Understanding and Predicting Variables in Research

1. Chapter 2: The Research

2. Understanding and Prediction Hypothesis=A tentative statement about two or more variables-a tentative statement about how things work (Students that eat breakfast perform better in school-what are the two variables here?) - an educated guess.

3. RESEARCH Applied Research=has clear, practical application; goal to control positive/negative situations • If it is found that students who eat breakfast perform better, schools will initiate a breakfast program Basic Research=questions of interest that may not have immediate, real world application • How does anxiety affect people’s desire to be with others (affiliation need)? • Do different cultures react differently to stress?

4. THEORY • If we observe a relationship between breakfast eaters and performance we formulate a theory • Theory=predicts behavior or events- only change as new information available - more permanent-have considerable facts to support it.

5. Unit 2 Review-this info. Will help determine type of study to do longitudinal study same people over a long period disadvantage=expensive; drop out advantage=same cohort, less confounding variables cross sectional study different cohorts at the same time and compare them, less expensive and less time; disadvantage=different cohorts Survey- questionnaire (can study more people, study things not ethical through experiment) case study- in depth study of an individual (used for rare occurrences of events/illnesses experimental study-manipulation of Independent variable (experimental group receives the treatment/control group does not) to see it’s effect on the dependent variable If you can manipulate the variables, then CAN do an experiment

6. Experimental Research:Looking for Causes • Experiment = manipulation of one variable under controlled conditions so that resulting changes in another variable can be observed (feeding one group of students) • Detection of cause-and-effect relationships Variable=any measurable conditions, controlled or observed in a study • Independent variable (IV)= variable manipulated (food) to see its effect on • Dependent variable (DV)= variable measured and affected by manipulation of IV (school performance) • How does IV (food) affect DV(performance)? Operational definitionsprecisely define each variable(IV-breakfast, DV-school performance)-required for good experiment most needed aspect of a study- so study can be refuted or verifiedthrough REPLICATIOIN (makes study scientific)

7. Independent and Dependent Variables-Hypotheses • Riding the bus to school (IV) makes students more intelligent(DV) • Kids who view aggressive cartoons(IV) are more likely to act aggressively(DV) • AP Psychology students who eat chocolate(IV) perform better on vocabulary tests (DV)

8. Operational Definitions=clearly defining independent/independent variables for replication Children (Male/female, ages 4 – 6) who viewaggressive cartoons = , viewing all of Sponge Bob, episode 5, while alone in….. are more likely to act aggressively = placed on the playground for 30 minutes with 10 children who did not view cartoon, five minutes after cartoon was shown and strikes another child

9. Experimental and Control Groups:The Logic of the Scientific Method • Experimental group (exposed to manipulation of independent variable-chocolate given) • Control group(similar subjects but does not receive IV manipulation given to the experimental group-no chocolate given) • EVERYTHING ELSE FOR THESE TWO GROUPS MUST BE THE SAME and Resulting differences in the two groups must be due to the independent variable and not to Extraneous and confounding variables • Levels of independent variable =1.chocolate verses not getting chocolate; 2. also two independent variables=chocolate and breakfast, verses none for Control Group

10. The Scientific Method: Terminology Population=animals or people from which a sample is drawn (all AP students in Broward) and researchers want to generalize about Participants or subjects=organisms whose behavior observed in a study Sample=subjects from the population (AP psych students selected from all schools in Broward County) Random sampling (choosing randomly)-all in population have equal chance of selection if not it is example of Sampling Bias Representative and Random Sample is only way to generalize results to population Random assignment-participants have equal chance of placement in control or experimental groups; lessons confounding variables

11. Figure 2.16 STATIFIED RANDOM SAMPLE

12. Unit 2 Review Descriptive statistics describes data– mean, mode , median, standard deviation Measures of central tendency= typical or average score in a distribution • Mean: arithmetic average of scores • Median: score falling in the exact center, or the average of the two center scores • Mode: most frequently occurring score mean is most useful measure of central tendency except whenOutliers = mean distorted by extreme scores statistical inference-conclusions drawn about the relationship between Independent & Dependent variables, from a sample to entire population

13. Figure 2.11 Measures of central tendency

14. Descriptive Statistics: Correlation • When two variables are related to each other, they are correlated. • Correlation Coefficient = relationship between two variables • How well does A predict B? • Strength of the correlation -1.0 to +1.0 positive correlation-as one variable increases, so does the other ; as one variable decreases, so does the other negative correlation-one variable increases the other decreases

15. Figure 2.14 Interpreting correlation coefficients

16. Correlation

17. Correlation

18. Positive Correlation

19. Correlation

20. Correlation

21. Correlation

22. Scatter plot =best way to show relationship between variables Hours Spent Watching Television per Day & GPA PERSONHRS GPA 1 0.5 3.50 2 1 3.75 3 2 4.00 4 2.5 2.75 5 3 2.75 6 3.5 1.75 7 4.5 2.25 8 5 1.50 9 5 2.50 • 7 1.00 Which statistic approximates the relationship between the variables? 50% N=20 N=10 r=--- .90 r=.50

