Chapter 1: Variables and Research Design

Research Hypothesis

________: our prediction of how specific variables might be related to one another or how groups of participants might be different from each other.

Categorical

________: values that the variables can take are categories.

Variable

________: something that can vary; it can take on many different values or categories.

Nominal Scales

________: consist of categories that are not ordered in any particular way.

Correlational Designs

________: those that investigate relationships between variables.

Streiner

________ highlights research that has shown that analyses using dichotomous variables are about 67 % as efficient as analyses using the original continuous /discrete measures.

Ratio Scales

________: have equal intervals between adjacent scores on the scale and an absolute zero.

Maxwell

________ and Delaney (1993) have shown that dichotomizing continuous variables can actually lead to spurious findings arising from statistical analyses.

Demand Effects

________: participants want to do what the experimenter wants them to and so may perform how they believe they should do rather than how they would normally have done.

Random Allocation

________: one of the major defining features of an experimental design; if we ________ participants to conditions, we can be more confident in our ability to infer a causal relationship between the IV and the DV.

Quasi Experimental Design

________: involve seeing if there are differences on the DV between conditions of the IV; there is not random allocation of participants to the various conditions of the IV.

Discrete

________: variables can take on only certain discrete values within the range.

Ordinal Scales

________: have some sort of order in the categories (e.g., in terms of magnitude) but the intervals between adjacent points on the scale are not necessarily equal.

Order Effects

________: a consequence of within- participants designs whereby completing the conditions in a particular order leads to differences in the DV that are not a result of the manipulation of the IV; differences between the conditions of the IV might be due to practice, fatigue, or boredom rather than to the experimenters manipulation of the IV.

main variables

Confounding Variable: a specific type of extraneous variable that is related to both of the ________ that we are interested in.

Variable

something that can vary; it can take on many different values or categories

Examples

gender, typing speed, top speed of a car, number of reported symptoms of an illness, temperature, attendances at rock festivals, level of anxiety

Continuous

variables that can take any value within a given range; the variable itself doesnt change in discrete jumps

Example

temperature

Example

level of anxiety

Discrete

variables can take on only certain discrete values within the range

Example

reported number of symptoms of an illness that a person has

Categorical

values that the variables can take are categories

Example

gender

Example

type of occupation (e.g., judge, teacher, miner, etc.)

Nominal Scales

consist of categories that are not ordered in any particular way

Example

gender (male or female)

Ordinal Scales

have some sort of order in the categories (e.g., in terms of magnitude) but the intervals between adjacent points on the scale are not necessarily equal

Interval Scales

have equal intervals between adjacent scores but do not have an absolute zero

Example

temperature (zero point does not equate to zero temperature)

Ratio Scales

have equal intervals between adjacent scores on the scale and an absolute zero

Example

speed (zero point means zero speed)

Extraneous Variables

variables that might have an impact on the other variables that we are interested in but we may have failed to take these into account when designing our study

Confounding Variable

a specific type of extraneous variable that is related to both of the main variables that we are interested in

Correlational Designs

those that investigate relationships between variables

Experimental Design

those where the experimenter manipulates one variable (IV) to see what effect this has upon another variable (DV); we are usually looking for differences between conditions of the IV; a hallmark of this design is random allocation of participants to the conditions of the IV

Independent Variable (IV)

the variable manipulated by the experimenter; its value is not dependent upon (is independent of) the other variables being investigated

Dependent Variable (DV)

it is assumed to be dependent upon the value of the IV

Research Hypothesis

our prediction of how specific variables might be related to one another or how groups of participants might be different from each other

Random Allocation

one of the major defining features of an experimental design; if we randomly allocate participants to conditions, we can be more confident in our ability to infer a causal relationship between the IV and the DV

Quasi-Experimental Design

involve seeing if there are differences on the DV between conditions of the IV; there is not random allocation of participants to the various conditions of the IV

Within-Participants Designs

have the same participants in every condition of the IV; each participant performs under all conditions of the study

Order Effects

a consequence of within-participants designs whereby completing the conditions in a particular order leads to differences in the DV that are not a result of the manipulation of the IV; differences between the conditions of the IV might be due to practice, fatigue, or boredom rather than to the experimenters manipulation of the IV

Counterbalancing

where you systematically vary the order in which participants take part in the various conditions of the IV; this would be introduced into a study where you have a within-participants design

Demand Effects

participants want to do what the experimenter wants them to and so may perform how they believe they should do rather than how they would normally have done

Between-Participants Designs

have different groups of participants in each condition of the IV; thus, the group of participants in one condition of the IV is different from the participants in another condition of the IV