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Chapter 15: Quantitative Analysis: Inferential Statistics - Coggle Diagram
Chapter 15: Quantitative Analysis: Inferential Statistics
Basic Concepts
P-value: the probability in inferential statistics.
Significance level: the maximum level of risk that we are willing to accept as the price of our inference from the sample to the population.
Sample distribution: the theoretical distribution of an infinite number of samples from the population of interest in your study.
Standard error: every sample has some inherent level or error.
Confidence interval: the precision of our sample estimates is defined by terms.
General Linear Model: most inferential statistical procedures in social science research are derived from a general family of statistical models.
Regression analysis: the process of estimating regression coefficients.
Two-Group Comparison
ANOVA: analytic technique for this simple design is a one-way. It is one-way because it involves only one predictor variable.
Student's t-test: the statistical test. Examines whether the means of two groups are statistically different from each other.
Null hypothesis: always the one with the "equal" sign, and the goal of all statistical significance tests is to reject the null hypothesis.
Other Quantitative Analysis
Factor analysis: a data reduction technique that is used to statistically aggregate a large number of observed measures into a smaller set of unobserved variables called factors based on their underlying bivariate correlation patterns.
Discriminant analysis: a classificatory technique that aims to place a given observation in one of several nominal categories based on a linear combination of predictor variables.
Logistic regression: a GLM in which the outcome variable is binary and is presumed to follow a logistic distribution and the goal of the regression analysis is to predict the probability of the successful outcome by fitting data into a logistic curve.
Probit regression: a GLM in which the outcome variable can very between 0 and 1 and is presumed to follow a standard normal distribution, and the goal of the regression is to predict the probability of each outcome.
Path analysis: a multivariate GLM technique for analyzing directional relationships among a set of variables.
Time series analysis: a technique for analyzing time series data, or variables that continually changes with time.