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Chapter 1: The Research Enterprise in Psychology.

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Presentation on theme: "Chapter 1: The Research Enterprise in Psychology."— Presentation transcript:

1 Chapter 1: The Research Enterprise in Psychology

2 The Scientific Approach: A Search for Laws Basic assumption: events are governed by some lawful order Goals: –Measurement and description –Understanding and prediction –Application and control

3 Figure 2.1 Theory construction

4 Figure 2.2 Flowchart of steps in a scientific investigation

5 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

6 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


8 Experimental Research: Looking for Causes Experiment = 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

9 Experimental and Control Groups: The Logic of the Scientific Method Experimental group Control group –Random assignment –Manipulate independent variable for one group only –Resulting differences in the two groups must be due to the independent variable Extraneous and confounding variables

10 Figure 2.5 The basic elements of an experiment

11 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

12 Figure 2.6 Manipulation of two independent variables in an experiment

13 Strengths and Weaknesses of Experimental Research Strengths: –conclusions about cause-and-effect can be drawn Weaknesses: –artificial nature of experiments –ethical and practical issues

14 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


16 Statistics and Research: Drawing Conclusions Statistics – using mathematics to organize, summarize, and interpret numerical data –Descriptive statistics: organizing and summarizing data –Inferential statistics: interpreting data and drawing conclusions

17 Descriptive Statistics: Measures of Central Tendency Measures of central tendency = typical or average score in a distribution Mean: arithmetic average of scores Median: score falling in the exact center Mode: most frequently occurring score –Which most accurately depicts the typical?

18 Figure 2.11 Measures of central tendency

19 Statistical Reasoning A Skewed Distribution 15 20 25 30 35 40 45 50 90 475710 70 Mode Median Mean One Family Income per family in thousands of dollars

20 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



23 Figure 2.12 Variability and the standard deviation

24 Descriptive Statistics: Correlation When two variables are related to each other, they are correlated. Correlation = numerical index of degree of relationship –Correlation expressed as a number between 0 and 1 –Can be positive or negative –Numbers closer to 1 (+ or -) indicate stronger relationship

25 Correlation Perfect positive correlation (+1.00) No relationship (0.00)Perfect negative correlation (-1.00) Scatterplots, showing patterns of correlations

26 Correlation Height and Temperament of 20 Men 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 80 63 61 79 74 69 62 75 77 60 64 76 71 66 73 70 63 71 68 70 75 66 60 90 60 42 60 81 39 48 69 72 57 63 75 30 57 84 39 Subject Height in Inches Temperament Subject Height in Inches Temperament

27 Correlation Scatterplot of Height and Temperament 55 60 65 70 75 80 85 95 90 85 80 75 70 65 60 55 50 45 40 35 30 25 Temperament scores Height in inches

28 Figure 2.14 Interpreting correlation coefficients

29 Correlation: Prediction, Not Causation Higher correlation coefficients = increased ability to predict one variable based on the other –SAT/ACT scores moderately correlated with first year college GPA 2 variables may be highly correlated, but not causally related –Foot size and vocabulary positively correlated –Do larger feet cause larger vocabularies? –The third variable problem

30 Correlation Three Possible Cause-Effect Relationships (1) Low self-esteem Depression (2) Depression Low self-esteem Depression (3) Distressing events or biological predisposition could cause or and

31 Two Random Sequences  Your chances of being dealt either of these hands is precisely the same: 1 in 2,598,960.

32 Figure 2.15 Three possible causal relationships between correlated variables

33 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

34 Evaluating Research: Methodological Pitfalls Sampling bias Placebo effects Distortions in self-report data: –Social desirability bias –Response set Experimenter bias –the double-blind solution

35 Figure 2.16 The relationship between the population and the sample

36 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

37 Figure 2.17 Ethics in research

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