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KEY TERMS: ENTERING THE CONVERSATION WHAT YOU NEED TO KNOW Who Needs Statistics? Researchers, consumers of research, decision makers (e.g., managers, administrators, and planners), and citizens all need to know statistical reasoning and terminology Concept of Variable and Related Terms Independent and dependent variables, predictors and outcomes, conceptual and operational variables—validity and reliability of operational measures Level of Measurement Nominal and ordinal (categoric) level of measurement, interval-ratio level of measurement, continuous and categoric variables, binary/dichotomous variables Units of Analysis Cases, individuals, or places/organizations (e.g., countries, companies, cities) Univariate (descriptions of a distribution of one variable) Descriptive stats and their formulas: measures of central tendency (mean, median, mode) and measures of variability/dispersion (e.g., standard deviation, variance, range, interquartile range) Frequency distributions and percentages (relative frequencies) Graphs: pie chart, bar chart, histogram, boxplot, and when to use which Sorting cases by the values of one variable Z-scores and standard deviation units and the Z-score formula Statistical Inference: Hypothesis Testing Sampling and sampling error—the variability of the sampling distribution of a mean or proportion Sampling distribution of the mean or of a proportion The normal curve and its characteristics Standard error of the mean and its formula, standard error of a proportion and its formula, and the fact that the standard deviation of the sampling distribution (of the mean or proportion) is smaller than the standard deviation of the empirical variable The null hypothesis: “reject” or “fail to reject” the null hypothesis and what that means Type I error (alpha error) and Type II error (beta error) Test statistics (Z, t, chi-square, F), when to use which, and understanding how to use the formulas Critical region and critical value—reading the tables for Z, t, F, and chi-square 1 p-values and how to read them: the .05 cut-off point and the meaning of “significant,” “highly significant,” and “not significant” Statistical Inference Based on Constructing Confidence Intervals Confidence intervals, confidence level, upper confidence limit and lower confidence limit, and how to compute the UCL and LCL Variable Relationships: How to Select Data Analysis Techniques and INTERPRET the Results Crosstabs percentaging tables the chi-square test of significance measures of association for the strength of the relationship adding a third variable (“layer”) Compare-Means Procedures One-sample t-test (select a specific test value for the null hypothesis) Independent-samples t-test (two groups, not matched or paired, which might have different sample sizes and variances) ANOVA: F-test, ratio of “between-groups” to “within-groups” mean sum of squares, and post-hoc measures Linear Regression Bivariate: scatterplot, correlation coefficient (r)—its strength and sign, coefficient of determination (r-squared), slope coefficients, standardized coefficient (beta), and unstandardized coefficient Multiple linear regression: several independent/predictor variables, adjusted r-squared, and examining the correlation matrix to select variables to enter into the model Logistic Regression Binary outcome variable, reading the results—odds ratio and coefficients (negative coefficients for a predictor variable go with reduced odds for the outcome) Using SPSS/PASW Creating a data file and obtaining output Creating and Using Quantitative Information Moving from a research question about the empirical/real world to numbers and back again to an interpretation applied to the “real world” Generally feeling comfortable looking at quantitative information 2