1. Mary Jones

2. Psychology: The Science of Behavior and Mental Processes Psychologists attempt to understand Observable behavior: Such as speech and physical movement Mental processes: Such as remembering and thinking, which cannot be directly observed

3. In This Chapter The Four Major Research Perspectives Research Methods Used by Psychologists How to Understand Research Results

4. The Four Major Research Perspectives

6. Perspectives Emphasizing Internal Factors Views physiological hardware (especially the brain and nervous system) as the major determinants of behavior and mental processing Biological perspective

7. Perspectives Emphasizing Internal Factors Depression: An Example -Perspective focuses on a deficiency of activity of certain chemicals in the nervous system as the cause of depression and the use of anti-depressant drugs -Demonstrates that mood is partly a function of brain chemistry

8. Perspectives Emphasizing Internal Factors Emphasizes how mental processes, such as perception, memory, and problem solving, work and impact behavior Addresses how memory retrieval is facilitated Cognitive perspective

9. Perspectives Emphasizing External Factors Posits that behavior is a result of past history of conditioning within the environment Involves two major types of conditioning, classical and operant Behavioral perspective

10. Behavioral Perspective

11. Classical Conditioning Can you think of an example of classical conditioning?

12. Operant Conditioning Can you think of an example of operant conditioning?

13. Research Methods Used by Psychologists

14. Beware…the Hindsight Bias May Be Lurking! What is this bias? Tendency after learning an outcome to be overconfident in the ability to predict it Belief that findings are more obvious and easier than they actually are

15. Which Perspective Is Best? No perspective is better than the others; all perspectives are complementary Psychologists use all four perspectives to get a more complete explanation of behavior and mental processing

16. Research Methods Used by Psychologists Descriptive methods Observational techniques Case studies Survey research

17. Research Methods Used by Psychologists Correlational studies Correlation coefficient

18. Descriptive Methods: Types Three types -Observational techniques -Case studies -Survey research Each method seeks to provide objective and detailed descriptions of behavior and/or mental processes

19. Descriptive Methods: Observational Techniques Observational techniques Behavior of interest is directly observed Naturalistic observation Behavior being observed occurs in its natural setting, without researcher intervention Participant observation Observer becomes part of the group being observed

20. Descriptive Methods: Case Studies Case studies Individual is studied in depth over extended period of time Results of case studies cannot be generalized Cause–effect statements based on the findings of a case study cannot be made

21. Descriptive Methods: Survey Research Survey research Uses questionnaires and interviews to collect information about the behavior, beliefs, and attitudes of particular groups of people Wording, order, and structure of survey questions may lead the participants to biased answers

22. Descriptive Methods: Survey Research Survey research Population: Entire group of people to be studied Sample: Subset of a population that actually participates in a research study Random sampling: Ensures that each individual in a population has an equal opportunity to be in the sample

23. Correlational Methods Correlational study Two variables measured to determine if they are related or how well either one predicts the other Variable Any factor that can take on more than one value

24. The Correlation Coefficient Correlation coefficient Demonstrates the type and the strength of the relationship between two variables Ranges in value from -1.0 to +1.0 Uses a plus (+) or minus (-) sign to convey the type of relationship Let’s look more closely at each of these relationships

25. Types: Positive Correlation Positive correlation Indicates a direct relationship between two variables Low scores on one variable tend to be paired with low scores on the other variable High scores on one variable tend to be paired with high scores on the other variable

26. Types: Negative Correlation Negative correlation Shows an inverse relationship between two variables Low scores on one variable tend to be paired with high scores on the other variable

27. Strength of Relationship Absolute value Second part of the correlation coefficient which ranges from 0 to 1 Zero and absolute values near zero indicate no relationship Remember: The sign of the coefficient conveys nothing about the strength of the relationship

28. Understanding Predictability: Scatterplots Scatterplots Visual depiction of correlational data Each data point in the scatterplot is a person's scores on each of the two variables

29. Understanding Predictability: Scatterplots Indicates maximal predictability Shows increasing (a) and decreasing (b) trends

30. Understanding Predictability: Scatterplots No relationship between variables Minimal or no predictability

31. Understanding Predictability: Scatterplots Fairly strong because not much scatter Indicates correlations with strengths between 0 and 1.0

32. The Third-Variable Problem Third-variable problem Occurs when a third, unmeasured variable is responsible for the relationship observed between the two measured variables Is not controlled in a correlation study Cause for observed relationship cannot be determined

33. Experimental Research Researcher control Is key aspect of experimental research Allows the researcher to make cause-and-effect statements about the experimental results

