Chapter 7 Glossary

ex post facto research: when a researcher goes back, after the research has been completed, looking to test hypotheses that were not formulated prior to the beginning of the study. (p.170)


archival data: data from existing records and public archives. (p. 171)


instrumentation bias: apparent changes in participants that are really due to changes in the measuring instrument. A real problem in archival research because the way records are kept may change over time. For example, unemployment statistics are difficult to interpret because the government has changed its definition of unemployment. (p. 175)


content analysis: a way to categorize a wide range of open-ended (unrestricted) responses. Content analysis schemes have been used to code the frequency of violence on certain television shows and are often used to code archival data.

 (p. 173)


nonreactive: measurements that are taken without changing the participant’s behavior. Researchers in both participant and naturalistic observation try to be nonreactive, but both often fail. (p. 176)


participant observation: an observation procedure in which the observer participates with those being observed. The observer becomes “one of them.” Some worry that, in participant observation, the observer may change the behavior of the people being observed. (p. 178)


naturalistic observation: a technique of observing events as they occur in their natural setting—without participating in those events. Advocates believe that naturalistic observation has more external validity than lab observation. In addition, they hope that naturalistic observation will be less reactive than either participant observation or lab observation. (p. 178)


scatterplot: a graph made by plotting the scores of individuals on two variables (for instance, plotting each participant’s height and weight). By looking at this graph, you should get an idea of what kind of correlation (positive, negative, zero) exists between the two variables. (p. 187)


positive correlation: a relationship between two variables in which the two variables tend to change in the same direction—when one increases, the other tends to increase. (For example, height and weight: The taller one is, the more one tends to weigh; the less tall one is, the less one tends to weigh.) (p. 188)


negative correlation: a relationship between two variables in which the two variables tend to change in opposite directions—when one is high or increases, the other tends to below or decrease. An example of this inverse relationship between two variables would be happiness and depression: The more happy one is, the less depressed one is. (p. 189)


zero correlation: when there doesn’t appear to be a linear relationship between two variables. For practical purposes, any correlation between –.10 and +.10 may be considered so small as to be nonexistent. (p. 189)


illusory correlation: when there is really no relationship (a zero correlation) between two variables, but people perceive that the variables are related. (p. 169)


correlation coefficient: a number that can vary from –1.00 to +1.00. The sign of the correlation coefficient indicates the kind of relationship that exists between two variables (positive or negative). The correlation coefficient also indicates the strength of the relationship. Specifically, the closer the correlation coefficient is to 0, the weaker the relationship; the farther from 0(regardless of the sign), the stronger the relationship. (p. 194)


coefficient of determination: the square of the correlation coefficient; tells the degree to which knowing one variable helps to know another. Can range from 0 (knowing a participant’s score on one variable tells you absolutely nothing about the participant’s score on the second variable) to 1.00 (knowing a participant’s score on one variable tells you exactly what the participant’s score on the second variable was). Note that the sign of the correlation coefficient (whether it is positive or negative) has absolutely no effect on the coefficient of determination. (p. 194)


restriction of range: a problem caused by when participants studied only represent a narrow range of scores on a key variable. Restriction of range is a problem because to observe a sizable correlation between two variables, both must be allowed to vary widely (if one variable does not vary, the variables cannot vary together). Occasionally, investigators fail to find a relationship between variables because they study one or both variables only over a highly restricted range. For example, saying that weight has nothing to do with playing offensive line in the NFL on the basis of your finding that great offensive tackles do not weigh much more than poor offensive tackles. Problem: You only compared people who ranged in weight from 315 to 330 pounds. (p. 199)


median: if you arrange all the scores from lowest to highest, the middle score will be the median. (p.183)


median split: the procedure of dividing participants into two groups (“highs” and “lows”) based on whether they score above or below the median. (p.187)