# Analytical Techniques on Statistic for Language Testing

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Analytical Techniques
Analytical techniques alter researchers to look at complicated relationships between variables. There area unit 3 basic varieties of analytical techniques:

1. Multivariate Analysis
2. Grouping strategies
3. Multiple Equation Models
Regression Analysis

Regression analysis assumes that the dependent, or outcome, variable is directly tormented by one or additional freelance variables. There area unit four necessary varieties of regression analyses:
Ordinary statistical procedure (OLS) regression
1. Accustomed verify the connection between a variable quantity and one or additional freelance variables.
2. Used once the variable quantity is continuous. for instance, if the variable quantity was family kid care expenses, measured in greenbacks, OLS regression would be used
Logistic regression
Used once the variable quantity is divided, or has solely 2 potential outcomes. for instance, supply regression would be accustomed examine whether or not a family uses kid care subsidies.

Hierarchical linear modeling
• Used once information area unit nested. Nested information occur once many people belong to a similar cluster below study. for instance, in kid care analysis, several kids area unit cared for by a similar kid care supplier and plenty of kid care suppliers work at intervals a similar state. {the kidren|the youngsters|the kids} area unit nested within the kid care supplier and also the child care supplier is nested within the state
• Permits researchers to see the results of characteristics for every level of nested information, kid care suppliers and states, on the end result variables
Duration models
Used to estimate the length of a standing or method. for instance, in kid care policy analysis, period models are accustomed estimate the length of your time that families receive kid care subsidies.

Grouping Strategies
Grouping strategies area unit techniques for classifying observations into substantive classes. One grouping methodology, discriminant analysis, identifies characteristics that distinguish between teams. for instance, a investigator might use discriminant analysis to see that characteristics establish families that ask for kid care subsidies and that establish families that don't.

The second grouping methodology, cluster analysis, is employed to classify similar people along. for instance, cluster analysis would be accustomed cluster along families WHO hold similar views of kid care.

Interagency Meeting on Subgroup Analysis
This meeting focused on innovative strategies for conducting subgroup analysis and discussions of tips for interpretation and coverage of subgroup analyses in bar and intervention analysis.

Multiple Equation ModelsMultiple equation modeling, that is Associate in Nursing extension of regression, is employed to look at the causative pathways from independent variables to the variable quantity. There area unit 2 main varieties of multiple equation models:
1. Path analysis
2. Structural equation modeling
Path analysis
• Allows researchers to look at multiple direct and indirect causes of a dependent, or outcome, variable.
• A path diagram is made that identifies the routes between the freelance and dependent variables
• The methods will run directly from Associate in Nursing variable quantity to a variable quantity, or they will run indirectly from Associate in Nursing variable quantity, through Associate in Nursing inter mediator variable, to the variable quantity
• The whole model is tested to see the relative importance of every causative pathway
Structural equation modeling
Expands path analysis by permitting multiple indicators of unobserved (or latent) variables within the model.