Download Key Terms - The Joy of Stats

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
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
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
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