the use of fewer subjects to obtain the same number of observations and allows for greater statistical power than between subjects design |
| Statistical Power | probability that will find statistical significance of effect of IV on the DV |
| Problems with within subjects designs | 1. Practice effects 2. Sensitization effects 3. carry over effects |
| Practice effects | occur when the subjects performance with the DV changes, either for better or worse as a result of experience with the experimental task |
| Counterbalancing | a method of assigning subjects to orders of IV levels so that across subjects, practice effects are balanced |
| Methods of counterbalancing | A. Use all possible treatment orders (for or fewer IV) B. Use a Latin square |
| Latin Square | (see notes on how to draw) |
| Sensitization effects | occur when subject becomes aware of the manipulations being used in a study and such awareness causes him or her to change his or her behavior |
| Carry over effects | occurs when the effect of one treatment persists when another treatment is introduced |
| Test of a single sample mean | used to compare the mean of one sample to a standard value |
| Internal validity | the extent to which your study provides a valid test of the relationship between the IV and the DV |
| Between subjects variance test | used to compare the variances from between subjects designs that have one IV with two levels |
| Between subjects T-test | used to compare the means from between subjects designs that have one IV with exactly two levels |
| Alpha | the probability of making a type I error given that our experiment finds an effect of the IV on the DV |
| Beta | the probability of making a type II error given that our experiment fails to find an effect of the IV on the DV |
| Type I error | finding an effect of the IV on the DV when in reality, no such effect exists |
| Type II error | failing to find an effect of the IV on the DV when in reality there is an effect |
| Statistical power | the probability that a given experiment will find the effect of the IV on the DV if an effect exists (1-beta) |
| Factors determining statistical power | 1. alpha level 2. effect size 3. variability in the DV 4. sample size 5. correlation between the IV levels (in within subjects designs only) |
| How to increase statistical power | 1. Choose IV levels that will maximize effect size (make the IVs as diff as possible) 2. Try to lower the variability in the data (cut down on extraneous variables) 3. increase sample size |
| Factors that can increase Type II errors | Nuisance variables floor and ceiling effects narrow range of the IV |
| Nuisance variables | any variable other than the IV that affects the DV |
| Floor and ceiling effects | occur when the values of the DV are sow low or so high that they are unlikely to be affected by the IV (test is too hard or too easy so everyone scores around the same) |
| Narrow range of the IV | occurs when the levels of the IV are so similar that their effects on the DV cannot be distinguished (100 vs 101 mg) |
| Can't prove the null hypothesis | you cannot prove that an IV has no effect on a DV |
| Factors producing type I errors | 1. Regression to the mean 2. Confounds |
| Regression to the mean | the tendency for extreme values of a variable to fall closer to the group mean when retested |
| Control group | a group of subjects in a between subjects design, that receives a treatment we know is ineffective at changing the DV, must use a control group if want to determine whether a treatment is effective |
| Confound | a nuisance variable that varies reliably with the IV |
| Types of confounds | 1. confounds due to subject assignment 2. confounds due to manipulation of the IV |
| How is the logic of experimentation ruined by confounds? | In the logic of experimentation, we presume that the IV levels differ only in the IV manipulation. If there is a confound then this logic breaks down because it is impossible to know whether it was the IV or the confound responsible for changes in the DV |
| Confounds due to subject assignment | occur when the subjects at the different IV levels differ on some variable prior (in random assignment, you have set alpha so not a valid argument if have already done subject assignment) |
| Confounds due to manipulation of the IV | occur when additional unanticipated changes accompany IV manipulation |
| Random factor | an IV whose levels were chosen randomly from a population of possible values. If a reliable effect of a random factor is found, the researcher may generalize the results to the other possible IV levels in the population |
| Fixed factor | an IV whose levels were chosen nonrandomly. Any effects of a fixed factor cannot be generalized outside the IV levels tested |
| External validity | the extent to which the results of a study apply to outside the research situation |
| Demand characteristics | Aspects of a study that indicate to subjects how they are expected to respond |
| Experimenter expectancy effect (rosenthal effect) | a demand characteristic that occurs when subjects change their behavior due to unintentional cues from the researcher |
| Placebo effects | a demand characteristic that occurs when subjects change their behavior due to their expectation that change should occur |
| Overcoming rosenthal and placebo effects | 1. Single blind experiment: an experiment in which the researcher but not the subjects knows which IV level the subject receives (overcomes placebo) 2. Double blind: an experiment in which neither the subject nor the person administering Tx knows IV (bot |
| Hawthorne effect | a demand characteristic that occurs when subjects change their behavior because they know they are being observed |
| novelty effect | occurs when the DV is being influenced by the novelty of the IV rather than any inherent quality of the IV |
| Lack of random selection | occurs when subjects are not randomly drawn from the population to which we would like to generalize |
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