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Psychological Research Methods Excavating Human Behaviors.

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Presentation on theme: "Psychological Research Methods Excavating Human Behaviors."— Presentation transcript:

1 Psychological Research Methods Excavating Human Behaviors

2 Hindsight Bias The tendency to believe, after learning the outcome, that you knew it all along. I knew New England would lose the Super Bowl?

3 Overconfidence We tend to think we know more than we do. 82% of U.S. drivers consider themselves to be in the top 30% of their group in terms of safety 81% of new business owners felt they had an excellent chance of their businesses succeeding. When asked about the success of their peers, the answer was only 39%. (Now that's overconfidence!!!)

4 Components of a Scientific attitude Humility- vulnerability Curiosity Skepticism- question authority Use empiricism- Experience and observation Scientific attitude asks 2 questions: What and How

5 First have a theory An explanation using an integrated set of principles that organizes observations and predicts behavior or events. Example: If we notice that depressed people talk about their lives in a negative way we can theorize that at the heart of depression is low esteem. Now we must test this So we develop a hypothesis

6 Hypothesis A tentative theory that has not yet been tested. Have operational definitions. Be replicable.

7 Operational Definitions Explain what you mean in your hypothesis. How will the variables be measured in “real life” terms. 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?

8 Types of Research Descriptive Correlational Experimental

9 Descriptive Research Any research that observes and records. Does not talk about relationships, it just describes. What is going on in this picture? We cannot say exactly, but we can describe what we see. Thus we have…..

10 Types of Descriptive Research The Case Study The Survey Naturalistic Observation

11 The Case Study Where one person (or situation) is observed in depth. What are the strengths and weaknesses of using a tragedy like the Columbine School Shootings as a case study?

12 The Survey Method Used in both descriptional and correlational research. Use Interview, mail, phone, internet etc… The Good- cheap, anonymous, diverse population, and easy to get random sampling (a sampling that represents your population you want to study).

13 Random Sampling

14 Why do we sample? One reason is the False Consensus Effect: the tendency to overestimate the extent to which others share our beliefs and behaviors.

15 Survey Method: The Bad Low Response Rate People Lie or just misinterpret themselves. Wording Effects How accurate would a survey be about the frequency of diarrhea?

16 Naturalistic Observation Observing and recording behavior in natural environment. No control- just an observer. What are the benefits and detriments of Naturalistic Observation?

17 Correlational Research Detects relationships between variables. Does NOT say that one variable causes another. Correlation is not causation There is a positive correlation between ice cream and murder rates. Does that mean that ice cream causes murder?

18 Measured using a correlation coefficient. A statistical measure of the extent to which two factors relate to one another Positive correlations are Direct Correlations Negative Correlations known as Inverse Correlations

19 How to Read a Correlation Coefficient


21 Correlation does not mean causation Correlations help to predict Low self esteem may be a predictor of depression but does it cause depression Correlations indicates the possibility but it DOES NOT PROVE CAUSATION

22 Illusory Correlations A perceived but nonexistent correlation Random coincidences Often caused by confirmation bias- look for evidence that supports our belief- ignore evidence that does not support out beliefs Every time I wash my hair it rains. Washing my hair must cause it to rain.

23 Experimental Research Explores cause and effect relationships. Eating too many bananas causes Constipation

24 Steps in Designing an Experiment 1.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.Analyze Results

25 Experimental Vocabulary Independent Variable: factor that is manipulated Dependent Variable: factor that is measured Extraneous Variables: factors that effect DV, that are not IV. Experimental Group: Group exposed to IV Control Group: Group not exposed to IV

26 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 causes B. A confounding variable is anything that could cause change in B, that is not A. Lifestyle and family history may also effect the heart.

27 Experimenter Bias Another confounding variable. Not a conscious act. Double-Blind Procedure.

28 Hawthorne Effect But even the control group may experience changes. Just the fact that you know you are in an experiment can cause change. Whether the lights were brighter or dimmer, production went up in the Hawthorne electric plant.

29 Other Important Stuff Random assignment holds constant all the factors that can affect an experiment Be aware of the Placebo affect – belief that a treatment works because a trusted person told them it would work

30 Placebo Effect BklYg&safety_mode=true&persist_safety_ mode=1&safe=active BklYg&safety_mode=true&persist_safety_ mode=1&safe=active YJWOo&list=PL2920A92123EAF834&ind ex=72&safety_mode=true&persist_safety_ mode=1&safe=active YJWOo&list=PL2920A92123EAF834&ind ex=72&safety_mode=true&persist_safety_ mode=1&safe=active

31 Use of statistics Used in descriptive, correlational and experimental research Helps us interpret what we may miss with just our eyes Measures of tendency used- organize data in a meaningful way very often a bar graph

32 Figure 2.8 Read the scale labels An American truck manufacturer offered graph (a)—with actual brand names included—to suggest the much greater durability of its trucks. Note, however, how the apparent difference shrinks as the vertical scale changes in graph (b). © 2011 by Worth Publishers

33 Analyze Results/ Describing Data Use measures of central tendency (mean, median and mode). Use measures of variation (range and standard deviation). SD answers how much scores vary around the mean. Range gap between the highest and lowest scores

34 Mean, Median, Mode Mean= Average 46 + 20 + 10 = 76 Mean = 25.3 Most Common Median= the midpoint 50 th percentile 1,2,3,4,5, Median =3 Mode= frequently occurring score 1,2,2,3,3,3,4 Mode = 3 Simplest

35 A Skewed Distribution Are the results positively or negatively skewed?

36 Distributions Outliers skew distributions. If group has one high score, the curve has a positive skew (contains more low scores). Pulls the mean toward the higher end If a group has a low outlier, the curve has a negative skew (contains more high scores)Pulls mean toward the lower end

37 Measures of variation Helps to know the amount of variation- how similar or diverse the scores are Range provides a rough estimate of variation Standard deviation is better – how much the scores deviate from one to another and how scores vary around the mean


39 Normal Distribution In a normal distribution, the mean, median and mode are all the same. Range= the gap between the highest and lowest scores

40 Scores A unit that measures the distance of one score from the mean. A positive z score means a number above the mean. A negative z score means a number below the mean.

41 Figure 2.10 The normal curve Scores on aptitude tests tend to form a normal, or bell-shaped, curve. For example, the Wechsler Adult Intelligence Scale calls the average score 100. © 2011 by Worth Publishers

42 Goals of Different research Methods Correlational- can be used to predict future phenomena Experiments determine cause and effect

43 When is a Difference Significant? Statistical tests help us determine whether differences are meaningful When the sample averages are reliable, and when the difference between them are relatively large, we say the difference has statistical significance: a statistical statement of how likely it is that an obtained result occurred by chance Remember: Statistical significance indicates the likelihood that a result will happen by chance. Goal is to get a significance level of 5% means only of the results was due to chance and 95% due to your independent variable


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

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

47 Human Research No Coercion- must be voluntary Informed consent Anonymity No significant risk Must debrief

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