1. Myers’ Psychology for AP*
David G. Myers
PowerPoint Presentation Slides
by Kent Korek
Germantown High School
Worth Publishers, © 2010
*AP is a trademark registered and/or owned by the College Board, which was not involved in the production of, and does not endorse, this product.

2. Unit 2: Research Methods: Thinking Critically with Psychological Science

3. Unit Overview The Need for Psychological Science
How Do Psychologists Ask and Answer Questions?
Statistical Reasoning in Everyday Life
Frequently Asked Questions about Psychology
Click on the any of the above hyperlinks to go to that section in the presentation.

4. The Need for Psychology Science

5. Did We Know It All Along? Hindsight Bias
“I knew it all along”
“Out of sight, out of mind”
“Absence makes the heart grow fonder”

6. Overconfidence Overconfidence
Together with hindsight bias, can lead to overestimate our intuition

7. The Scientific Attitude
Three main components
Curious eagerness
Skeptically scrutinize competing ideas
Open-minded humility before nature

8. Critical Thinking Critical Thinking “Smart thinking” Four elements
Examines assumptions
Discerns hidden values
Evaluates evidence
Assesses conclusions

9. How Do Psychologists Ask and Answer Questions?

10. The Scientific Method Theory Hypothesis Operational Definition
“mere hunch”
Hypothesis
Can be confirmed or refuted
Operational Definition
Replication (repeat)

12. Figure 2.1 Theory construction

13. Figure 2.2 Flowchart of steps in a scientific investigation

14. The Scientific Method: Terminology
Operational definitions are used to clarify precisely what is meant by each variable
Participants or subjects are the organisms whose behavior is systematically observed in a study
Data collection techniques allow for empirical observation and measurement
Statistics are used to analyze data and decide whether hypotheses were supported
Psychologists use operational definitions to clarify what their variables mean…what exactly is sociability?
Researchers use procedures for making empirical observations and measurements, including direct observation, questionnaires, interviews, psychological tests, physiological recordings, and examination of archival records.
They depend on statistics to analyze data and decide whether hypotheses were supported…observations are converted into numbers, which are then compared.

15. Table 2.1 Key Data Collection Techniques in Psychology

16. The Scientific Method: Terminology
Findings are shared through reports at scientific meetings and in scientific journals – periodicals that publish technical and scholarly material
Advantages of the scientific method: clarity of communication and relative intolerance of error
Research methods: general strategies for conducting scientific studies
They share their findings through reports at scientific meetings and in scientific journals…this way others can evaluate new research findings and build new ideas.
Using the scientific approach, scientists state EXACTLY what they are talking about, resulting in clarity of communication. The scientific method also yields more accurate and dependable information than, for example, speculation.
Research methods consist of differing approaches to the observation, measurement, manipulation, and control of variables in empirical studies.

17. The Scientific Method A good theory is useful if it:
Effectively organizes a range of self-reports and observations
Implies clear predictions that anyone can use to check the theory

18. Types of Psychological Research
Experimental
Descriptive
Correlational

19. Experimental Research: Looking for Causes
Manipulation of one variable under controlled conditions so that resulting changes in another variable can be observed
Detection of cause-and-effect relationships
Independent variable (IV) = variable manipulated
Dependent variable (DV) = variable affected by manipulation
How does X affect Y?
X = Independent Variable, and Y = Dependent Variable
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.

20. Experimental and Control Groups: The Logic of the Scientific Method
Experimental group
Control group
Random assignment
Different from random sample
Manipulate independent variable for one group only
Resulting differences in the two groups must be due to the independent variable
Eliminates alternative explanations
Extraneous and confounding variables
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.

