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Research Methods & Statistics:

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Presentation on theme: "Research Methods & Statistics:"— Presentation transcript:

1 Research Methods & Statistics:
“Those who trust in their own wits are fools”. -Proverbs 28:26

2 Guiding Principles The goal is research literacy.
Most of us will not be career researchers. BUT….we are all consumers of research. How do you tell the good from the bad? No research is perfect. Everything is open to critique—a great platform for critical thinking.

3 Methods Is Like Whac-A-Mole…
a fun game for which there is no perfect solution

4 Whac-A-Mole If I’m conducting my own research, the goal is to minimize the moles. If I’m considering the research of others, the goal is to identify as many moles as I can.

5 98% Certainty Answer the questions on the next slide by writing a small number and a large number such that you are at least 98% certain that the correct answer is in between.

6 98% Certainty The area of the US in square miles?
The population of Australia 2007? American battle deaths in Spanish-American War? Female psychiatrists in the US in 2005? Operating nuclear plants worldwide in 2007?

7 98% Certainty Area of US: Australian pop.: Battle deaths:
Female psychiatrists: Nuclear plants: 3.6 million sq. miles 20.4 million 385 13,079 435 How’d that work out?

8 Topic 1: Moles Hindsight bias (Myers Activity)
Confirmation bias (NY Times activity) Overconfidence

9 Topic 2: Descriptive Techniques
Case studies Surveys Naturalistic observation.

10 Case Studies Case Study- Descriptive technique in which one individual or group is studied in depth in the hope that a universal principle will be discovered. What is the potential mole? The person or group studied may be atypical. Individual cases are useful, but can often lead to mistaken judgments, and false conclusions.

11 Phineas Gage https://www.youtube.com/watch?v=_nikOxNfjqs
Most famous psychological case study. Taught psychologists a lot about the brain.

12 Survey Activity Half of you will go out in the hall while the other half answers the questions on the next slide. When they are finished, the other half of you will come in and answer questions on the following slide.

13 Group 1 Is the Mississippi River longer or shorter than 500 miles?
2. How many miles long is it?

14 Group 2 Is the Mississippi River longer or shorter than 3000 miles?
2. How many miles long is it?

15 Activity Continued Both of the groups actually answered the same second question. It was clearly an unambiguous question, no tricks or anything. Let’s record the answers to the second question from the first group, and then the answers for the second group. What do we notice? *The river is 2,320 miles long

16 Surveys A survey- a technique for ascertaining the self-reported attitudes and behaviors of a particular group, usually by questioning a representative, random sample of the group. What are the potential moles? The wording of the question can influence the answer. How do you insure the sample is indeed random, and accurately reflects the attitudes of the group as a whole?

17 Good Surveying Techniques
Carefully word your survey questions in order to ensure you are not generating a particular response. Control for any errors by asking a similar question with different wording. Avoid sampling bias or a flawed sampling process that produces an unrepresentative sample. Establish a random sample i.e. a sample that fairly represents a population because each member has an equal chance of inclusion.

18 Topic 3: Experimentation
The purpose of an experiment is to establish a cause-and-effect relationship. Experiments are the only research method that can establish cause-and-effect.

19 Example Experiment General hypothesis: Food affects learning.
Specific hypothesis with operational variables: Students who eat an oatmeal raisin cookie before class each day will have higher average scores on the semester final than students who don’t eat a cookie.

20 Eating cookies before class each day will lead to higher average scores.
Variables: Independent (IV) Controlled by experimenter The “cause” variable Dependent (DV) Predicted by experimenter The “effect” variable

21 Eating cookies before class each day will lead to higher average scores.
What if kids get cookies and A’s? Groups (conditions): to establish different levels of the IV Experimental group Exposed to IV Get cookie Control group Not exposed to IV No cookie

22 Eating cookies before class each day will lead to higher average scores.
Confounding Variables IV DV Environmental Expt. Gp. Cookie 95% Expectations Cntrl. Gp. No Cookie 82% Individual differences

23 Control for confounding variables
Environmental: Make the environment the same for both groups (so it’s not a variable). Expectations: Utilize a blind procedure (so nobody knows what to expect). Individual differences: Randomly assign participants to groups (so the differences have the same average impact on each group).

24 Random Sampling & Random Assignment
To select participants from population Allows you generalize results Random Assignment To divide participants into groups Controls individual difference confounding variables

25 Eating cookies before class each day will lead to higher average scores.
IV DV Expt. Gp. Cookie 95% Cntrl. Gp. No Cookie 82% 85% 93%

26 Statistical Significance
p value likelihood a result is caused by chance can be no greater than 5% p ≤ .05

27 Replication Non-replicated results are preliminary.
Linus Pauling (1970). Vitamin C prevents colds. IV DV Has never been proven, even after 16 double blind tests. Expt. Gp. Vit C Expt. Gp. 45% Fewer colds Cntrl. Gp. Placebo

28 Importance of Operational Definitions
Students are more likely to smile for their senior pictures if they have a friendly photographer. IV? Photographer friendliness DV? Smiling Operational definitions are needed for both of these variables. To illustrate the importance of this, have students determine how many of the students on the following slide are smiling.

29 How Many Smiles

30 Importance of Operational Definitions
If we want to be critical consumers of research, we need to always ask how research variables were operationalized (“What do they mean by ‘best school,’ ‘learning,’ ‘happiness,’ etc.?”). Research cannot be replicated without operational definitions.

31 Statistics Our focus should be conceptual, not computational.
Statistics are necessary to understand the meaning of a set of numbers. The importance of statistics needs to be evident throughout the entire course, not just in the methods unit.