23. Unit 2 Review Innormal curve-distribution of scores-68% fall within 1 SD above/below the mean percentile scores – the same as or better than 72% of population/test takers

24. Describing DataMeasures of Variability-how scores vary from the center • Normal Curve (bell shaped)

25. Descriptive Statistics: Variability= how much scores vary from each other and from mean (see the normal curve or bell curve pp 63-64 or 66-Barrons) • Standard deviation = how far scores are from the mean/average; for the Normal curve with IQ, one standard deviation is 15 points from the mean If scores deviate 10 points, curve by 10 points, how far will scores deviate from mean??? Innormal curve-distribution of scores-68% fall within 1 SD above/below the mean -Range=distance between highest and lowest scores in data set

26. Figure 2.12 Variability and the standard deviation

27. Z scores measure the distance of a score from the mean (either - or +); a z score of -1 is 15 points below the mean, -2 is 30 points below meanPercentile scores – the same as or better than 72% of population/test takers38th percentile=youdid the same or better than 38 percent of people who took a test

28. Distribution of Scores-Bell Curve • Symmetrical distribution-see p. 65 in Barron’s • Positively skewed (curve to Left)-more low scores than high • Negatively skewed (curve to Right)-more high scores than low If Negative skewed due to test scores, can only assume????

29. Experimental Research: • Replication=repeat a study to see if earlier results are duplicated (this is why the operational definitionsare important) • Reliablewhen you can replicate or repeat it • Validwhen it measures what the researcher set out to measure

30. Statistics and Research:Drawing Conclusions Statistics – using mathematics to organize, summarize, and interpret numerical data • Descriptive statistics (the numbers) organizing and summarizing data (measures of central tendency, measures of variability, and the correlation coefficient) to see if there is a relationship between variables • Inferential statistics: interpreting data and drawing conclusions about the larger population Statistical significance = the relationship found between the IV and DV is not due to chance (.05 level of significance)= less than 5 chances in 100. P value = probability due to chance (lower is better).It can never be 0 because we can never= be 100% certain

31. Correlation Predicts Strength or Relationship Between Variables DOES NOT Say ONE CAUSES OTHER: Correlation does not =Causation • Foot size and vocabulary positively correlated • larger feet belong to older children Negative correlation between income and dental care-what can one conclude?

32. Strengths and Weaknessesof Experimental Research • Strengths: • conclusions about cause-and-effect relationships can be drawn • Weaknesses: • artificial nature of experiments • ethical and practical issues

33. Figure 2.10 Comparison of major research methods

34. Advantages/Disadvantages of these (naturalistic observation, case studies, surveys) called Descriptive/ Correlation Methods: Advantage: • explore questions that can not be examined with experimental methods (poor maternal nutrition and birth defects) Disadvantage: Cannot control events to isolate cause and effect

35. Evaluating Research:Methodological Pitfalls • Sampling bias =sample not representative of population- I CANNOT DRAW CONCLUSIONS-I use all 5th period Psych. students • Placebo effects = participants’ expectations lead them to experience some change (do better on a test, less headaches), regardless of the Independent Variable/Treatment (expectations cause results) • Placebo method helps=give both groups a drug (one real and one a placebo) expectations cause results

36. Evaluating Research:Methodological Pitfalls Distortions in self-report data (survey, interview): • Social desirability bias = give socially approved answers to personal questions • Response set = respond to questions in a particular way that is unrelated to the content of the question (agreeing with almost everything on a questionnaire) -Hawthorne Effect=changes in subjects behaviordue to the attention of researcher (having control and experimental groups help)

37. Evaluating Research:Methodological Pitfalls/Design Flaws • Experimenter bias = researchers expectations about outcome of study influences results; treats experimental and control groups differently to increase chance of confirming hypothesis double-blind control/procedure = neither subject or experimenter know which group is the control or experimental group Single blind control/procedure=the subject does not know if they are the control or experimental group

38. Ethics in Psychological Research:Do the Ends Justify the Means? Ethical standards for research: the American Psychological Association- academic research and the IRB : ensures ethical treatment for animal and human research: 1. Informed consent – participant’s permission, told potential risks, offered alternative activity-MUST BE GIVEN RIGHT TO WITHDRAW 2. No harm to humans Psychological or physical 3. Minimal harm to animals-Ethical Treatment 4. Debriefing to offset deception 5. Confidentiality- cannot share names (includes test scores-UNLESS WRITTEN PERMISSION GIVEN Deception IS PART OF RESEARCH!!!

39. Design Flaw in Experimental Research vs. Ethical Flaws • Control group and experimental group receive different treatment/conditions (time of day for ex.) with the exception of the manipulation of the Independent Variable • Subjects are not randomly selected and assigned • No Operational definitions Show AP Test Research MC Question 2016

40. Figure 2.17 Ethics in research