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 definitions precisely define each variable(IV-breakfast, DV-school performance)-required for good experiment
most needed aspect of a study- so study can be refuted or verified through REPLICATIOIN (makes study scientific)
An experiment is a research method where there is manipulation of one variable under carefully controlled conditions so that resulting changes in another variable can be observed…key word “resulting.”
Experiments are very powerful in that they allow for detection of cause-and effect relationships…Does X cause Y?
The IV is the variable that the experimenter controls or manipulates…the DV is the variable thought to DEPEND (at least in part) on manipulation of the IV.
If we wanted to know how X affects Y, X would be the IV, and Y would be the DV.

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 view aggressive 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
In an experiment, the investigator assembles two groups who are as alike as possible, an experimental group (who receives a special treatment in regard to the independent variable) and a control group (who do not receive the special treatment). Then, after they administer the treatment, if the two groups differ on the dependent variable, it MUST be due to the treatment.
An extraneous variable is a variable, other than the independent variable, that may influence the dependent variable.
Confounding of variables occurs when participants in one group of subjects are inadvertently different in some way from participants in the other group, influencing outcome.
Random assignment of subjects is used to control for confounding variables.

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 when Outliers = 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
A correlation exists when two variables are related to each other.
The correlation coefficient is a numerical index of the strength and direction of association between two variables.
A correlation is expressed as a number between 1 and 0, and the number may be positive or negative.
The closer to 1 the number is, whether +1 or –1, the stronger the relationship between the variables…for example, a correlation of .17 is pretty weak, while a correlation of -.89 is pretty strong.
The positive/negative dimension of the correlation coefficient expresses the direction of the relationship. If two variables are positively correlated, they co-vary in the same direction…as scores on one variable go up, scores on the other variable go up too…if two variables are negatively correlated, the variables co-vary in the opposite direction…as one goes up, the other goes down.

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
PERSON HRS GPA
Which statistic approximates the relationship between the variables?
50% N= N= r= r=.50

23. Unit 2 Review
In normal 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. Normal Curve (bell shaped)
Describing Data Measures 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 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???
In normal curve-distribution of scores-68% fall within 1 SD above/below the mean
-Range=distance between highest and lowest scores in data set
Variability refers to how much scores in a set of data vary from one another and from the mean…the standard deviation is a numerical index of variability.
If the variability in a data set is high, the standard deviation will be a higher number than if the variability is low.

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 mean Percentile scores – the same as or better than 72% of population/test takers 38th percentile=you did 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 definitions are important)
Reliable when you can replicate or repeat it
Valid when it measures what the researcher set out to measure
Sampling bias – when a sample is not representative of the population…poll only men, may get a different outcome if the population is both male and female.
Placebo effects – when a participant’s expectations lead them to experience some change even though they receive empty, fake, or ineffectual treatment…cured by a sugar pill.
Distortions in self-report data:
Social desirability bias – a tendency to give socially approved answers to questions about oneself…did you vote?
Response set – a tendency to respond to questions in a particular way (agree with everything, etc.).
Experimenter bias – when a researcher’s expectations or preferences about the outcome of a study influence the results obtained…researchers see what they want to see – errors are usually in favor of the hypothesis…similarly, researchers may unintentionally influence the behavior of their subjects, possibly through body language, smiles, etc. To control for this problem, a double-blind procedure in which neither subjects nor experimenters know which subjects are in the experimental and which are in the control groups is used…a non-directly involved researcher keeps track of everything.

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
Statistics - using mathematics to organize, summarize, and interpret numerical data…statistical analyses allow researchers to draw conclusions about their data.
Statistics are a part of everyday modern life…batting averages, economic projections, popularity ratings for TV shows, etc.
There are two basic types of statistics, descriptive and inferential.
Descriptive statistics are used to organize and summarize data to provide some sort of overview.
Inferential statistics use the laws of probability to allow researchers to interpret data and draw conclusions.

31. Correlation does not =Causation
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?
As a correlation increases in strength (closer to – or + 1), the ability to predict one variable based on knowledge of the other variable increases.
SAT/ACT scores are correlated with first year college GPA at a moderate .40 to this may not be perfect, but it allows admissions committees to predict with some accuracy how well a prospective student will do in college.
Although correlation may allow prediction, it does not infer cause-and-effect.
For example, a strong positive correlation has been shown between foot size in children and vocabulary…as foot size increases, so does vocabulary. Do bigger feet make children learn more words? No. It is a third variable, age, which causes both feet and vocabulary to grow.

32. Strengths and Weaknesses of Experimental Research
conclusions about cause-and-effect relationships can be drawn
Weaknesses:
artificial nature of experiments
ethical and practical issues
The power of the experimental method lies in the ability to draw conclusions about cause-and-effect relationships from an experiment. No other research method has this power.
Experimental research does, however, have limitations. Experiments are often artificial; researchers have to come up with contrived settings so that they have control over the environment.
Some experiments cannot be done because of ethical concerns…for example, you would never want to malnourish infants on purpose to see what the effects are on intelligence.
Others cannot be done because of practical issues…there’s no way we can randomly assign families to live in urban vs. rural areas so we can determine the effects of city vs. country living.

33. Figure 2.10 Comparison of major research methods

34. Disadvantage: Cannot control events to isolate cause and effect
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 behavior due 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!!!
The question of deception:
Is it OK to make subjects think they are hurting others? Have homosexual tendencies? Think they are overhearing negative comments about themselves?
The question of animal research:
Controversy regarding humane treatment of animals vs. no use of animals in research.
These and other ethical issues have led the American Psychological Association (APA) to develop a set of ethical standards for research, to ensure that both human and animal subjects are treated with dignity.

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