Unit 0: Foundation Methods and Approaches

Experiment

an investigation seeking to understand relations of cause and effect

Independent Value

the manipulated variable within an experiment

Dependent Variable

the variable being measured

Control Variable

the constant variable within both groups

Population

group of interest to be studied

Representative Sample

drawn when the population is to large to study effectively

Representativeness

the degree to which a sample reflects the diverse characteristics of the population that is being studied

Experimental Group

the group receiving or reacting to the independent variable

Control Group

the group that does not receive the independent variable but should be kept identical in all other aspects

Random Sampling

way of ensuring maximum representativeness

Randomly Assigned

done to ensure that each group has minimal differences

Biases

• Bias of Selection - when people are selected in a physical space

• Self-Selection Bias - when the people being studied have some control over whether or not to participate

• Pre-Screening/Advertising Bias - often occurs in medical search; how volunteers are screened or where advertising is placed might skew the sample

• Healthy User Bias - occurs when the study population tends to be in better shape than the general population

Single/Double-blind Design

• Single-blind Design - the subjects don’t know whether they are in the control or experimental group

• Double-blind Design - neither the subjects nor the researcher knows who is in either group. These are designed so the experimenter doesn’t inadvertently change the responses of the subject through personal biases.

Placebo

seemingly therapeutic object or procedure, which causes the control group to believe they’re in the experimental

Correlational Research

assessing the degree of association between two or more variables or characteristics of interest that occur naturally

• Researchers do not directly manipulate variables but observe naturally occurring differences

• Correlation does NOT equal Causation! The former only shows the strength of the relationship among variables.

Confounding/Third/Extraneous Variable

an unknown factor playing a role within an experiment/naturally occurring event

Surveys

one way to gather information for correlational studies. An accumulation of tremendous amount of data and study relationships among variables.

Longitudinal Studies

a study happening over long periods of time with the same subjects

Cross-Sectional Studies

designed to test a wide array of subjects from different backgrounds to increase generalizability

Clinical Research

takes the form of case studies

Case Studies

intensive psycholofical studies of single individuals

Generalizable

applicable to similar circumstances because of the predictable outcomes of repeated tests

Conceptual Definition

the theory or issue being studied

Operational Definition

the way in which that theory or issue will directly observed or measured in the study

Internal Validity

certainty with which the results of an experiment can be attributed to the manipulation of the independent variable rather than to some other, confounding variable. Principal threats are confounding variables which haven’t been adequately controlled by the experimenter.

External Validity

the extent to which the findings of a study can be generalized to other contexts in the real world. Principal threat is the often-artifical nature of experimental environment.

Reliability

the same results appear if the experiment is repeated under similar conditions

Inter-rater Reliability

the degree to which different raters agree on their observations of the same data

Naturalistic Observation

allows the study of the authentic real-world behaviors, disadvantage is the difficulty of controlling for the numerous extraneous variables present in real-world environments which can limit the reliability of findings

Qualitative Research

provides detailed descriptions of experience rather than the numerical data of quantitative methods

Descriptive Studies

summarize data

Inferential Statistics

allow researches to test hypotheses about data and determine how confident they can be in their inferences about the data

Central Tendency

characterize the typical value in a set of data

Mean

average of a set of numbers

Mode

most frequently occurring value in the data set

Bimodel

two numbers both appearing with the greatest frequency

Median

number that falls exactly in the middle of a distribution of numbers

Normal Curve

mean, mode, median are represented by this, in a normal distribution the mean, median, and mode are identical.

Range

the largest number minus the smallest

Variability

how much the numbers in the set differ from one another

Standard Deviation

measures a function of the average dispersion of numbers around the mean and is a commonly used measure of variability

Percentile

expresses the standing of one score relative to all other scores in a set of data

Positive Skew

most values are on the lower end, but there are some exceptionally large values.

Negative Skew

the direct opposite - most values are on the higher end but there are some exceptionally small values.

Correlation Coefficient

numerical value that indicates the degree and direction of the relationship between two variables

Pearson Correlation Coefficient

specific type of correlation coefficient that describes how close to linear the relationship between two attributes is

Positive Correlation

as x increases, so does y

Negative Correlation

as x increases, y decreases

Sample Size

the number of observations or individuals measured. The larger the sample size, the more likely that inferences made about the broader population are correct. However, sample size is typically determined based on convenience, expense, and the need to have sufficient statistical power

Null Hypothesis

states that a treatment had no effect in an experiment

Alternative Hypothesis

states that the treatment did have an effect

Alpha

the accepted probability that the result of an experiment can be attributed to chance rather than the manipulation of the independent variable

Type I Error

conclusion that a difference exists when it actually doesn’t

Type II Error

refers to the conclusion that there is no difference when there is one

P-value

the probability of making a Type I error

Deception

can be used if informing the participants of the nature of the experiment might bias results

Stanley Milgram

1970s experiment where he conducted obedience experiments where he convinced participants they were giving painful electric shocks to other when none were given

Confederates

people aware of the true nature of the experiment but pretended to be participants

Institutional Review Boards (IRBs)

assess research plans before the research is approved to ensure that it meets all ethical standards

Informed Consent

participants must agree to participate in the study only after being told what the experiment entails

Debriefing

at the conclusion of the experiment, participants are told the exact purpose of their participation in the research and of any deception that might have occurred during it.

Confidentiality

involving collecting sensitive information about participants that is anonymous