1. Research Strategies It is actually way more exciting than it sounds!!!!

2. Why do we have to learn this stuff? Psychology is first and foremost a science. Psychology is based on research and critical thinking (not blindly accepting arguments and conclusions).

3. Observation and Bias The simplest scientific technique is observation. But sometimes we look for evidence to confirm our beliefs and ignore evidence that contradicts them (confirmation bias). For example, if one believes that all Italians are in shape and go tanning, then they turn on MTV to prove our point. Look…I knew it was true!!! But is it really?

4. Participant Bias Just the fact that you know you are in an experiment can cause you to act and behave differently. Whether the lights were brighter or dimmer, production went up in the Hawthorne electric plant because they knew they were being studied (thus the Hawthorne Effect).

5. Methods of Research Naturalistic Observation The Case Study Correlational Study The Survey Longitudinal Studies Cross-Sectional Studies

6. Naturalistic Observation Watch subjects in their natural environment (Jane Goodall’s study of great apes in Africa). Adv. = the witnessed behavior is not artificial & eliminates participant bias. Disadv. = subjects responses may be misinterpreted. (& what about ethical issues… watching people without them knowing).

7. Case Studies A detailed picture of one or a few subjects. The hope is to reveal universal principles. Adv. = can suggest hypothesis for further study. Disadv. = subjects may be atypical. The ideal case study is John and Kate. Really interesting, but what does it tell us about families in general?

8. Correlational Study Correlation studies investigates the degree to which two variables are related. DOES NOT show cause and effect! As more ice cream is eaten, more people are murdered. Does ice cream cause murder, or murder cause people to eat ice cream?

9. Types of Correlation Positive Correlation The variables go in the SAME direction. Negative Correlation The variables go in OPPOSITE directions. Studying and grades hopefully has a positive correlation. Heroin use and grades probably has a negative correlation.

11. Survey Method Technique used to collect info about peoples attitudes and behaviors. Most common type of study in Psychology. Cheap and fast. The survey results must be relevant to the population and you must have a good sized random sample. Adv. = can tell you a lot about the population you’re targeting. Disadv. = People may not respond truthfully and samples may not be representative.

12. Longitudinal Study Studying the same group of people over the years and noting changes. Adv. = see how people change over time. Disadv. = expensive, time consuming, and participants may not be available for the whole study.

13. Cross Sectional Study Various age groups studied at the same time. Adv. = less time consuming than a longitudinal study. Disadv. = differences between the members of the sample cannot necessarily be attributed to age or development.

14. Experimental Method Looking to prove causal relationships. Cause and Effect. Laboratory vs. Field Experiments. Smoking causes health issues.

15. Steps in Designing an Experiment 1.Develop a Hypothesis. 2.Pick Population: Random Selection then Random Assignment. 3.Operationalize the Variables. 4.Identify Independent and Dependent Variables. 5.Look for Extraneous Variables. 6.Type of Experiment: Blind, Double Blind etc.. 7.Gather Data. 8.Use statistical analysis to discover whether the difference in the DV between the two groups has been caused by the IV.

16. Terminology for Experiments

17. Hypothesis Expresses a relationship between two variables. A variable is anything that can vary among participants in a study. A hypothesis is a testable prediction such as: “Participating in class leads to better grades than not participating”.

18. Operational Definitions Explain what you mean in your hypothesis. A specification of the exact procedures used to measure a variable. How you operationalize the variables will tell us if the study is valid and reliable. Let’s say your hypothesis is that “chocolate causes violent behavior.” What do you mean by chocolate? What do you mean by violent behavior?

19. Independent Variable Whatever is being manipulated in the experiment by the researcher. Hopefully the independent variable brings about change. If there is a drug in an experiment, the drug is almost always the independent variable.

20. Dependent Variable The dependent variable would be the effect of the drug. Factor that is being measured in the experiment. It is dependent on the independent variable.