21. Experimentation Random Assignment
Blind (uninformed)
Single-Blind Procedure
Double-Blind Procedure
Placebo Effect
Use Viagra study as an example
69% of Viagra-assisted attempts at intercourse were successful vs. 22% who were receiving a placebo
Independent of men’s age, weight, and personality (confounding variables)

22. Experimentation Random Assignment
Groups
Experimental Group
Receives the treatment (independent variable)
Control Group
Does not receive the treatment
Use Viagra study as an example
69% of Viagra-assisted attempts at intercourse were successful vs. 22% who were receiving a placebo
Independent of men’s age, weight, and personality (confounding variables)

23. Experimentation Independent and Dependent Variables
Independent Variable
Confounding variable
Effects minimized by using random assignment
Dependent Variable
What is being measured
Use Viagra study as an example
69% of Viagra-assisted attempts at intercourse were successful vs. 22% who were receiving a placebo
Independent of men’s age, weight, and personality (confounding variables)

24. Figure 2.5 The basic elements of an experiment
Ask what could be confounding variables affecting “desire to affiliate”
Gender, SSS, cultural background
Figure 2.5 The basic elements of an experiment

25. Experimental Designs: Variations
Expose a single group to two different conditions
Reduces extraneous variables
Manipulate more than one independent variable
- Allows for study of interactions between variables
Use more than one dependent variable
- Obtains a more complete picture of effect of the independent variable
Experimental designs can be quite complex. These are a few of the ways designs can vary.
Sometimes, a single group can be used for both experimental and control conditions…for example, you might study the effects of having the radio on when people work on an assembly line…you’d collect data from the same group of workers twice, once with the radio on and once with it off.
Researchers can also manipulate more than one IV to see what the combined effect is…sometimes, the effect of one variable depends on the effect of another…for example, you might find that having the radio on increases productivity in workers, but only in the morning…in this example, time of day interacts with the effects of the radio.
Researchers can also use more than one dependent variable in a single study to get a more complete picture of the effect of the independent variable. For example, we might measure not only number of pieces workers finish when the radio is allowed to be on while they work, but also worker satisfaction, absenteeism, and attitude. Having 1 day less a month absenteeism might make up for a slight decrease in productivity.

26. Figure 2.6 Manipulation of two independent variables in an experiment

27. Strengths and Weaknesses of Experimental Research
conclusions about cause-and-effect 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.

28. Descriptive/Correlational Methods: Looking for Relationships
Methods used when a researcher cannot manipulate the variables under study
Naturalistic observation
Case studies
Surveys
Allow researchers to describe patterns of behavior and discover links or associations between variables but cannot imply causation
When practical or ethical issues do not allow for variables to be manipulated, researchers rely on descriptive/correlational methods.
Naturalistic observation is when a researcher engages in careful observation of behavior without intervening directly with the subjects…do more men than women run yellow lights?
A case study is an in-depth investigation of an individual subject…profile of a serial killer, etc.
In a survey, researchers use questionnaires or interviews to obtain specific information about subjects’ behavior…the Kinsey Report on “normal” sexual behavior. A modern day example: Cooper (1999) set out to determine how much time people spend on online sexual pursuits…conducted an online questionnaire that was posted for seven weeks that invited internet sex pursuers to participate. A self-selected sample such as this is not representative of the population of the U.S., but it probably was representative of those who visit sexually explicit websites.
Descriptive/Correlational methods allow researchers to discover links or associations between variables, but cannot imply causation.

29. Description Naturalistic Observation
Describes behavior
Describing behavior is the first step in predicting it
Does not explain behavior

30. Description The Case Study
Suggest further study
Cannot discern general truths
Mostly used to
gather insights and ideas, particularly in the early stages of investigating a topic
describe rare phenomena
create psychobiographies of famous people
provide illustrative anecdotes
Limitations
useless in providing evidence to test behavioral theories or treatments
uncontrolled environment and no comparison information
no way of assessing the reliability or validity of the researcher’s observations or interpretations

31. Description The Survey
Looks at many cases at once
Word effects
Ignorance of numbers or words
Acceptable rather than honest responses
Questions are poorly framed
Wording in emotionally charged issues
Range of response options
Order of questions and alternatives
5 Rules of question design
Balance questions
Don’t assume knowledge
Use everyday language
Be concrete/clear
Don’t employ pejoratives
Random sampling
Representative sample

