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Model Samples and Research MethodsThere are an Infinite number of possibilities mathematically BUT NOT NECESSARILY theoretically!!! Problem to Test Statistic Chain Relationships{5C22544A-7EE6-4342-B048-85BDC9FD1C3A} DefinitionThe research questionThe hypothesisThe methodologyThe statistical testThe test statisticsThe problemIdentifies an area that needs to be corrected or addressedIs a rephrasing of the problem into a questionIs a relationship within or between variables that if demonstrated addresses the problemThe methodology is the set of procedures that gathers data within the environment that the problem manifests itself  The research questionA statement, that if answered addresses the problem Is a relationship that if demonstrated, answers the questionThe methodology is a set of procedures that gathers data within the environment where the research questions are be answered  The hypothesisA relationship within or between constructs/variables that if demonstrated in a statistically acceptable manor, answers the question  Is a means to gather data in a manor that allows addressing the hypothesis and accounts for the environment and sample (population) under investigation  The methodologyA procedures used to gather appropriate data to ensure that the statistical test will actually address the hypothesis   The statistical test is only relevant if appropriate data is gathered using a specifies methodology for the environment and for the sample in which the phenomena is present The statistical testA procedure using tools/software that is run using appropriate data gathered using a methodology to address the hypothesis     The test statistic is unique to each statistical procedure Problem through Test Statistics and Control Chain Examples{5C22544A-7EE6-4342-B048-85BDC9FD1C3A}ProblemThe research questionThe hypothesisThe methodologyThe statistical testThe test statisticsControls i.e. factors that are NOT theorized to affect the results Sample SizeThere are positive or negative differences between 2 or more groups in an environment (team, company etc..). The differences are a problem.Are there differences between 2 or more groups in an environment (team, company, etc..)The two groups that demonstrate XXX in an environment are different.Gather a enough data (sample size) related to the problem in the environment for each group depending on the number of variables (indicators) and the number of controls. The means of each group(s) are different (ANOVA or MANOVA) regardless of the controls i.e. the controls do not matterDifference in means with a p-value(s). The control groups do not show a difference than the main groupsAge, education, years of experience, position in organization, gender, ethnicity to ensure that these ARE NOT the problemObtain samples large enough for the statistical tests AND to break up into groups per the controlsSomething(s) is(are) affecting something else in positive or negative way within an environment. The Effect(s) is (are) the problem.Does XXX (or Do XXX, WWW, and NNN) affect YYYY in an environment (team, company, etc..)XXX(and WWW NNNn) affect(s) YYYY in a(n) environment (team, company, etc..)Gather enough data (sample size) related to XX (and WWW, NNN) and gather data related to YYY in the environment depending on the number of variables (indicators) and the number of controls.XXX (and WWW, NNN) predicts YYYyy (Regression singular or multiple or SEM) regardless of the controls i.e. the controls do not matterbeta weight(s) or path coefficients and statistically significant p-value(s) The control groups do not show a difference.Age, education, years of experience, position in organization, gender, ethnicity to ensure that these ARE NOT the problemObtain samples large enough for the statistical tests AND to break up into groups per the controlsSomething(s) is(are) interacting with other things that are then affecting something else in positive or negative way within an environment. Effect(s) is (are) the problem.Does XXX (or Do XXX, WWW, and NNN) interact with MMM, EEE to affect YYYY in an environment (team, company, etc.)XXX(and WWW NNNn) affect(s) interacts with MMM or EEE YYYY in a(n) environment (team, company, etc.)Gather data related to XX (and WWW, NNN), MMM, EEE, and gather data related to YYY (y1, y2 etc.) in the environment depending on the number of variables (indicators) and the number of controls.XXX (and WWW, NNN) predicts via moderation or mediation with YYY (Regression singular or multiple with moderation or mediation or SEM) regardless