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Understanding Non-Experimental Research Techniques in Psychology

Explore non-experimental research methods like naturalistic and unobtrusive observation, survey techniques, and correlational studies in psychology. Learn how to avoid biases and interpret correlations effectively.

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Understanding Non-Experimental Research Techniques in Psychology

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  1. Experimental PsychologyPSY 433 Chapter 2 Observation and Correlation

  2. Does Country Music Cause Suicide? http://www.youtube.com/watch?v=-Xu71i89xvs • Stack & Gundlach found that metro areas that played more country music had higher suicide rates, concluding that country music causes suicide. • Maybe depressed people seek out sad music? • http://www.jstor.org/stable/2580303 • Or maybe it is a spurious correlation? • http://www.tylervigen.com/spurious-correlations

  3. Non-Experimental Research • Variable -- a characteristic that can have different values (height, weight). • Value -- usually a single, specific number (6 feet tall, 140 pounds). • Measurement – the process of assigning numbers to entities in the world. • In non-experimental research, variables may be measured, but nothing is being manipulated by the experimenter. • No independent variable, just DVs.

  4. Naturalistic Observation • Methods for observing behavior in its natural environment. • Behavior is complex and humans have limited attention span, so we delimit, or narrow, the range of behaviors we plan to observe. • Reactivity -- subjects may behave differently than usual when they know they are being observed.

  5. Unobtrusive Observation • Unobtrusive observation -- subject is unaware of being observed in presence of observer • Example: chivalry study, bathroom study. • Unobtrusive measurement -- observer collects evidence in absence of subject and infers behavior of subject. • Example: graffiti study, collecting scat • There is a danger in anthropomorphizing or incorrectly interpreting what is observed. • Participant observation – “going native”

  6. Survey Techniques • Gives a picture of people’s attitudes, beliefs, behaviors, and feelings about a topic. • Sample from a population, then infer based on sample -- only as good as the sample. • Return rate may produce sampling problem. • Collect large amounts of data from large number of people quickly. • Does not show causality among variables. • Can also be used to provide data for the correlational method.

  7. Relational Research • Contingency Research • Variables are presented in a contingency table. • A Chi Square statistic is computed to determine whether relationships among variables exist. • Values in the tables are “counts” or frequencies for categories, not measurements. • Data is ex post facto

  8. Correlational methods • Correlation – a statistical technique that expresses the degree of linear relationship between 2 variables. • If the correlation is high, a strong linear relationship exists. • If the correlation is low, a weak relationship exists. • If the correlation is zero, there is no relationship.

  9. Correlation Coefficient (r) • r is a numerical index of the degree of linear relationship between 2 variables. • r is computed by taking into account pairs of scores – one score from one variable and the other score from another variable. • Correlation coefficient (r) has a strength (0-1) and a direction (+ or -). • r allows us to more precisely compare different sets of variables: • SAT & GPA vs IQ & GPA

  10. Using Coefficient (r) • Does income level predict reading level? • Measure income level at grade 1 • Measure reading level at grade 1 • Compute correlation between reading level & income: • What if r = +1.0? What if r = -1.0? • What if r = +.88? What if r = -.88? • What if r = +.15? What if r = -.19? • What if r = 0.0?

  11. Values of r for Multiple Variables

  12. Causality and Correlation • The directionality problem -- for any correlation between X and Y: • X may cause Y • Y may cause X • Z may cause both X and Y • Classic examples: • Hours watching violent TV & violent behaviors (+) • Grades in physics & grades in statistics (+)

  13. Spurious Correlations • Spurious correlation -- a correlation exists but no causal relationship exists. • Spurious correlations sometimes occur because both variables are mediated by a third variable. • Classic examples: • Number of churches in a town and number of murders. • Number of toasters owned in a household and number of teen pregnancies. • Kids in private schools get higher test scores.

  14. Biases • Selection bias – a kind of spurious correlation • Students in Mississippi had a higher average SAT than in California even though California spent more per pupil than Mississippi. • In Mississippi only top students took the SAT whereas in California nearly all took it. • Restriction of range -- a correlation may underestimate the relationship between two variables if the range of either variable is restricted.

  15. An Example of Restricted Range

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