34. Experimental Research: Experimenter Control Experimenter control For influence of possible third-variables For any possible influence due to individual characteristics of the participants

35. What is Random?

36. Experimental Research: Designing an Experiment Experiment Researcher manipulates one or more independent variables and measures their effect on one or more dependent variables while controlling other potentially relevant variables

37. Experimental Research: Designing an Experiment Experiment Hypothesis is made The hypothesis determines the prediction to be tested about the cause-and-effect between two variables Independent variable: Hypothesized cause; Manipulated by experimenter Dependent variable: Hypothesized variable; Measured by the experimenter

38. Experimental Research: Designing an Experiment Hypothesis is made. Variables are operationally defined Description of the operations or procedures that a researcher uses to manipulate or measure a variable are delineated This facilitates replication of the experiment

39. Experimental Research: Designing an Experiment Hypothesis is made Variables are operationally defined Groups are determined Experimental group: Exposed to the independent variable Control group: Not exposed to the independent variable Placebo group: Believes they are receiving treatment but are not Nocebo effect

40. Experimental Research: Statistical Analyses Double-blind procedure may be used Control measure in which neither the experimenter nor the participants know which participants are in the experimental and control groups Measure controls for experimenter expectations

41. Design of Aerobic Exercise and Anxiety Experiment

42. Experimental Research: Statistical Analyses Indicate the probability that results of a study are due to random variation (chance) Inferential statistical analyses Significant finding is one that has a probability less than 0.05 (1/20) that it is due to chance Significant finding does not insure that the result has practical significance or value in our everyday world Statistical significance

43. Experimental Research: Statistical Analyses Significant finding that has a probability less than 0.05 (1/20) that it is due to chance Significant finding does not insure that the result has practical significance or value in our everyday world Statistical significance Do you know why?

44. Experimental Research: Statistical Analyses Statistical technique that combines the results of a large number of studies on one experimental question into one analysis to arrive at an overall conclusion Conclusion is considered much stronger evidence than the results of an individual study in answering an experimental question Meta-analysis

45. Summary of Research Methods

46. How to Understand Research Results Descriptive Statistics Frequency Distributions

47. How to Understand Research Results Descriptive statistics Used to describe the data of a research study in a concise fashion Frequency distributions Indicate the probability that the results of the study are due to random variation

48. How to Understand Research Results: Descriptive Statistics Types of descriptive statistics Measures of central tendency Measures of variability Frequency distribution Depicts the number of participants receiving each score for a variable in a table or graph

49. How to Understand Research Results: Measures of Central Tendency Central tendency Designed to summarize a set of data with a single score Three measures of central tendency Mean: Numerical average for a distribution of score Median: Score that is positioned in the middle of the distribution of scores when scores are listed from lowest to highest Mode: Most frequently-occurring score in a distribution of scores

50. That’s One Mean Statistic! Mean Is most commonly used measure of central tendency Used to analyze data in many inferential statistical tests Can be distorted by extremely high or extremely low scores because it uses all scores in its computation

51. How to Understand Research Results: Measures of Variability Measures of variability Designed to provide an idea of how scattered a set of scores tends to be Two measures of variability Range: Difference between the highest and lowest scores in a distribution of scores Standard deviation: Average extent to which the scores vary from the mean of the distribution

52. Summary of Descriptive Statistics

53. How to Understand Research Results: Frequency Distributions Frequency distributions O rganizes the data in a score distribution so that the frequency of each score can be determined Types of distributions Normal distributions Skewed distributions

54. How to Understand Research Results: Normal Distributions Main aspects of normal distribution Mean, median, and mode are all equal because the normal distribution is symmetric about its center Percentage of scores falling within a certain number of standard deviations of the mean is set

55. How to Understand Research Results: The Normal Distribution

56. How to Understand Research Results: Normal Distributions with Different Standard Deviations

57. How to Understand Research Results: Percentile Rank Remember: The percentages of scores and the number of standard deviations from the mean always have the same relationship in a normal distribution Percentile rank: Percentage of scores below a specific score in a distribution of scores

58. How to Understand Research Results: Skewed Distributions Skewed distributions Asymmetric frequency distribution in which some unusually high scores distort the mean to be greater than the median Right-skewed (also called positively skewed) distribution Left-skewed (also called negatively skewed) distribution

59. Sample Skewed Distributions

60. Skewed Distributions Distortions Distortion occurs for the means of skewed distributions, because unusually high or low scores distort the mean Consequently, with a skewed distribution, median should be used because atypical scores in the distribution do not distort the median

61. An Example of a Right-Skewed Distribution