of the controls i.e. the controls do not matterbeta weight(s) or path coefficients and p-value(s) of the moderation or mediation are GREATER and statistically significant than without the moderation or mediation. The controls are NOT significantAge, education, years of experience, position in organization, gender, ethnicity to ensure that these ARE NOT the problemObtain samples large enough for the statistical tests AND to break up into groups per the controls Group differences for the same phenomena Tested using ANOVA i.e. difference in means and p-valueEnvironment/PopulationGroup 1Group 2Phenomena XXX(aka variable)=Group n=IndicatorsIndicatorsIndicatorsExampleResearch Question : In an organization, do different age groups (or education level groups) demonstrate different levels of commitment (the phenomena)?H: Different age groups (or education level groups) demonstrate different levels of commitment (the phenomena). Phenomena XXX RELATED to Phenomena YYYTested via Correlation and p-valueExampleResearch Question : In an organization, is the time in the organization (a phenomena) related to the level of commitment (a phenomena)?H1: Time in the organization (a phenomena) is related to the level of commitment (a phenomena)Environment/PopulationPhenomenaXXXPhenomenaYYYPierson's correlation and p-ValueIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsH1 Phenomena XXX affects (or predicts) Phenomena YYYcan be done with Regression or SEMExampleResearch Question : In an organization, does level of commitment affect turnover intention?H1: In an organization, level of commitment negatively affects turnover intentionEnvironment/PopulationPhenomenaXXX(Independent Variable – IV)PhenomenaYYY(DependentVariable - DV)Beta WeightOr and p-ValuePath CoefficientIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsH1- Phenomena's XXX and WWW affect (or predict) Phenomena YYYCan be Accomplished with Regression or SEMEnvironment/PopulationBeta WeightOr and p-ValuePath CoefficientIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsBeta WeightOr and p-ValuePath CoefficientExampleResearch Question : In an organization, do level of commitment and servant leadership affect turnover intention?H1: Level of commitment negatively affects turnover intentionH2: Servant leadership negatively affects turnover intentionH1-H2-PhenomenaXXX(Independent Variable – IV)PhenomenaWWW(Independent Variable – IV)PhenomenaYYY(DependentVariable -DV) Phenomena's XXX and WWW affects or predicts Phenomena YYY1 and YYY2Can be Accomplished with Regression or SEMEnvironment/PopulationBeta WeightOr and p-ValuePath CoefficientIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsBeta WeightOr and p-ValuePath CoefficientIndicatorsIndicatorsIndicatorsExampleResearch Question: In an organization, do level of commitment and servant leadership predict turnover intention and productivity?H1: Level of commitment negatively affects turnover intentionH2: Level of commitment positively affects productivityH3: Servant leadership negatively affects turnover intentionH4: Servant leadership positively affects productivityH1-H2+H4+H3-PhenomenaXXX(Independent Variable – IV)PhenomenaWWW(Independent Variable – IV)PhenomenaYYY1(DependentVariable -DV)PhenomenaYYY2(DependentVariable -DV) Phenomena's XXX affects on Phenomena YYY is fully or partially Mediated by Phenomena EEECan be Accomplished with Regression or SEMEnvironment/PopulationBeta WeightsOr and p-ValuesPath CoefficientsIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsPhenomenaEEE(Mediator)IndicatorsIndicatorsIndicatorsExampleResearch Question: In an organization, does Level of Commitment (ME), mediate the relationship between Servant Leadership (IV) and Productivity (DV)?H1: Servant Leadership positively affects Level of CommitmentH2: Level of Commitment positively affects to productivityH3: Servant Leadership positively affects to ProductivityFull Mediation: The H3 relationship is zero in presence of CommitmentPartial Mediation: H2 is stronger than H3H1+H2+H3+PhenomenaXXX(Independent Variable – IV)PhenomenaYYY(DependentVariable - DV) Phenomena's XXX affects on Phenomena YYY is moderated by Phenomena MMMCan be Accomplished with Regression or SEMEnvironment/PopulationBeta WeightOr and p-ValuePath CoefficientIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsIndicatorsPhenomenaMMM(Moderator)IndicatorsIndicatorsIndicatorsExampleQ: In an organization, does amount of training, moderate the relationship between servant leadership (IV) predict productivity (DV)H1: Servant Leadership positively affects ProductivityH2: Level of Training