chapter 16 Flashcards
How can one differentiate between positive and negative correlations in research?
A positive correlation indicates that as one variable increases, the other also increases, while a negative correlation indicates that as one variable increases, the other decreases.
Describe the relationship between correlation and causation in social sciences.
Correlation does not imply causation; just because two variables are correlated does not mean that one causes the other.
How can a third variable influence the correlation between two other variables?
A third variable can act as a causal factor that influences both correlated variables, making it difficult to determine the direct cause-and-effect relationship between them.
How are correlational techniques utilized in educational settings?
Correlational techniques are used to describe the relationship between two variables and to make inferences about the correlation in a population based on a sample, which helps colleges screen applicants for their programs.
Describe the purpose of a scattergram in bivariate frequency distributions.
A scattergram, or scatterplot, is used to graph bivariate frequency distributions, allowing us to visually assess whether two variables are related or correlated.
How are the values represented in a scattergram?
In a scattergram, the values of the first variable are plotted on the abscissa (horizontal axis) and the values of the second variable are plotted on the ordinate (vertical axis). The points representing the data are not connected.
Describe how a scattergram can be used to analyze the relationship between communication levels and marital satisfaction.
A scattergram plots data points representing couples’ ratings of communication levels against their ratings of marital satisfaction. By examining the pattern of points, one can identify potential relationships, such as whether couples with better communication tend to report higher marital satisfaction.
Describe the range of correlation coefficients and their meanings.
Correlation coefficients range from +1 to -1. A coefficient of +1 indicates a perfect positive correlation, meaning the values of one variable are exactly related to the values of another variable. A coefficient of -1 indicates a perfect negative correlation, while a coefficient of 0 indicates no correlation between the two variables.
How is Pearson’s product-moment coefficient of correlation used?
Pearson’s product-moment coefficient of correlation is used to compute the correlation between two continuous variables, helping to quantify the strength and direction of their relationship.
Describe Pearson’s Product-Moment Coefficient of Correlation.
Pearson’s product-moment correlation coefficient is a statistical measure used to determine the extent of the relationship between two variables, represented by the correlation coefficient (r), which indicates how closely the points in a bivariate frequency distribution fit the straight line of best fit.
How is the strength of correlation indicated in Pearson’s coefficient?
The strength of correlation in Pearson’s coefficient is indicated by the value of r, which ranges from +1 to -1. A value of +1 or -1 indicates a perfect correlation, while a value approaching zero indicates no correlation.
Describe the formula for Pearson’s coefficient in deviation-score form.
The formula for Pearson’s coefficient in deviation-score form is ρ = (Σ(X - μ_X)(Y - μ_Y)) / √(SS_X * SS_Y), where the numerator is the sum of the cross products of the deviation scores for each variable, SS_X is the sum of the squared deviations in X, and SS_Y is the sum of the squared deviations in Y.
How can the relationship between aggressiveness and television habits be visually represented?
The relationship between aggressiveness and television habits can be visually represented using a scattergram, which plots the data points of aggressiveness against the hours of watching violent TV.
Describe the formula for Pearson’s correlation when data is in z-score form.
The formula for Pearson’s correlation in z-score form is ρ = (ΣzXzY) / N, where zX and zY are the z-scores of variables X and Y, respectively, and N is the number of pairs.
How can deviation scores be used to calculate correlation?
Deviation scores can be used to calculate correlation by applying the formula ρ = - (Σ(X - μX)(Y - μY)) / (SSX * SSY), where μX and μY are the means of X and Y, and SSX and SSY are the sums of squares for X and Y.
