(Source: Sommer, B and Sommer, R. A (1997). A Practical Guide to Behavioral Research)
Alternative Hypothesis: Working or research hypothesis, and alternative to the null hypothesis, stated in positive terms (i.e., that there will be an effect)
Analysis of Covariance (ANCOVA): Takes into account scores on some other variable believed to affect the action of the independent variable
Analysis of Variance (ANOVA): Statistical technique for comparing differences between means
Anonymity: Researcher does not know the identity of the participants in the study
Applied Research: Systematic procedures to answer pressing questions
Attitude Scale: Type of questionnaire designed to produce scores indicating the overall degree of favorability of a person’s attitude on a topic
Average: Generic term referring to various measures of central tendency- the mean, median, and mode
Bar Graph: A graphic representation of results using bars to indicate frequency of response
Blind Testing: In a sensory evaluation or drug research, blind testing indicates the subject is not aware of the stimulus.
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Case Study: In-depth investigation of a single instance
Categorical: Refers to variables that have levels that are mutually exclusive
Causal Patterns: Relationships of cause and effect
Central Tendency: Number (statistic) that best summarizes the characteristics of a sample as a whole
Chi-Square (X Squared): Statistical test used with categorical data to test whether an obtained distribution of scores differs reliably from what would be expected by chance.
Coding: Transformation or reduction or raw data into a set of standard categories for statistical analysis
Comparative Rating Scales: Respondent compares individual with others in the same category
Concurrent Validity: Correlation of a test with present behavior, or with other existing tests or measures; one type of criterion validity
Confidentiality: Participant or respondent identity is known to researcher, but is not publicly revealed.
Confounding: Confusion of the effects of variables, resulting in the inability to determine which variable is the cause of the observed effect.
Constants: Qualities that do not vary
Construct Validity: Linkage of the test measurement to specific theoretical constructs, the relationship of a test to theory
Content Validity: Degree to which test items assess the domain that a test claims to cover, the relevance of the items to the behavior measured
Contingency Table: Table used to record the relationship between two or more variables. The observed frequencies are placed into the cells of the table.
Continuous Variable: Variable whose levels can take on any value within the lowest and uppermost limit of the variable, for example time or income
Control Group: Subject group that resembles the experimental group in every respect except that it is not exposed to the independent variable
Convenience sampling: taking what is available as your sample
Correlated measures: Groups being compared have been matched on some characteristic, or the same people have been tested before and after an experimental treatment
Correlation: Association between two sets of scores. Often expressed in terms of a correlation coefficient
Correlation Coefficient: Indicates the degree of relationship between two sets of scores, a number that can range from +1.0 to -1.0.
Criterion Validity: Relationship of the test scores to other measures of the same characteristics
Degrees of Freedom: (df): Number of values that are free to vary after certain restrictions have been placed on the data. Used to evaluate the results of various statistical tests; related to the sample size and number of levels of the independent variable
Dependent Variable: Consequence or outcome or the manipulation, the variable that is affected by the independent variable; synonyms are outcome, response, or criterion variable
Descriptive statistics: Techniques to organize and summarize data
Equivalent forms: Two different, but comparable, forms of a scale; used for assessing reliability or to avoid practice effects
Expected Frequencies (E): Represent the null hypothesis- what you would expect by chance
Experimental Group: Subjects exposed to the levels of the independent variable; also called the treatment group
Experimental Variable: Variable that is manipulated or systematically altered by the experimenter; synonyms are independent variable or predictor variable
External Validity: Generalizability of research findings
Extraneous Variables: Variables, in addition to the independent variable, that might be affecting the dependent variable (outcome)
Face Validity: Appearance of being a valid measure of something; a measure that “looks right” to an outside observer. A form of content validity
Factor: Independent variable or treatment. A study investigating the effects of age and education on attitude would be a two-factor study.
