1. Read through the key parts of the research article provided and answer the following important general questions IN YOUR OWN WORDS:
   1. What is the research study about? What is the topic or focus of the research? What is the main research question and why it is important?
   2. What data were used and what are their possible shortcomings?
   3. What is the dependent variable? What is the effect or outcome being measured in an experiment?
   4. What is the independent variable? What is doing the causing? What is being manipulated or changed?
   5. How was the research carried out? What methods were used?
   6. What are the results or findings of the research study? What does/do the researcher(s) conclude?
2. Critically analyze and evaluate the research article provided by answering our four CRITICAL QUESTIONS:
   1. What is being claimed? (Discuss any of the following that may apply)
      1. What questions does the research article address?
      2. What are the main conclusions of the research article?
      3. Why are the conclusions important?
   2. How good is the evidence? (Discuss any of the following that may apply)
      1. What evidence supports those conclusions?
      2. Do the data actually support the conclusions?
      3. What is the quality of the evidence?
      4. Does the research demonstrate causation or correlation?
      5. Was the evidence provided by an observational study (also called prospective, cohort, or sometimes even epidemiological studies), which inherently is not able to measure a cause-and-effect relationship?
      6. Was the evidence provided by research that was industry sponsored (promoted without a measure of healthy skepticism or blindly accepted and believed without any doubt or reservation)?
      7. Is the evidence of a correlation the result of statistical significance (a difference between two statistics that is likely to be true of the population as a whole—unlikely to be due to chance), but confused with clinical significance (taken as evidence that the research is showing something practically “significant” in the sense of humanly important (Battersby, p. 232, adapted))?
   3. What other information is relevant? (Discuss any of the following that may apply)
      1. What is the history, background, context of the issue?
      2. Have other researchers found similar correlations? Of similar strength?
      3. Did other researchers use different types of samples and groups?
      4. Does the argument meet the burden of proof?
      5. Is the argument consistent with the direction of previous (or other) research or evidence?
      6. If in conflict with previous (or other) research, does the argument deal effectively with opposing evidence or arguments; is it strong enough to counter this previous (or other) research?
      7. Is there relevant information that is missing?
      8. Is there emerging or manufactured coherence (or convergence) based on bandwagoning (consensus obtained by everyone coming on board without any analysis or evaluation) or prejudicial funding bias (consensus obtained by funding an experiment and introducing biased results to promote some favorable economic or political interest)?
   4. Are relevant fallacies avoided? (Discuss any of the following that may apply)
      1. Fallacies of Internal Validity (The elements of this list may not all apply to the research article. You ought to be looking at any applicable fallacy we studied in the course.)
         1. Confounding Variable (change in the dependent variable caused by a variable not included in the experimental study);
         2. Placebo effect (a non-active treatment that (for possibly psychological or unknown reasons) seems to be causing a change like an active treatment);
         3. Contamination (participants from one group communicate with participants of the other group and find out what the experiment or treatment is all about);
         4. Compensatory Behavior (participants from one group find out that the participants of the other group were given something of value and try to reduce (or compensate for) the differences);
         5. Experimenter Bias (participants change their behavior after the experimenter carelessly or unconsciously communicates (through subtle clues or nonverbal behavior) the outcome that s/he wants);
         6. Selection Bias (assignment bias, self-selection, existing groups);
         7. Demand Characteristics (participants discover what the hypothesis is and work to confirm it in order to please the experimenter);
         8. Selection by Maturation Interaction (a dependent variable at one point of testing isn’t always the same at all points in time—e.g., IQ matching of two existing groups measured or sampled to be equivalent on the dependent variable at one point of testing);
         9. Statistical Regression—given time, an extreme measure/score will move to the average/mean (e.g., a sample of subjects with either very high or low measures of depression);
         10. Mortality (differential attrition/unequal group drop-out of a study);
         11. Biological Maturation (e.g., infants mature faster than adults);
         12. History Effects (some factor out in the world that effects the dependent variable—e.g., collective anxiety produced by the 9/11 historical disaster);
         13. Testing (practice effects—experience makes the participant perform better), fatigue effects—repeated experience makes the participant tired or bored, “catching on” effects—experience helps the participant figure out the hypothesis);
         14. Instrumentation (measurement bias or error is introduced by instrumentation effects).
      2. Fallacies of External Validity (The elements of this list may not all apply to the EMPIRICAL research article. You ought to be looking at any applicable fallacy we studied in the course.)
         1. Population Validity (small unrepresentative sample);
         2. Change over Time (treatment will not work when applied to a later time period; or, treatment may work later than expected);
         3. Multiple-Treatment Interference (multiple-treatments given to participants make it difficult to separate the change associated with a single treatment);
         4. Experimenter Effect (a treatment may not work if a different experimenter delivers it);
         5. Hawthorne Effect (participants may behave differently because they are being watched);
         6. Novelty Effect (participants may or may not react to the novel nature of a treatment, rather than the actual treatment);
         7. Measurement (a treatment effect may only be observable with specific measures or types of measures).
         8. Internal Validity may exclude External Validity (Does the study have real world applications / implications?)
         9. Statistical Significance confused with Clinical Significance.

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