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# WGU RFC 1 ch 8

### Summary of Chapter 8

Correlational research involves collecting data to determine whether and to what degree a relations exists between two or more variables
degree of relation is expressed as correlation coefficient
If two variables are related scores within a certain range on one variable are associated with scores within a certain range on the other variable
Relation between variables does not imply that one is the cause of the other
not infer causal relations on the basis of data from a correlational study
correlational studies may be designed either to determine whether and how a set of variables are related or to test hypotheses regarding expected relations.
Variables to be correlated should be selected on the basis of some rationale suggested by theory or experience
common, minimally accepted sample size for a correlational study 30 participants
variables correlated have low reliabilities and validities a bigger sample is necessary
Basic correlational design, scores for two (or more) variables of interest are obtained for each member of a selected sample, and the paired scores are correlated.
A correlation coefficient is a decimal number between -1.00 and +1.00. It describes both the size and direction of the relation between two variables
If correlation coefficient is near .00, the variables are not related
A correlation coefficient is near +1.00 indicates that the variables are strongly and positively related. An increase on one variable is associated with an increase on the other
If the correlation coefficient is near -1.00, the variables are strongly and negatively or inversely related. An increase on one variable is associated with a decrease on the other variable
Correlations of +1.00 and -1.00 represent the same strength but different directions of relation
A correlation coefficient much lower than .50 is generally not useful for group prediction or individual prediction.
However a combination of correlations below .50 may yield useful prediction
Coefficients in the .60s and .70s are usually considered adequate for group prediction purposes
Coefficients in the .80s and higher are adequate for individual prediction purposes
Common variance or share variance indicates the extent to which variables vary in a systematic way
the higher the common variance the higher the correlation
Statistical significance refers to the probability that the study results (e.g., correlation coefficient of this size) would have occurred simply due to chance
Small samples require larger correlation coefficients to achieve significance
the value of the correlation coefficient needed for significance increases as the level of confidence increases
A low coefficient represents a low degree of association between variables, regardless of statistical significance
relationship study conducted to gain insight into the variables or factors that are related to a complex variable, such as academic achievement, motivation, or self concept
in a relationship study, the researcher first identifies the variables to be related
Prediction study is an attempt to determine which of a number of variables are most highly related to the criterion variable
Prediction studies are often conducted to facilitate decision making about individuals or to aid in the selection of individuals.
Variable used to predict predictor
variable that is predicted is complex variable, called the criterion
Data analysis in prediction studies involve correlating each predictor variable with the criterion variable
prediction study using multiple variables results in a prediction equation referred to as a multiple regression equation, which combines all variables that individually predict the criterion to make more accurate prediction.
Created by: Xyrarose