1. Chapter 2 Research Methods

2. The Scientific Approach: A Search for Laws Empiricism: testing hypothesis Basic assumption: events are governed by some lawful order Goals: – Measurement and description – Understanding and prediction – Application and control Goal of theory testing in science: refutation not proving – Karl Popper

3. What is Experimental Research? Explores cause and effect relationships Has control and experimental groups Laboratory experiments are good at controlling variables. Implementing school uniforms causes Less violence in school

5. Steps in Designing an Experiment 1.Hypothesis 2.Design Study: Pick Population: Random Selection then Random Assignment. Operationalize the Variables Identify Independent and Dependent Variables. Look for Extraneous Variables Type of Experiment: Blind, Double Blind etc.. 3.Gather Data 4.Analyze Results 5.Publish

7. 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

8. Experimental and Control Groups: The Logic of the Scientific Method Experimental group – subjects who receive some special treatment in regard to the independent variable Control group – similar subjects who do not receive the special treatment – Logic: Two groups alike in all respects (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

9. 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

10. Figure 2.7 Manipulation of two independent variables in an experiment

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

12. 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

13. Figure 2.9 Sample from a case study – a descriptive research method

15. 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 – use of probability

16. 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. 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 – use of probability

19. 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 – use of probability

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

22. 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

23. Figure 2.13 Positive and negative correlation

24. XX 2.14

25. Correlation: Prediction, Not Causation Higher correlation coefficients = increased ability to predict one variable based on the other Example: 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

26. 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 – Other factors might account for the resultS

28. Evaluating Research: Methodological Pitfalls Sampling bias Placebo effects – is not always uniform – cost factors and perceived pain http://www.youtube.com/watch?v=yfRVCaA5o18 Distortions in self-report data: – Social desirability bias – Response set Experimenter bias – the double-blind solution – Research protocol of clinical trial for drugs – FDA in U.S.

30. Ethics in Psychological Research: Do the Ends Justify the Means? Question of deception http://www.youtube.com/watch?v=KPgpRw9tiuM&feature=r elmfu The question of animal research – Controversy among psychologists and the public http://www.youtube.com/watch?v=l0QXUHeGeOc Ethical standards for research: the American Psychological Association – Ensures both human and animal subjects are treated with dignity