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Describe, predict, explain and change behavior |
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Interval, Ratio, Nominal and Ordinal |
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Is your roommate clean? yes, no (terms) |
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Ranked terms (not at all, somewhat, always) |
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Scale of 1-10 (1, not at all-extremely)No absolute zero point. |
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Roommate's cleanliness is operationalized as the # of times you've seen them clean the dorm room this week. An absolute zero point |
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Choosing subjects from the population in such a way that every member of the population of interest has an equal chance of being selected for a study. |
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Assigning subjects to the conditions of your experiment in a way that every subject has an equal chance of being assigned to any condition. |
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The boundaries of intervals for scores represented on a continuous number line. |
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Frequency Distributions help... |
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1. Tell you if scores are high or low 2. If scores are spread out or clustered 3. Where one's scores is relative to all other scores |
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Obtaining estimates of the intermediate values in a a frequency distribution table. |
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How consistently does this measure what I want to measure? |
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How well is this variable operationalized? |
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On the face of it, does this measure reflect the construct? |
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Criterion-oriented Validity |
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predictive, concurrent, convergent, discriminant. |
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Predictive Validity (Example) |
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Do GRE's predict success in grad school? |
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Do scores on a measure relate to a particular criterion/expectation for behavior? (2 tests administered at same time) |
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Do 2 operational definitions of the same construct relate to one another (at different times or studies)? |
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Is the construct related to something it shouldn't be related to? |
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Goal: To describe and understand particular social environments where people interact. |
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Most frequently occurring score |
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Middle score in distribution |
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Average of scores in a distribution |
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Gives level of confidence that true population value lies within the interval around the result generated using your sample. |
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Every member of population has equal probability of being selected for the sample |
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Stratified Random Sampling |
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-Population divided into subgroups -Random sampling within each subgroup |
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Random set of clusters of individuals identified |
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8 Threats to Internal Validity |
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History, Maturation, Regression to the mean, Testing Effects, Heterogeneous Attrition, Participant Reaction Biases, Experimenter Bias, Confounds |
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Did the thing you manipulated (independent variable) actually affect the thing you measured (dependent variable)? |
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Do your results generalize to the population your sample was supposed to represent? |
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Specific events that happen during the study can affect the variable being measured |
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Changes that happen when individuals as a function of time passing can affect the variable being measured |
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The tendency for people get high scores on a particular measure to score closer to the mean if they are tested again (and vice versa) |
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People tend to do better on a test the 2nd time they take it |
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When different amounts of people or different types of people drop out of the conditions of the experiment |
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Participant Reaction Bias |
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Subjects try to behave in ways that are consistent with or the opposite of the researcher's hypothesis |
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Experimenters' expectancies, can affect either how they act with subjects or what they observe |
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Some additional variable varies systematically along with the thing you manipulated; an alternative explanation |
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The degree to which scores in a distribution vary from one another, defined in terms of distance |
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Forms a standardized distribution that can be directly compared to other distributions |
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Standard normal distribution |
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In a 3 x 3 factorial design, there are... |
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2 main effects, 1 interaction effect and 9 conditions possible |
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