32 Topic 1: Frequency Distributions
Putting scores in order adds meaning Bar graphs (histograms) are visual representations of frequency distributions.

33 Topic 2: What’s the center of the distribution?
Measures of Central Tendency Quiz Scores 4 3 5 Mode --Most common = 4 Mean --Arithmetic avg = 20/5 = 4 Median --Middle score = 4

34 Central Tendency: Mean vs. Median 1968 TOPPS Baseball Cards
Nolan Ryan $1500 Billy Williams $8 Luis Aparicio $5 Harmon Killebrew $5 Orlando Cepeda $3.50 Maury Wills $3.50 Jim Bunning $3 Tony Conigliaro $3 Tony Oliva $3 Lou Pinella $3 Mickey Lolich $2.50 Elston Howard $2.25 Jim Bouton $2 Rocky Colavito $2 Boog Powell $2 Luis Tiant $2 Tim McCarver $1.75 Tug McGraw $1.75 Joe Torre $1.5 Rusty Staub $1.25 Curt Flood $1 With Ryan: Without Ryan: Median=$2.50 Median=$2.38 Mean=$74.14 Mean=$2.85

35 The median is a better measure of central tendency than the mean when there are extreme scores.

36 Topic 3: How spread out are the data?
Measures of variation Range The spread between the highest number & the lowest number. Only considers two numbers Standard deviation- a computed measure of how scores vary around the mean score.

37 Calculation Example for Standard Deviation
Punt Distance Deviation from Mean Deviation Squared std. dev. = 36 38 41 45 -4 -2 +1 +5 16 4 1 25 Variance = 11.5 = 3.4 yds 46 Mean = 160/4 = 40 yds 46/4 = 11.5 = variance

38 Topic 4: Properties of the Normal Curve
In a large, randomly distributed data set 68% of scores will be within 1 SD of the mean. 95% of scores will be within 2 SDs of the mean. 99.7% of scores will be withing 3 SDs of the mean.

39 Topic 4: Properties of the Normal Curve
Marilyn vos Savant: claimed IQ of 228. Is it more meaningful to express her IQ as points above average or as standard deviations above average?

40 Topic 5: Correlation A measure of the strength of the relationship between two variables. Can be positive or negative. Useful for making predictions. You can fairly easily calculate correlations with Excel or Google Docs.

41 What does a correlation looks like?
Topic 5: Correlation What does a correlation looks like? Scatterplots Positive Correlation Negative Correlation

42 Topic 5: Correlation No Correlation

43 Topic 5: Correlation How do you express a correlation numerically?
The Correlation Coefficient

44 Topic 5: Correlation A strong correlation is not enough to establish a cause and effect relationship. Example: There is a correlation between TV watching and grades. Do you think it’s positive, or negative? From this, what do we know about cause-and-effect.

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49 Topic 5: Correlation Even correlations that are clearly not cause-and-effect relationships can be used for prediction. Ex: College entrance exams and freshman GPA. Ex: Shoe size and vocabulary size in elementary school children. Ex: Ice cream sales and the rate of violent crimes.

50 Topic 5: Correlation Weird correlations:

51 Topic 5: Correlation

52 Topic 6: Statistical Significance
A measure of the likelihood that a result is caused by chance. In an experiment, we want that likelihood to be low so we can conclude a cause-and-effect relationship exists between the IV and the DV.

53 Topic 6: Statistical Significance
P value is an estimate of the probability that a result was caused by chance. In an experiment, it’s the likelihood that the difference between the experimental and control conditions as measured by the DV was caused by chance. We want this difference to be caused by our manipulation—the IV—not by chance.

54 Topic 6: Statistical Significance
To say that the results of an experiment are statistically significant means that there is a small likelihood that the results were caused by chance; that is, a high likelihood they were caused by the IV. The threshold for statistical significance is no more than a 5% likelihood the results were caused by chance. We express this: p ≤ .05

55 Important Things to Consider In Regards to Research
Laboratory experiments can often illuminate certain principles that can help us understand everyday life. While the laboratory is a simplified reality, the idea is to control certain features that cannot be controlled in the outside world. Experiments are conducted in this setting in order to test theoretical principles, which can then be applied. Think of the laboratory as a sort of wind tunnel, like the kind used to develop aeronautical technology.

56 Important Things to Consider In Regards to Research
It is important to consider cultural differences when considering research. Culture, or the enduring behaviors, attitudes, values, and traditions shared by a group of people and transmitted from one generation to the next, matters. Gender often matters to. As a result, research needs to take these differences into consideration. The key to remember though, is that the same underlying processes guide people everywhere. We are more alike than we are different.

57 Important Things to Consider In Regards to Research
Many psychologists study animals, and much research is conducted in this way. There is certainly much that can be learned from animals that can be applied to humans, because there are fundamental similarities. In addition, animals are used in many other scientific experiments that will yield results for humans. There is considerable debate concerning the use of animals in research. The question is whether the good that may come from the research to humans, and many times to other animals, is worth the potential pain or stress that animal may endure in research and testing. It is important to reach a balance of conducting research that is effective, but still ethical in dealing with animals. As Myers states, “A psychology concerned for humans, and sensitive to animals serves the welfare of both.”

58 Important Things to Consider In Regards to Research
There are important ethical principles adopted by the APA. Psychologists are urged to: Obtain informed consent from participants (be told enough to decide whether they wish to participate). Protect participants from physical and emotional harm and discomfort. Maintain confidentiality. Debrief participants after a study (inform them of the purpose of the study, and any deceptions that may have been used).


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