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Statistic & Research Design II

by: Miss Mellie O'Conner

Statistic & Research Design II PSY 862

Marketplace > Eastern Kentucky University > Psychlogy > PSY 862 > Statistic Research Design II
Miss Mellie O'Conner
GPA 3.97

Jonathan Gore

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Jonathan Gore
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This 7 page Class Notes was uploaded by Miss Mellie O'Conner on Sunday October 11, 2015. The Class Notes belongs to PSY 862 at Eastern Kentucky University taught by Jonathan Gore in Fall. Since its upload, it has received 22 views. For similar materials see /class/221431/psy-862-eastern-kentucky-university in Psychlogy at Eastern Kentucky University.

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Date Created: 10/11/15
192009 Structural Equation Modeling Definition A statistical technique for testing and estimating causal relationships using a combination of statistical data and qualitative causal assumptions SEM vs I Very similar procedure EXCEPT I Structural equation modeling assesses a hypothesized model All proposed paths tested simultaneoust reduces Type I error DAssesses how well the model fits the data you have Important Notes I Your model should follow a theoretical basis SEM is rarely exploratory I You are usually going to leave some paths out based on theory LDiscarded paths are assumed to be non significant iFully recursive model all possible paths are included 192009 of Anal sis I Similar to mediation analysis EXCEPT LYOU will have a different dependent variable in each analysis 1 will not use Blocks I Preliminary analysis in SPSS to checkthe significance of paths I Followup analysis in AMOS or LISREL to checkthe fit ofthe model and magnitude of indirect effects Drs Palmer or Gore can help Steps in SEM Estimation Draw out your model Specify your paths what you expect will be related Determine how many of the following you have 1 Predictor variables far le Outcome variables far right Intermediate variables in between 39 Note You may have sever l intermediate variables h a in the sequence eg 2 3 4t then the outcome 192009 I Steps in SEAl Estimation I Conduct a linear regression analysis with rAll predictors as the independent variables rAll intermediate variables as the dependents You will have as many tests as you have intermediate variables i Note the standardized 3 s and RZ s for each aha ys39s L Write these numbers on the model isNote which additional paths may be significant Steps in SEAl Estimation I Conduct a linear regression analysis with All predictors and rst group ofintermediate variables as the independent variable The second group ofintermediate variables as the dependents Again ou will have as manytests as you have intermediate variables J Note the standardized 3 s and Rz s for each analysis Write these numbers on the model Repeat this step ifyou have several groups of intermediate variables Steps in SEAl Estimation I Conduct a linear regression analysis with rAll predictors and all intermediate variables as the independent variables rThe outcome variables as the dependents uYou will have as many tests as you have outcome variables i Note the standardized 3 s and RZ s for each analysis rEWrite these numbers on the model 192009 I Keep in mind which paths you didn t specify in the model were significant I Rememberthat you need to report how well the proposed model fits the data Fit indices examine the covariance matrix of your data set and determines How many paths you estimated a How much all variables are related to each other U A model that ts the best is one that uses the least amount of paths and all paths are signi cant and CutOffs I Cutoff criteria proposed by Hu and Bentler I Chisquare must report Cutoff nonsigni cant p gt 10 1 Problem large sample sizes lower pvalues I Goodness of Fit lndex GFI Cutoff GFI gt 90 I Root Mean Square Error of Approximation RMSEA Cutoff RMSEA lt 06 I Comparative Fit lndex CFI Cutoff CFI gt 95 I LISREL gives you all of these indices What Happens If You Ge r I Consider a simpler model I Consider adding paths I LISREL provides Modification lndices which flag the paths w may want to add Addi rional Versions of SEM l Longitudinal Model Estimation we ll discuss next time ii Tests tne tit ota rnooei tnat proposes grouping otiterns onto tactors Combination ot CFA and SEM i Oniytme variance used in tne rnooei Testing tne rnooei between two orrnore groups Look tor moderated oatns in tne rnooei Examines cnange overtime as mean quadratic etc ii More accurate version ofTrend Anaiysis 192009 192009 Moderation Effects and Analysis Definition An effect that identifies a qualitative or quantitative variable that affects the direction andor strength of the relation between two variables Answers quotWhoquot and quotWhenquot I Begin with two variables and their association 1 Ex Incentives and motivation are positively correlated I Although the association is well established we might not know FOR WHOM orWHEN this is more likely to be the case ie an it depends question I Moderation answers this question


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