Chương 18 - ĐO LƯỜNG LIÊN HỆ

Chia sẻ: Lê Đức Hoàng Minh | Ngày: | Loại File: PPT | Số trang:15

Thêm vào BST

Báo xấu

122
lượt xem 18
download

Download Vui lòng tải xuống để xem tài liệu đầy đủ

Parametric correlation requires two continuous variables measured on an interval or ratio scale. The coefficient does not distinguish between independent and dependent variables

Chủ đề:

Bình luận(0) Đăng nhập để gửi bình luận!

Lưu

Nội dung Text: Chương 18 - ĐO LƯỜNG LIÊN HỆ

Chương 18 ĐO LƯỜNG LIÊN HỆ 18-1
Bivariate Correlation vs. Nonparametric Measures of Association • Parametric correlation requires two continuous variables measured on an interval or ratio scale • The coefficient does not distinguish between independent and dependent variables 18-2
Bivariate Correlation Analysis Pearson correlation coefficient – r symbolized the coefficient's estimate of linear association based on sampling data – Correlation coefficients reveal the magnitude and direction of relationships – Coefficient’s sign (+ or -) signifies the direction of the relationship • Assumptions of r Linearity 18-3 Bivariate normal distribution
Bivariate Correlation Analysis Scatterplots – Provide a means for visual inspection of data • the direction of a relationship • the shape of a relationship • the magnitude of a relationship (with practice) 18-4
Interpretation of Coefficients • Relationship does not imply causation • Statistical significance does not imply a relationship is practically meaningful 18-5
Interpretation of Coefficients • Suggests alternate explanations for correlation results – X causes Y. . . or – Y causes X . . . or – X & Y are activated by one or more other variables . . . or – X & Y influence each other reciprocally 18-6
Interpretation of Coefficients • Artifact Correlations • Goodness of fit – F test – Coefficient of determination – Correlation matrix • used to display coefficients for more than two variables 18-7
Bivariate Linear Regression • Used to make simple and multiple predictions • Regression coefficients – Slope – Intercept • Error term • Method of least squares 18-8
Interpreting Linear Regression • Residuals – what remains after the line is fit or (Yi-Yi) • Prediction and confidence bands 18-9
Interpreting Linear Regression • Goodness of fit – Zero slope • Y completely unrelated to X and no systematic pattern is evident • constant values of Y for every value of X • data are related, but represented by a nonlinear function 18-10
Nonparametric Measures of Association • Measures for nominal data – When there is no relationship at all, coefficient is 0 – When there is complete dependency, the coefficient displays unity or 1 18-11
Nonparametric Measures of Association • Chi-square based measure – Phi – Cramer’s V – Contingency coefficient of C • Proportional reduction in error (PRE) – Lambda – Tau 18-12
Characteristics of Ordinal Data • Concordant- subject who ranks higher on one variable also ranks higher on the other variable • Discordant- subject who ranks higher on one variable ranks lower on the other variable 18-13
Measures for Ordinal Data • No assumption of bivariate normal distribution • Most based on concordant/discordant pairs • Values range from +1.0 to -1.0 18-14
Measures for Ordinal Data • Tests – Gamma – Somer’s d – Spearman’s rho – Kendall’s tau b – Kendall’s tau c 18-15