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Summary of Chapter 8

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