21. Sampling Identify the population you want to study. The sample must be representative of the population you want to study. GET A RANDOM SAMPLE.

22. Random Assignment Once you have a random sample, randomly assigning them into two groups helps control for confounding variables. Experimental Group: (Group exposed to IV) vs. Control Group: (Group NOT exposed to IV). Placebo: substance that has no effect apart from a person’s belief in it.

23. Beware of Confounding Variables If I wanted to prove that smoking causes heart issues, what are some confounding variables? The object of an experiment is to prove that A (IV) causes B (DV). A confounding variable is anything that could cause change in B, that is not A. Lifestyle and family history may also effect the heart.

24. Experimenter Bias A type of confounding variable. Not a conscious act. To eliminate bias they do… Single blind studies – participants don’t know what group they’re in. Double blind studies – participants and researchers don’t know which group is which.

25. Replication One other safeguard in the experimental process is to see if you can repeat an experiment and see if the results can be reliably reproduced (replication).

26. Data Analysis In an experiment, the results are called statistically significant if they are unlikely to have occurred by chance (no more than a 5% chance).

27. APA Ethical Guidelines for Research IRB - Internal Review Board. Both for humans and animals.

28. Animal Research Clear purpose. Treated in a humane way. Acquire animals legally. Least amount of suffering possible.

29. Human Research Must be voluntary. Informed consent. Confidentiality. No significant risk of harm or discomfort. Must debrief the participants afterwards.

30. Statistics Recording the results from our studies. Statistics help to make data meaningful. Must use a common language so we all know what we are talking about.

31. Descriptive Statistics Just describes sets of data. One example would be a frequency distribution. Or even frequency polygons or histograms.

32. Frequency Distributions A list of scores from highest to lowest. Helps make the raw data more meaningful.

33. Bar Graphs Putting the info into a bar graph makes that info even more meaningful because we can see how the scores cluster.

34. Measures of Central Tendency Mean (the average), Median (middle score) and Mode (most frequent). Watch out for extreme scores or outliers; this effects the mean most!! $25,000-Pam $25,000- Kevin $25,000- Angela $100,000- Andy $100,000- Dwight $200,000- Jim $300,000- Michael Let’s look at the salaries of the employees at Dunder Mifflen Paper in Scranton: The median salary looks good at $100,000. The mean salary also looks good at about $110,000. But the mode salary is only $25,000. Maybe not the best place to work. Then again living in Scranton is kind of inexpensive.

35. Normal Distribution In a normal distribution, the mean, median and mode are all the same.

36. Normal Distribution 68% of the population fall within one standard deviation of the average. 96% fall within two SD’s of the average.

37. Distributions Outliers lead to skewed distributions. If a group has one high score, the curve has a positive skew (contains more low scores). If a group has a low outlier, the curve has a negative skew (contains more high scores).

38. A Skewed Distribution Are the results positively or negatively skewed? Look at how much the mean is affected!

39. Other measures of variability Range: distance from highest to lowest scores. Standard Deviation: the variance of scores around the mean. The higher the variance or SD, the more spread out the distribution is. Do scientists want a big or small SD? A small one... less variance. Kobe and LeBron may both score 30 ppg (same mean). But their SDs are very different.

40. Comparative Statistics Two major types: Percentage (compares to a perfect score of 100). Percentile rank (compares to other scores of an imaginary 100 people).

41. Correlation Coefficient A number that measures the strength of a relationship. Range is from -1 to +1 The relationship gets weaker the closer you get to zero. Which is a stronger correlation? -.13 or +.38 -.72 or +.59 -.91 or +.04

43. How to Read a Correlation Coefficient

44. Inferential Statistics The purpose is to discover whether the findings can be applied to the larger population from which the sample was collected. Remember “statistical significance.” (Is it unlikely the results occurred by chance??

45. Statistical Significance When two distributions show little overlap, the difference between them is LESS likely due to chance... Therefore, statistically significant.