Quantitative Research Terms

Quantitative Research

Quantitative Research

method of research that relies on measuring variables using a numerical system.

(quantitative = quantity)

Variables

any characteristic that is objectively registered and quantified.

Constructs

theoretically defined variables. To define a construct, you muct separate it from similar constructs.

Operationalization

expressing a construct in terms of observable behavior, makes it clear what is being measured.

Independent Variable (IV)

variable that is being manipulated by researcher.

Dependent Variable (DV)

how the condition changes based on what changed in the IV.

Controls

variables that are constant and don’t change, they are controlled and make sure to not affect the IV and DV.

Hypothesis

predicts how the IV affects the DV

Null Hypothesis: no relationship - results are just due to chance

Experimental Hypothesis: there IS a cause-and-effect relationship.

Extraneous Variables

variables that can distort relationships between the IV and the DV.

True Lab Experiment

randomlly allocated (split) participants into control groups and experimental groups.

Field Experiments

studies conducted in the real world yet researchers can still manipulate the study (is in an environment that is not a lab).

Quasi Experiments

purposefully put people of race/gender into specific groups in order to test stereotypes

Natural Experiments

beyond the control of researcher, they are just observing. Cannot establish a cause-&-effect relationship.

Demand Characteristics

when participants act differently because they know the intention of the study.

Expectancy Effect

participants aim to guess the hypothesis in order to “help” the researcher by acting in a certain way.

Screw-You Effect

participant attempts to detect the hypothesis in order to purposefully throw off the study.

Social Desireability

When a participant attempts to present themselves in a generally favorable manner, in order to conceal their true opinions.

Researcher Bias

when researchers expectations affect the result of the study, double blind control can help with this (where participant and researcher don’t know who is in what condition).

Participant Variability

when characteristics of the participant affect the dependent variable.

Correlational Studies

(ex: surveys, questionnaires, naturalistic observations) Variables are NOT manipulated, but data is still collected in order to show that there is a relationship between them.

Positive/Negative correlation: they change together or co-vary.

Correlation

• Tests association between variables.

• variables are ONLY observed ( no manipulation by researcher ).

• Limited control allows other variables to be present.

• High external validity.

Statistical Significance

the likelihood that your data has a cause-and-effect relationship. Shows how reliable you data is. If it is GREATER than 5%, it is considered to be “non-reliable”.

Independent Measures Design

random allocation of (dividing of) participants into control and experimental groups and then a comparison of the two groups. So only the IV differs in each.

Matched Pairs Design

Participants are tested on a variable, matched up based on performance & then split up between the 2 conditions (to balance both conditions out).

Example:  a researcher testing a new Alzheimer's disease drug matched up participants of the same age & intelligence, then randomly assigned a person per pair to a group receiving the drug (experimental group) and the other person from the pair to the group that will not receive it (control group).

Repeated Measures Design

where each condition of the experiment uses the same group of participants. Goal is to compare the CONDITIONS rather than the groups.

Example: in a candy taste test, the researcher would want every participant to taste and rate each type of candy.

Order Effects

occurs when the participants' responses are affected by the ORDER in which the conditions of the experiment were presented to them.

Counterbalancing

where the participant sample is divided in half - one half completing the experiment in a specific order, and the other half completing the experiment in REVERSE order.