**Confidence interval/Confidence level **– A percentage or decimal value that tells how confident a researcher can be about being correct. The confidence level states the long-run percentage of the time that a confidence interval will include the true population mean. The confidence level is the amount of uncertainty you can tolerate. Suppose that you have 20 yes-no questions in your survey. With a confidence level of 95%, you would expect that for one of the questions (1 in 20), the percentage of people who answer *yes* would be more than the margin of error away from the true answer. The true answer is the percentage you would get if you exhaustively interviewed everyone. Higher confidence level requires a larger sample size

**Correlation analysis** measures and quantifies the relationships among variables.

**Factor Analysis** is a technique to reduce or form a set of variables to coherent subsets (such as measures). Variables within the subsets are correlated with one another. And variables between the subsets are relatively independent of one another. Factor analysis provides researchers the flexibility of dealing with coarser components rather than every individual variable.

**Margin of Error – **The margin of error is the amount of error that you can tolerate. If 90% of respondents answer *yes*, while 10% answer * no*, you may be able to tolerate a larger amount of error than if the respondents are split 50-50 or 45-55. Lower margin of error requires a larger sample size.

**Mean ** – A measure of central tendency; the arithmetic average.

**Pearson’s r** summarizes the magnitude and direction of the relationship between variables or between the same variable on pairs of observations. In all situations r can have values that range from -1.0 for a perfect inverse (negative) relationship, through 0 for no relationship, and up to +1.0 for perfect direct (positive) relationship. Absolute values of 0 – .29 are low, .30 – .49 are moderate, and .50 to 1.0 are high.

**Population ** – A complete group of entities sharing some common set of characteristics (i.e. all regional center clients, all Early Start clients, etc.)

**Quantitative Measurement **– Utilization of a numbered scale to collect data that solicits responses from respondents that easily be measured; quantitative data can be extremely useful when the questions are directly focused on specific information needs. Quantitative measurement produces statistical analysis.

**Qualitative Measurement** – When a unique information need does not fit neatly into an item dimension category, it is often a good candidate for an open ended question. Customer responses to such questions provide a wealth of valuable information, usually about areas of concern and suggestions for improvements. These comments can be categorized into codes and analyzed quantitatively as well as qualitatively, providing creative, constructive ideas for action planning.

**R**^{2} indicates the proportion of variance of the dependent variable (overall satisfaction) accounted for by the independent variables (attributes). And the value of R^{2} shows how well attributes explain or predict overall satisfaction. It varies from 0 – 1, with 0 – .29 being low, .30 – .49 moderate, and .50 – 1 high. Researchers almost can never achieve a perfect R^{2} because some proportion of variance of the overall satisfaction is not accounted for by the attributes.

**Range** – The distance between the smallest and largest values of a frequency distribution.

**Regression analysis** is a method of analyzing the variability of a dependent variable (overall satisfaction) by resorting to information available on one or more independent variables (attributes). What are the expected changes in the overall satisfaction as a result of changes (observed or induced) in the attributes?

**Spearman Rank r** summarizes the magnitude and direction of the relationship between two variables in the form of ranks or dichotomies. An example would be the relationship of overall satisfaction ranking and index ranking, with Spearman rank r = 0 – .49 being low, .50 – .74 moderate, .75 – .79 good enough, .80 to 1.0 most desirable.

**Statistical Significance – **A finding is described as statistically significant, when it can be demonstrated that the probability of obtaining such a difference by chance only, is relatively low. In statistics, a result is significant if it is unlikely to have occurred by chance. Statistical significance is a calculation that takes into effect the sample size and variance and the variance in the data (mean or percentage).

**Variance** – A measure of variability or dispersion.

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