Research Final 6

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  1. Main effects
    difference in one factor cause difference in DV
  2. Interactions
    • differences that may be unique from combinations of factors
    • different from what would be predicted from one factor alone
  3. Mixed Design
    included a between-subjects factor and a within-subjects factor
  4. nested design
    • combines between and within-subjects components
    • different levels of within-subjects IVs are included under each level of a between subjects factor
    • used to nest tasks or groups of subjects
  5. pretest-posttest design
    • differs from time series: single measure before and after
    • true experimental design, not quasi experimental designs
    • used in situations in which researcher lacks control over the assignment of participants to conditions and/or does not manipulate the caual variable of interest
  6. Non equivalent before-after design
    used when we want to make comparisons between 2 groups that we strongly suspect may differ in important ways before the experiment begins
  7. longitudinal deigns
    • 3 drawbacks...
    • researchers typically find it difficult to obtain samples of participants who agree to be studied again and again over a long period of time
    • trouble keeping track of participants: more, die
    • repeated testing can lead to practice effects
  8. Cross-sectional designs
    • cannot distinguish age-related changes from generational effects 
    • people of different ages differ not only in age, but also conditions under which their generation grew up
  9. Cohort-Sequential Design
    • Disadvantage of cross-sectional and longitudinal designs is their relative inability to determine whether factors other than age are influencing observed changes
    • C-S design combines two developmental designs and lets you evaluate degree of contribution made by factors such as generation effects
  10. Evaluating Quasi-Experimental Designs
    • consider what is required to establish that a particular variable causes changes in behavior
    • to infer causality, we must be able to show that: the presumed causal variable preceded the effect in time, the cause and the effect covary, all other alternative explanations of the results are eliminated through randomization or experimental control
    • primary weakness: degree to which they eliminate effects of extraneous variables on the results
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Research Final 6
2013-04-25 20:50:45
Research Final

Research Final 6
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