Chapter 5 Glossary

bias: systematic errors that can push the scores in a given direction. Bias may lead to “finding” the results that the researcher wanted. (p. 100)

observer bias: bias created by the observer seeing what the observer expects to see, or selectively remembering/counting/looking for data that support the observer’s point of view (also known as scorer bias). (p. 103)

blind (masked), blind observer: an observer who is unaware of the participant’s characteristics and situation. Using blind observers reduces observer bias. (p. 105)

operational definition: a publicly observable way to measure or manipulate a variable; a “recipe” for how you are going to measure or manipulate your factors. (p. 98)

random error of measurement: inconsistent, unsystematic errors of measurement. Carelessness on the part of the person administering the measure, the person taking the test, and the person scoring the test can cause random error. (p. 100)

reliable, reliability: the extent to which a measure produces stable, consistent scores. Measures are able to produce such stable scores if they are not strongly influenced by random error. A measure can be reliable, but not valid. However, if a measure is not reliable, it cannot be valid. (p. 111)

test-retest reliability: away of assessing the total amount of random error in a measure by administering the measure to participants at two different times and then correlating their results. Low test-retest reliability could be due to inconsistent observers, inconsistent standardization, or poor items. Low test-retest reliability leads to low validity. (p. 113)

interobserver agreement: the percentage of times the raters agree. (p. 116)

interobserver reliability: like interobserver agreement, interobserver reliability is an index of the degree to which different raters rate the same behavior similarly. Low interobserver reliability probably means that random observer error is making the measure unreliable. (p. 116)

internal consistency: the degree to which all the items on a measure correlate with each other. If you have high internal consistency, all the questions seem to be measuring the same thing. If, on the other hand, answers to some questions are inconsistent with answers to other questions, this inconsistency may be due to some answers being (1) strongly influenced by random error or being (2) influenced by different constructs. Internal consistency can be estimated through average correlations, split-half reliability coefficients, and Cronbach’s alpha. (p. 120)

subject (participant) biases: ways the participant can bias the results. The two main subject biases are (1)trying to help the researcher out by giving answers that will support the hypothesis, and (2) giving the socially desirable response. (p. 107)

social desirability bias: participants acting in a way that makes the participant look good. (p. 110)

demand characteristics: aspects of the study that allow the participant to figure out how the researcher wants that participant to behave. (p. 107)

unobtrusive measurement: recording a particular behavior without the participant knowing you are measuring that behavior. Unobtrusive measurement reduces subject biases such as social desirability bias and obeying demand characteristics. (p. 109)

Hawthorne Effect: when the treatment group changes its behavior not because of the treatment itself, but because group members know they are getting special treatment. (p. 133)

Instructional manipulation: manipulating the variable by giving written or oral instructions. (p.134)

environmental manipulation: a manipulation that involves changing the participant’s environment rather than giving the participant different instructions. (p. 134)

stooges (confederates): people who seem (to the real participants) to be participants, but who are actually the researcher’s assistants. (p. 136)

construct validity: the degree to which an operational definition reflects the concept that it claims to reflect. Establishing content, convergent, and discriminant validity are all methods of arguing that your measure has construct validity. (p. 124)

content validity: the extent to which a measure represents a balanced and adequate sampling of relevant dimensions, knowledge, and skills. (p. 124)

convergent validity: validity demonstrated by showing that the measure correlates with other measures, manipulations, or correlates of the construct.
(p. 126)

known-groups technique: a convergent validity tactic that involves seeing whether groups known to differ on a characteristic differ on a measure of that characteristic (e.g., ministers should differ from atheists on a measure of religious beliefs). (p. 127)

discriminant validity: the extent to which the measure does not correlate strongly with measures of constructs other than the one you claim to be measuring. (p. 128)

experimenter bias: experimenters being more attentive to participants in the treatment group or giving different nonverbal cues to treatment group participants than to other participants. (p.132)

standardization: treating each participant in the same (standard) way. Standardization should reduce experimenter bias. (p. 106)

manipulation check: a question or set of questions designed to determine whether participants perceived the manipulation in the way that the researcher intended.

(p. 133)

placebo treatment: a treatment that is known to have no effect. To reduce the impact of subject (participant) bias, the group getting the real treatment is compared to a group getting a placebo treatment—rather than to a group that knows it is getting no treatment. (p. 133)

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