Bài giảng Chapter 12 Testing for Relationships

Chapter 12Testing for RelationshipsTests of linear relationshipsCorrelation2 continuous level variablesRegression2 or more continuous level variablesIdentifies statistically significant linear patterns in the association of variables1Basic AssumptionsData collected from sample to draw conclusion about populationData from normally distributed populationAppropriate variables are selected to be tested using theoretical modelsParticipants randomly selected2Alternative and Null HypothesesInferential statistics test the likelihood that the alternative hypothesis is true and the null hypothesis is notSignificance level of .05 is generally the criterion for this decisionIf p  .05, then alternative hypothesis acceptedIf p > .05, then null hypothesis is retained3Four Analytical StepsStatistical test determines if a relationship existsExamine results to determine if the relationship found is the one predictedIs the relationship significant?Evaluate the process and procedures of collecting data4CorrelationAlso known as Pearson product-moment correlation coefficientRepresented by rCorrelation reveals one of the following:Scores on both variables increase or decreaseScores on one variable increase while scores on the other variable decreaseThere is no pattern or relationship 5Correlation Correlation coefficient or r reveals the degree to which two continuous level variables are relatedParticipants provide measures of two variablesIf r is  .05, then the relationship is significant –hypothesis or research question acceptedCorrelation cannot necessarily determine causationX causes YY causes XThird variable causes both6Interpreting the CoefficientDirectionof relationshipPositive- both variables increase or both variables decreaseNegative – one variable increases while the other decreasesRelationship strength .90 – very high or dependable7Scale of Correlation8Amount of Shared Variancer2 – represents the percentage of variance two variables have in commonKnown as coefficient of determinationFound by squaring r.2 or -.2 = .04 r2.3 or -.3 = .09 r2.4 or -.4 = .16 r2.5 or -.5 = .25 r2.6 or -.6 = .36 r2.7 or -.7 = .49 r2.8 or -.8 = .64 r2.9 or -.9 = .81 r29Example of Correlation Matrix10Other Forms of CorrelationPoint biserial correlationOne continuous level variable, other variable is a dichotomous measure Spearman correlation coefficient or rhoBoth variables are ordinal scale dataBoth interpreted for their direction and strength11Limitations of CorrelationCan only examine relationship between 2 variablesAny relationship is presumed to be linearLimited in the degree to which inferences can be madeCorrelation does not necessarily equal causationCausation depends on the logic of relationship12RegressionPredicts some variables by knowing othersAssesses influence of several continuous level predictor, or IVs, on a single continuous criterion, or DVUsed to examine causation without experimentation“Variance accounted for” – describes the % of variance in the criterion variable accounted for by the predictor variable13Linear RegressionRegression line – line drawn through the data points that best summarizes the relationship between the IV and DV The better the fit of the line, the higher RAdjusted R2 - the proportion of variance explained or accounted for on the DV by the IV14Beta WeightsAlso known as beta coefficientsRepresented by βAllows comparison among variables of different measuring unitsRange from +1.00 to –1.0015Multiple RegressionTests for significant relationship between the DV and multiple IVsIndependentlyAs a groupCommon in communication researchUse beta weights to interpret the relative contribution of each IV16Other Forms of RegressionHierarchical regressionResearcher enters IVs in the order in which they are theoretically presumed to influence the DVStepwise regressionOrder of variables is determined by statistical program based on the degree of influence each IV has on the DV17Cautions in Using StatisticsUse and interpretation of statistical tests is subjectiveMany variations of each testResearcher must interpret statistical resultAre the results worth interpreting statistically?Was appropriate statistical test selected and used?Are the results statistically significant?Are the results socially significant?18