Chapter 12

Evaluating Relationships for Non-Numerical Scores

The categories can be numerically coded as 0, 1. i.e. male =0.

Linear relationship

The data points in the scatter plot tend to cluster around a straight line.

Correlational Strengths

describes relationships between variables, nonintrusive, high external validity

Scatter plot

each individual is represented by a single point with a horizontal coordinate determined by the individuals x score and the vertical coordinate corresponding to the y value. o A scatter plot allows you to see the characteristics of the relationship between the two variables

The statistical significance of a relationship

is the second important factor for interpreting the strength of a correlation.

Correlation coefficient

measure and describe the relationship between two variables

Pearson Correlation

measures linear relationships. Most common.

Correlational, Experimental, and Differential Research

o An experimental study involves measuring only one variable and looking for differences between two or more groups of scores. o A correlational study is intended to demonstrate the existence of a relations between two variables. Not trying to explain the relationship. o Differential design establishes the existence of a relationship by demonstrating a difference between groups

Predictor Variable

one variable

Criterion Variable

second variable the sound variable (being explained or predicted)

Correlational research strategy

two or more variables are measured and recorded to obtain a set of scores (usually 2 scores) for each individual. The measurements are then reviewed to identify any patterns of relationship that exist between the variables and to measure the strength of the relationship.

Spearman correlation

used to measure monotonic relationships

The consistency or strength of the relationship:

+/- 8 indicates a nearly perfect linear relationship in which the data points cluster close around a straight line.

The directionality Problem

A correlational study does not determine which variable is the cause and which the effect is.

Correlation, or correlation coefficient

A numerical value hat measures and describes the relationship between two variable. The sign of the correlation (+/-) indicates the direction of the relationship. The numerical value of the correlation (0.0 to 1.0) indicates the strength or consistency of the relationship. The type of correlation (Pearson or Spearman) indicates the form of the relationship.

phi-coefficient

If both are non-numerical, the resulting value is called a

point-biserial correlation

If one of the two variables is non-numerical it is called

Coefficient of Determination

Most common technique for interpreting the strength of the relationship between two variables. It is the square of the numerical value of the correlation and measures the percentage of variability in one variable that is determined or predicted by its relationship with the other variable.

Regression

The statistical process for using one variable to predict another

Negative Relationship

There is a tendency for two variables to change in opposite directions; increases in one variable tend to be accompanied by decreases in the other.

Positive Relationship

There is a tendency for two variables to change in the same direction; as one variable increases, the other also tends to increase.

Multiple regression

a stistical procedure that measures multivariate relation

Correlational Weaknesses

can't assess causality, third variable problem, directionality problem, low internal validity.

Monotopic Relationship

consistently one-directional, either consisitently positive or consistently negative