32. Description The Survey
Sampling
Population
Random Sample

33. Figure 2.10 Comparison of major research methods

34. Correlation Correlation (correlation coefficient)
How well does A predict B
Positive versus negative correlation
Strength of the correlation
-1.0 to +1.0
Scatterplot
Correlation helps us predict

35. Figure 2.14 Interpreting correlation coefficients

36. Correlation

37. Correlation

38. Correlation

39. Correlation

40. Correlation

41. Correlation

42. Correlation

43. Correlation Correlation and Causation
Correlation helps predict
Does not imply cause and effect
Ex: length of marriage correlates positively with hair loss in men- both are associated with a 3rd factor, age

45. Correlation Illusory Correlations
Perceived non-existent correlation
A random coincidence
We’re prone to perceive patterns. Evolutionarily adaptive

46. Correlation Perceiving Order in Random Events
Comes from our need to make sense out of the world
Coin flip
Poker hand

47. Comparing Research Methods

48. Statistical Reasoning in Everyday Life

49. Describing Data Measures of Central Tendency
Mode (occurs the most)
Mean (arithmetic average)
Median (middle score)
Mean is not the most representative in some instances
Ex: on average we have one ovary and one testicle

50. Figure 2.11 Measures of central tendency

51. Describing Data Measures of Variability
Range
Standard Deviation
Example: which basketball player would you choose? One that has an average of 20pts/game with a standard deviation of 2 or one that has an average of 25 pts/game with a standard deviation of 12.

52. Descriptive Statistics: Variability
Variability = how much scores vary from each other and from the mean
Standard deviation = numerical depiction of variability
High variability in data set = high standard deviation
Low variability in data set = low standard deviation
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.

53. Figure 2.12 Variability and the standard deviation

54. Inferential Statistics: Interpreting Data and Drawing Conclusions
Hypothesis testing: do observed findings support the hypotheses?
Are findings real or due to chance?
Statistical significance = when the probability that the observed findings are due to chance is very low
Very low = less than 5 chances in 100/ .05 level
Researchers use inferential statistics to determine whether their data support their hypotheses…with these statistical methods, they can interpret data and draw conclusions.
Inferential statistics use the laws of probability to allow researchers to determine how likely it is that their findings are real, that is, not due to chance.
Statistical significance is said to exist when the probability that the observed findings are due to chance is very low…many psychologists see “very low” as fewer than 5 chances in 100 that results are not real…the .05 level of significance.

55. Making Inferences When Is an Observed Difference Reliable?
Representative samples are better than biased samples
Less-variable observations are more reliable than those that are more variable
More cases are better than fewer

56. Making Inferences When Is a Difference Significant?
Statistical significance
The averages are reliable
The differences between averages is relatively large
Does imply the importance of the results

57. Frequently Asked Questions about Psychology

58. Psychology Applied
Can laboratory experiments illuminate everyday life?
The principles, not the research findings, help explain behavior

59. Psychology Applied Does behavior depend on one’s culture and gender?
Influence of culture on behavior
Gender
More similarities than differences

60. Ethics in Research Ethics in animal research
Reasons for using animals in research
Safeguards for animal use

61. Ethics in Psychological Research: Do the Ends Justify the Means?
The question of deception
The question of animal research
Controversy among psychologists and the public
Ethical standards for research: the American Psychological Association
Ensures both human and animal subjects are treated with dignity
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.

62. Ethics in Research Ethics in human research Informed consent
Protect from harm and discomfort
Maintain confidentiality
Debriefing

63. Figure 2.17 Ethics in research

64. The End

65. Definition Slides

66. Hindsight Bias
= the tendency to believe, after learning an outcome, that one would have foreseen it.
Also known as the “I knew it all along” phenomenon.

