Presentation on theme: "Chapter 2 The Research Enterprise in Psychology. n Basic assumption: events are governed by some lawful order Goals: Measurement and description Understanding."— Presentation transcript:
n Basic assumption: events are governed by some lawful order Goals: Measurement and description Understanding and prediction Application and control The Scientific Approach: A Search for Laws
n Operational definitions are used to clarify what variables mean n Statistics are used to analyze data and decide whether hypotheses were supported n Findings are shared through reports at scientific meetings and in scientific journals n Research methods: general strategies for conducting scientific studies Tools of the Trade: Definitions, Data, Journals, and Methods
Figure 2.1 Theory construction. A good theory will generate a host of testable hypotheses. In a typical study, only one or a few of these hypotheses can be evaluated. If the evidence supports the hypotheses, our confidence in the theory they were derived from generally grows. If the hypotheses are not supported, confidence in the theory decreases and revisions to the theory may be made to accommodate the new findings. If the hypotheses generated by a theory consistently fail to garner empirical support, the theory may be discarded altogether. Thus, theory construction and testing is a gradual process.
Figure 2.2 Flowchart of steps in a scientific investigation. As illustrated in a study by Cole et al. (1996), a scientific investigation consists of a sequence of carefully planned steps, beginning with the formulation of a testable hypothesis and ending with the publication of the study, if its results are worthy of examination by other researchers.
n 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 n How does X affect Y? X= Independent Variable and Y= Dependent Variable Experimental Research: Basics
n Experimental group – subjects who receive some special treatment in regard to the independent variable n 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 Experimental and Control Groups: The Logic of the Scientific Method
n Expose a single group to two different conditions Reduces extraneous variables n Manipulate more than one independent variable Allows for study of interactions between variables n Use more than one dependent variable Obtain a more complete picture of effect of IV Experimental Designs: Variations
Figure 2.6 Manipulation of two independent variables in an experiment. As this example shows, when two independent variables are manipulated in a single experiment, the researcher has to compare four groups of subjects (or conditions) instead of the usual two. The main advantage of this procedure is that it allows an experimenter to see whether two variables interact.
n Strength: conclusions about cause-and-effect can be drawn n Weaknesses: artificial nature of experiments ethical and practical issues Strengths and Weaknesses of Experimental Research
n Methods used when a researcher cannot manipulate the variables under study Naturalistic observation Case studies Surveys n Allow researchers to describe patterns of behavior and discover links or associations between variables but cannot imply causation Descriptive/Correlational Methods: Looking for Links
Figure 2.10 Comparison of major research methods. This chart pulls together a great deal of information on key research methods in psychology and gives a simple example of how each method might be applied in research on aggression. As you can see, the various research methods each have strengths and weaknesses.
n Statistics – using mathematics to organize, summarize, and interpret numerical data Descriptive statistics: organizing and summarizing data Inferential statistics: interpreting data and drawing conclusions Statistics and Research: Drawing Conclusions
n 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 n Which most accurately depicts the typical? Descriptive Statistics: Measures of Central Tendency
Figure 2.11 Measures of central tendency. The three measures of central tendency usually converge, but that is not always the case, as these data illustrate. Which measure is most useful depends on the nature of the data. Generally, the mean is the best index of central tendency, but in this instance the median is more informative.
n 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 Descriptive Statistics: Variability
n 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 Descriptive Statistics: Correlation
Figure 2.13 Positive and negative correlation. Notice that the terms positive and negative refer to the direction of the relationship between two variables, not to its strength. Variables are positively correlated if they tend to increase and decrease together and are negatively correlated if one tends to increase when the other decreases.
Figure 2.14 Interpreting correlation coefficients. The magnitude of a correlation coefficient indicates the strength of the relationship between two variables. The sign (plus or minus) indicates whether the correlation is positive or negative. The closer the coefficient comes to +1.00 or –1.00, the stronger the relationship between the variables.
Figure 2.15 Three possible causal relations between correlated variables. If variables X and Y are correlated, does X cause Y, does Y cause X, or does some hidden third variable, Z, account for the changes in both X and Y? As the relationship between smoking and depression illustrates, a correlation alone does not provide the answer. We will encounter this problem of interpreting the meaning of correlations frequently in this text.
n Higher correlation coefficients = increased ability to predict one variable based on the other n SAT/ACT scores moderately correlated with first year college GPA n 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 Correlation: Prediction, Not Causation
n Hypothesis testing: do observed findings support the hypotheses? n Are findings real or due to chance? n Statistical significance = when the probability that the observed findings are due to chance is very low Inferential Statistics: Interpreting Data and Drawing Conclusions
n Sampling bias n Placebo effects n Distortions in self-report data: Social desirability bias Response set n Experimenter bias and the double-blind solution Evaluating Research: Watch for Methodological Pitfalls
n The question of deception n The question of animal research Controversy among psychologists and the public n Ethical standards for research: the American Psychological Association Ensures both human and animal subjects are treated with dignity Ethics in Psychological Research: Do the Ends Justify the Means?
Figure 2.17 Ethics in research. Key ethical principles in psychological research, as set forth by the American Psychological Association (1992), are summarized here. These principles are meant to ensure the welfare of both human and animal subjects.