**Bivariate Statistics:** Considers two variables together and describes the relationship between variables (e.g. crosstabulation)

**Closed-ended questions:** Both asks a question and gives respondents specific answer categories to choose from.

**Conjoint Analysis:** Mail or intercept survey measuring the relative importance of a set of identified attributes that people may consider when choosing between competing candidates, products or services.Focus Group: Group discussion of 8-10 people led by a skilled moderator who directs the discussion to generate unforeseen hypotheses and probe for complicated perceptions and feelings. Also see *Qualitative Analysis*.

**Intercept Survey:** A questionnaire administered face-to-face to a sample of respondents as they pass by the interviewer.

**Interval Data:** Indicates a difference among categories, that the categories can be ranked, and specifies an equal distance between categories (e.g. IQ score-95, 110, 125...).

**Multivariate Statistics:** Analysis of the simultaneous relationships among several variables. Examples include logistic regression and factor analysis.

**Nominal Data:** Indicates that there is a difference among categories (e.g. gender).

**Nonsampling Error:** Errors other than those resulting from the measuring a sample rather than a population (e.g. measurement errors, data entry errors, question bias, sample bias).

**Null Hypotheses:** The statistical assumption that observed differences are due to chance (sampling error) and that the observed relationship between variables is due to chance.

**Open-ended Question:** An unstructured question allowing respondents to answer in their own words.

**Ordinal Data:** Indicates a difference among categories plus that the categories can be ordered and ranked (e.g. letter grades - A, B, C, D...).

**Population:** The complete set of individuals about whom the researcher is interested.

**Probability Sample:** Virtually every unit in the population has a known and non-zero probability of being included in the sample.

**Qualitative Research:** Consists of observations not easily reduced to numbers, such as a focus group.

**Quantitative Research:** Consist of information in numerical form, such as a telephone survey.

**Sample:** A subset of a population used to collect data.

**Sample Survey:** Research whereby a subset of the population is measured.

**Sampling Error:** The difference between a statistic and a parameter that is due to the use of the sample rather than the census.

**Sampling Frame:** The list (or quasi-list) of units from which a sample is selected (e.g. a telephone directory).

**Statistical Significance:** The conclusion that the apparent sample relationship between two variables is not due to chance (sampling error).

**Strength of Association:** The question of how well two variable are related (correlated). Statistical significance should not be confused with Strength of Association.

**Univariate Statistics:** Describes a single variable. An example is a frequency table or measure of central tendency.

**Variance:** A measure of the average squared variability, calculated by dividing variation by the number of scores ( or the number of scores minus one when a sample is used to estimate population).

**Variation:** A measure of total squared variability, calculated by summing across the squared difference between each score and mean.