Comparison Of Multilevel Model And Its Statistical Diagnostics

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Diagnostics in statistical analysis is atmost important as there may be few influential observations which may distort the inference of the problem statement at hand. Diagnostic measures are to be selected with the suitable model in validating the multi-level regression results with greater accuracy. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following – Always on Time, outstanding customer support, and High-quality Subject Matter Experts. Contact Us: Website: www.statswork.com Email: info@statswork.com United Kingdom: +44-1143520021 India: +91-4448137070 WhatsApp: +91-8754446690

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Comparison of multilevel model and its statistical diagnostics Research Planing | Data Collection | Semantic Annotation | Consumer Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved Tags: Statswork | Linear Regression Models | Multilevel model | Statistical diagnostics | Programmers | Statistical Data Analysis | Data Analysis Services Research paper NOV 4 2019

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Research Planing | Data Collection | Semantic Annotation | Consumer Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved STATISTICAL DIAGNOSTICS Diagnostics in statistical analysis is atmost important because there may be few influential observations which may distort the inference of the problem statement at hand. All influential observations are not outliers but some outliers are influential.

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Research Planing | Data Collection | Semantic Annotation | Consumer Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved MULTILEVEL DATA AND ITS DIAGNOSTICS Multi-level models are the statistical models of parameters like in usual linear regression model that vary at more than one level. Referred with many terms namely mixed-effect models random effect model hierarchical models and many more. With the advent of statistical software and computations multi-level or hierarchical models are widely used for longitudinal repeated measures analysis and in many meta data applications. Multi-level models also applicable for non-linear case too by using appropriate Generalized Linear Mixed Models.

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Research Planing | Data Collection | Semantic Annotation | Consumer Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved TABLE:1 FIXED EFFECT MODEL USING REGRESSION

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Research Planing | Data Collection | Semantic Annotation | Consumer Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved TABLE:2 RANDOM EFFECT MODEL USING REGRESSION

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Research Planing | Data Collection | Semantic Annotation | Consumer Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved TABLE:3 HIERARCHICAL MODEL Like in linear regression model the mixed model also must satisfies the assumptions of the model. If any one of the assumptions is violated then the data is taken to the diagnostics part of the model. Mostly researchers checks the data for the independence. If it gets violated then the most popular residual diagnostics is carried out to identify the influential or outlier points which deviate from other.

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Research Planing | Data Collection | Semantic Annotation | Consumer Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved TABLE:4 LINEAR REGRESSION BETWEEN ATTRACTIVENESS AND PURCHASE INTENTION TABLE:5 R R-SQUARE AND ADJUSTED R- SQUARE

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Research Planing | Data Collection | Semantic Annotation | Consumer Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved TABLE:6 RESIDUALS OF LINEAR REGRESSION Residual diagnostics in the multilevel models needs careful attention. Usually statistical analysis practitioner prefer to fit a level 1 with one independent variable regression model with and without the influential points and compare the plots of the residuals. Later to fit level 2 regression model and cross check the results. Bootstrapping technique with jacknife residuals can also be useful in diagnosing the multi-level model for greater accuracy.

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Research Planing | Data Collection | Semantic Annotation | Consumer Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved TABLE:7 MULTIPLE REGRESSION ANALYSIS TO PREDICT ONE DEPENDENT VARIABLE BASED ON MORE THAN ONE INDEPENDENT VARIABLE

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Research Planing | Data Collection | Semantic Annotation | Consumer Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved SOFTWARE PACKAGES IN R FOR DIAGNOSING MULTI-LEVEL MODEL Residplot DHARMa HLMdiag Used for linear mixed model diagnostics. Misspecification is a major problem when using usual residual statistics such as Pearson and Response in the multi-level modelling. Used for residual diagnostics of GLMMs. Overcomes the drawback of residplot package and gives a straightforward method as in linear regression models. Unusual pattern in the data are identified using the residual vs the predicted plots. Used for the diagnostics for hierarchical models. Provides deletion diagnostics with the help of distance based metrics such as Cook’s distance COVratio COVtrace and MDFFITS. Allows the user to obtain the residuals through least square estimates or bayes estimates. Also allows the user to obtain various residuals using marginal conditional distributions.

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Research Planing | Data Collection | Semantic Annotation | Consumer Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved OTHER MULTI-LEVEL MODELS 01 02 03 Diagnostic tools for random effects model with an application to growth curve model- Lindsey and Lindsey 2000. Diagnostics for multilevel models in a more concrete way- Snijders and Berkhof 2007. Case deletion diagnostics in multilevel models for identifying the influential observations in the data- Shi and Chen 2008.

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Research Planing | Data Collection | Semantic Annotation | Consumer Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved SUMMARY There have been a lot of applications emerging for multilevel regression models especially in the meta data and it became a common practice in the field of statistics to make the model more accurate. Thus more appropriate diagnostic measures are to be selected with the suitable model in validating the multi-level regression results with greater accuracy.

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Research Planing | Data Collection | Semantic Annotation | Consumer Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved Statswork Lab Statswork.com www.statswork.com

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