Chapter 2

Research Methods

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

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

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

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

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

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

Figure 2.7 Manipulation of two independent variables in an experiment

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

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

Figure 2.9

Sample from a case study – a descriptive research method

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

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?

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

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

Figure 2.13 Positive and negative correlation

XX 2.14

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

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 result

Evaluating Research: Methodological Pitfalls

• Sampling bias • Placebo effects – is not always uniform – cost

factors and perceived pain• 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.

Ethics in Psychological Research: Do the Ends

Justify the Means?

• 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