Factorial Design: ANOVA design involving more than one independent variable (factor)
Focus Group: Group interview designed to explore what a specific set of people think and feel about a topic
Format: Structure; pertains to the appearance, order, and wording of items in a questionnaire or interview; in contrast to content, which refers to meaning
Frequency: Number of times a score or category level occurs
Frequency Distribution: Arrangement of scores from highest to lowest, together with the frequency of each score
Histogram: Particular type of bar graph
Hypothesis: testable statement logically derived from theory or observation; can be either confirmed (accepted) or disconfirmed (rejected)
Independent Variable: Variable that is manipulated in order to measure its effect on some outcome; synonyms are experimental variable, or predictor variable
Inferential statistics: Statistical techniques used to make generalizations from samples to populations
Informed Consent: Those who participate in research studies should understand what is involved and freely consent to participate
Institutional Review Board (IRB): Group of people with formal responsibility for reviewing submitted research proposals in terms of ethics and protection of the participants
Interaction: Using ANOVA, the outcome produced by changes in one variable differs depending upon the levels of a second variable
Internal Validity: Degree to which a procedure measures what it is supposed to measure
Interval Scale: Level of measurement that provides information about size or direction, plus having equal intervals between scale points
Item Analysis: Shows the degree that the various items in a scale or test “hang together”
Likert Scale: Type of attitude scale containing statements that are clearly favorable or clearly unfavorable, to which respondent indicates degree of agreement
Matched Groups: Assigning subjects so that the experimental and control groups are as similar as possible.
Mean Comparisons: Comparisons or contrasts between individual means to locate where the significant difference lies.
Median: Midpoint of a distribution when all the scores are arranged from highest to lowest.
Mode: Single score that occurs most often in a distribution.
Nominal Measures: Characteristics assigned to categories. No underlying continuous dimension
Normal Curve: Symmetrical bell-shaped curve which often approximates the frequency of occurrence of events in nature
Null Hypothesis: Assumes that differences produced by the research manipulation are due to chance fluctuations and that the independent variable has no effect on the dependent variable. Generally, the researcher hopes to disprove the null hypothesis.
Ordinal Scale: Characteristics can be ordered along an underlying dimension, but no information is provided about the distance between points; only provides information about increasing or decreasing size or direction
Pilot Study: Preliminary use of a procedure designed to identify problems and omissions before the actual study is conducted
Positive correlation: An increase in one variable is accompanied by an increase in the other
Probability Level: When used in statistics, it indicates the likelihood that an obtained difference on a statistical test is due to chance alone.
Probability Sample: Sample that is drawn in such a way that the probability for the inclusion of nay given individual can be estimated. Two general types of probability samples are random samples and stratified samples
Purposive Sample: Type of non-probability sample in which the individuals considered most relevant to the issue studied are selected for inclusion
Quasi-experiment: Non-random assignment of subjects to conditions; the experimenter lacks direct control over the independent variable; also called a natural experiment
Random sample: Type of probability sample in which every individual in the entire population being studied has an equal chance of being selected
Range: Measure of dispersion or variability, computed by subtracting the lowest from the highest score
Ratio Scale: Level of measurement that contains information on direction, possesses equal intervals, and an absolute zero point
Regression: Pertains to the correlation; the relationships between sets of scores
Reliability: Consistency in measurement; the repeatability or replicability of findings; stability of measurement over time
Reliability Coefficient: Coefficient of correlation between two administrations of a test or other measures
Repeated Measures Design: Research design involving the same subjects studied at different times or under different conditions
Sample: Subset of the population
Sample Bias: Error introduced by a sampling procedure that favors certain characteristics over others
Sampling Error: Chance variation among samples selected from a single population
Snowball Sample: Type of purposive (non-random) sample in which the researcher asks respondents for other people that should be contacted
Split-half Reliability: Dividing a scale or test into two halves which are then compared
Standard Deviation: Measure of dispersion or variability; abbreviated as s or SD
Stratified Sample: Type of probability sampling in which the characteristics of the sample are selected to be proportionate to those present in the total population
Treatment Condition: Refers to the presence of the independent variable, as opposed to the control condition in which the independent variable is absent
Treatment Group: Subjects exposed to the levels of the independent variable; also called the experimental group
Variability: Amount of spread or dispersion within a distribution of scores