increases relationship between Servant Leadership and ProductivityH1+H2+PhenomenaXXX(Independent Variable – IV)PhenomenaYYY(DependentVariable - DV) More Complicated ModelsAre better suited for SEMIndicators left out for simplicityEnvironment/PopulationPhenomenaXXXPhenomenaYYYPhenomenaEEE1PhenomenaEEE2PhenomenaWWWPhenomenaMMMIndependentVariable (IV)DependentVariable (DV)ModeratingVariable (MV)IndependentVariable (IV)MediatorVariable (ME)MediatorVariable (ME) Psychometric Variables vs. Control or Directly Measurable VariablesPsychometric VariableMeasure an individuals “perception” of a ConstructLeadershipPerceived UsefulnessMust be assessed for Reliability and Validity because it is a perception!Contain measurement error!!Control Variable/Directly Measured VariablesAre not approximations (i.e. contain no measurement error – they are absolute – always or at specific point in timeGenderAgeEducationYears in WorkforceSales for the yearARE NOT assessed for reliability or validity because they are irrefutable!contain no measurement error – they are absolute – always or at specific point in time Not understanding the stages (steps) required for data analysis and why each is accomplishedData cleansing first – remove bad data or add missing dataData validity next – ensure we are correctly measuring the variables in our modelVariable relationships last – do the hypothesis hold valid?Not understanding which tools can be for specific stepsFrequent Student Challenges Why Do We…..Clean the data firstTo remove data that could produce bad indicatorsi.e. some subjects did not answer the questions in a manor consistent with the way other subject didAdd missing data i.e. only one or two indicators from a responderSo that we can make use of a otherwise good dataAssess the indicators and variables nextBecause if we are not measuring what we think we are measuring, our hypothesis testing will not be justified!Assess the variable relationships lastThis is the goal!!!!! Terms that refer to the same ideas or are relatedScale = measures = set of Items = questions related to a variable = questions that measure a constructThis leads to terms and related quality assessment mechanisms such as 1) measurement instrument, or 2) item reliability, 3) scale reliability, or) 4) internal consistency reliabilityConstruct = factor = variableThe term “Construct” is used in theoretical model – it is a theoretical phenomena of interest that cannot be readily measuredThe term “Factor” is used when looking all phenomena that are present in an environmentThe term “Variable” refers to the operationalization version of a construct which is measured using indicatorsNote by equal ( = ), I mean “related” in some cases http://www.slideshare.net/DrAkterCMC/reliability-validity-generalizability-and-the-use-of-multiitem-scales http://www.slideshare.net/DrAkterCMC/reliability-validity-generalizability-and-the-use-of-multiitem-scales The Operationalized Model – All Reflective IndicatorsOBSEORG_CARE FAIRNESSAUTHORITYREPUTATIONOrgCare 1OrgCare 2OrgCare 3OrgCare 4OrgCare 5Author 1Author 2Author 3Author 4Author 5Fair 1Fair 2Fair 3Fair 4Fair 5OBSE 1OBSE 2OBSE 3OBSE 4OBSE 5REP 1REP 2REP 3REP 4REP 5 The Measurement Instrument/Measurement Model(What is Indicator Reliability and Internal Consistency Reliability – What are the Differences?)OBSEORG_CARE FAIRNESSAUTHORITYOrgCare 1OrgCare 2OrgCare 3OrgCare 4OrgCare 5Author 1Author 2Author 3Author 4Author 5Fair 1Fair 2Fair 3Fair 4Fair 5OBSE 1OBSE 2OBSE 3OBSE 4OBSE 5REP 1REP 2REP 3REP 4REP 5Indicator Reliability -How well a single indicator measures the variableInternal Consistency ReliabilityA total measure of how well ALL the indictors support measuring the construct/variableREPUTATION The Measurement Instrument/Measurement Model(What is Discriminative Validity?)Discriminative ValidityIndicators of respective variables correlate most (i.e. associate themselves most) with other indicators from that variablecorrelations strongestand significantall other individual correlations lower and may be significant or notX The Measurement Instrument/Measurement Model(What is Convergent Validity?)Factor 4Factor 1Factor 2Factor 5OrgCare 1OrgCare 2OrgCare 3OrgCare 4OrgCare 5Author 1Author 2Author 3Author 4Author 5Fair 1Fair 2Fair 3Fair 4Fair 5OBSE 1OBSE 2OBSE 3OBSE 4OBSE 5REP 1REP 2REP 3REP 4REP 5Factor 3Convergent ValidityAssociated Indicators converge (i.e. align themselves most) on an unnamed factor. X The Regression, Path, or Structural ModelCoefficients, p-values, R2OBSER2ORG_CARE FAIRNESSR2AUTHORITYR2REPUTATIONOrgCare 1OrgCare 2OrgCare 3OrgCare 4OrgCare 