67. Critical Thinking
= thinking that does not blindly accept arguments and conclusions. Rather, it examines assumptions, discerns hidden values, evaluates evidence, and assesses conclusions.

68. Theory
= an explanation using an integrated set of principles that organizes observations and predicts behaviors or events.

69. Hypothesis
= a testable prediction, often implied by a theory.

70. Operational Definition
= a statement of the procedures (operations) used to define research variables.
i.e. Human intelligence may be operationally defined as what an intelligence test measures.

71. Replication
= repeating the essence of a research study, usually with different participants in different situations, to see whether the basic finding extends to other participants and circumstances.

72. Case Study
= an observation technique in which one person is studied in depth in the hope of revealing universal principles.

73. Survey
= a technique for ascertaining the self- reported attitudes or behaviors of a particular group, usually by questioning a representative, random sample of the group.

74. Population
= all the cases in a group being studied, from which samples may be drawn.
Note: Except for national studies, this does NOT refer to a country’s whole population.

75. Random Sample
= a sample that fairly represents a population because each member has an equal chance of inclusion.

76. Naturalistic Observation
= observing and recording behavior in naturally occurring situations without trying to manipulate and control the situation.

77. Correlation
= a measure of the extent to which two factors vary together, and thus of how well either factor predicts the other.

78. Correlation Coefficient
= a statistical index of the relationship between two things (from -1 to +1).

79. Scatterplot
= a graphed cluster of dots, each of which represents the values of two variables. The slope of the points suggests the direction of the relationship between the two variables. The amount of scatter suggests the strength of the correlation (little scatter indicates high correlation).

80. Illusory Correlation
= the perception of a relationship where none exists.

81. Experiment
= a research method in which an investigator manipulates one or more factors (independent variables) to observe the effect on some behavior or mental process (the dependent variable). By random assignment of participants, the experimenter aims to control other relevant factors.

82. Random Assigment
= assigning participants to experimental and control groups by chance, thus minimizing preexisting differences between those assigned to the different groups.

83. Double-Blind Procedure
= an experimental procedure in which both the research participants and the research staff are ignorant (blind) about whether the research participants have received the treatment or the placebo. Commonly used in drug-evaluation studies.

84. Placebo Effect
= experimental results caused by expectation alone; any effect on behavior caused by the administration of an inert substance or condition, which the recipient assumes is an active agent.

85. Experimental Group
= in an experiment, the group that is exposed to the treatment, that is, to one version of the independent variable.

86. Control Group
= in an experiment, the group that is NOT exposed to the treatment; contrasts with the experimental group and serves as a comparison for evaluating the effect of treatment.

87. Independent Variable
= the experimental factor that is manipulated; the variable whose effect is being studied.

88. Confounding Variable
= a factor other than the independent variable that might produce an effect in an experiment.

89. Dependent Variable
= the outcome factor; the variable that may change in response to manipulations of the independent variable.

90. Mode
= the most frequently occurring score(s) in a distribution.

91. Mean
= the arithmetic average of a distribution, obtained by adding the scores and then dividing by the number of scores.

92. Median
= the middle score in a distribution, half the scores are above it and half are below it.

93. Range
= the difference between the highest and lowest score in a distribution.

94. Standard Deviation
= a computed measure of how much scores vary around the mean score.

95. Normal Curve
= a symmetrical, bell-shaped curve that describes the distribution of many types of data; most scored fall near the mean (68 percent fall within one standard deviation of it) and fewer and fewer near the extremes.

96. Statistical Significance
= a statistical statement of how likely it is that an obtained result occurred by chance.

97. Culture
= the enduring behavior, ideas, attitudes, and traditions shared by a group of people and transmitted from one generation to the next.

98. Informed Consent
= an ethical principle that research participants be told enough to enable them to choose whether they wish to participate.

99. Debriefing
= the postexperimental explanation of a study, including its purpose and any deceptions, to its participants.