5Author 1Author 2Author 3Author 4Author 5Fair 1Fair 2Fair 3Fair 4Fair 5OBSE 1OBSE 2OBSE 3OBSE 4OBSE 5REP 1REP 2REP 3REP 4REP 5Regression or Beta Weight orPath Coefficient and p-value R2How much the variation in a dependent or mediating variable is explained by the related independent variables.Regression/Beta Weigh/Path CoefficientHow much change does one variable create on anotherand p-value – ex. 0.05Probability that the result IS by chance 1 minus p-value - ex 0.95Probability that result holds true Removal of Outliers – i.e. removal of specific responses not behaving similar to restGoogleData Analysis OutliersWhat is an OutlierTwo Variable ExampleCorrelation Plot ToolOutlier 4 Indicators Example - Box Plot Tool – Sample Size 442Data Cleansing – Outlier Removal by Variablei.e. Assess Indicators Associated with One VariableOutliers(Response #’)Indicator # 1Indicator # 2Any VariableIndicator 1Indicator 2Indicator 3Indicator 4Indicator # 1Indicator # 2 What kinds of issues have you seen people experiencing?{5C22544A-7EE6-4342-B048-85BDC9FD1C3A}Area to AssessSimple DefinitionSPSSSmart PLSReliabilityIndicator ReliabilityDoes the indicator consistently measure the variable?Indicator Loading on VariableGreater than 0.7 and p value > 0.05Indicator Loading on the VariableGreater than 0.7 and p value > 0.05Internal Consistency ReliabilityAre the items measuring the same thing?Cronbach Alpha > 0.7Cronbach Alpha, Composite Reliability, or Average Variance Extracted (AVE)All greater than 0.7ValidityConvergent ValidityDoes a set of indicators represents the same underlying constructFactor Analysis - Correlations between associated indicators are greater than with others.Average Variance Extracted (AVE) is greater than 0.5Discriminant ValidityDo indicators for different constructs only align to their own constructs.Factor Analysis – Related Indicators load highest on the same factorAVE of each variable should be greater than squared correlations or Indicators should not have higher correlations with other variables.Measurement Model Validity - Are we Actually Measuring the Variables? Reflective Indicators Variables ONLY Measurement Model Validity - Formative Variables ONLYCan ONLY be done with SEM Tools - Will NOT work with SPSS What kinds of issues have you seen people experiencing?{5C22544A-7EE6-4342-B048-85BDC9FD1C3A}Area to AssessSimple DefinitionSPSSSmart PLSExplained Variance(Explanatory Power)(Measured at the affected Variable)How much the variation in a dependent is explained by the related independent variables.R2Values of 0.67, 0.33, and 0.19 as large, moderate, and low levels R2Values of 0.67, 0.33, and 0.19 as large, moderate, and low levels The following are measured on the path between 2 variablesEffect SizeWhat is the strength of the relationship between 2 variablesMust be Calculated ManuallyEffect Size = f2= R2/(1-R2)Only simple to do for single (one to one) dependent to Independent Variable relationshipsValues of 0.35, 0.15, and 0.02 represent large, medium, and small effectsEffect Size = f2Values of 0.35, 0.15, and 0.02 represent large, medium, and small effectsPredictive Behavior Path Coefficient or Beta WeightHow much change does one variable create on anotherBeta Weight with p valuePredictive validity variable, we would hope for a beta coefficient coefficient of at least 0.2, p-value less than 0.1, 0.05, 0.01Path Coefficient with p-valuePredictive validity variable, we would hope for a path coefficient of at least 0.2, p-value less than 0.1, 0.05, 0.01Predictive Behavior Q2Is the change in the affect variable actually due to changes affecting variableNot calculatedPredictive Relevance Q2Should be > 0 and PositiveStructural Model Assessment Relationships Between Variables Make sure you understand:Which tool is used for which data analysisWhich statistic is used to report each data analysisAs you come across articles related to variables you are studying …..pay attention to: The tools used and The means to display resultsRemember – Not all hypothesis are quantitative – some are qualitative – i.e. demonstrated via rigorous presentation of an argument/defenseAsk Questions throughout this and every class.Advice Email Questions toJose.angeles@trident.edu
cas3 - model assessment using SPSS tools then using Smart PLS

cas3 - model assessment using SPSS tools then using Smart PLS

Question # 00712883 Posted By: matabi Updated on: 12/01/2018 06:44 PM Due on: 12/05/2018
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model assessment using SPSS tools then using Smart PLScas

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