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Multilevel Longitudinal Modelling Assignment Help At Your Fingertips!

Multilevel Longitudinal Modelling is a major area of statistics. It caters to different Logistic as well as Linear regression and their applications in the field of social research. The study of longitudinal study generally produces different measurements on each subject.

Let us understand with an example explained by our Multilevel Longitudinal Modelling assignment experts. Suppose an HIV patient is followed over a specific time and measures such as CD4 counts or any other viral load are obtained to categorise disease burden and immune status respectively.

Several Australian universities are offering Multilevel Longitudinal Modelling study and a large number of students get enrolled in this discipline to learn the role of a longitudinal study in clinical research, epidemiology, and therapeutic evaluation.

They also come to characterise the normal growth and aging, to evaluate the treatment, effectiveness or comparison of different strategies to analyse longitudinal data, mixed model analysis, multilevel modeling, and regression, etc.

On this web page, our Multilevel Longitudinal Modelling assignment expert focuses on how to construct and interpret the following models:

  • Strategies comparison to assess longitudinal data which includes repeated measures ANOVA, regression, mixed models analysis, and multilevel modelling
  • Multilevel models used to study longitudinal data
  • Models for valuation changes in slope and elevation over time
  • Analyse treatment effects over time

Our multilevel longitudinal modeling homework help assignment experts will mainly concentrate on the developing and interpretation of the above-given models.

A comparison of strategies for analyzing longitudinal data

For Example Children's alcohol use calculated at 3 time points, age of 14, 15 and 16

  • Each has the same number of waves of data i.e. 3 waves of data
  • All these 3 data waves were measured together at the same time
  • Measures across the time that are not independent

What Are The Strategies for Analyzing Longitudinal Data?

Traditional Repeated Measures ANOVA

  • Suppose that each child have the exact number of waves of data
  • It is assumed that all children measured at the same time points
  • It looks after the correlations among time points

traditional repeated measures anova

Mixed Models Anova

  • It is fine in case if a few children have more waves of data than others
  • The statement that is repeated assumes that all the children are measured at the same time points
  • It looks after the correlations among time points by using mixed models

mixed models anova

Regression

  • Don't worry if few kids have some extra waves of data than other kids
  • Here, you are not required to have people who are measured at the same schedule
  • Presupposes that there are no correlations between time points for the given person

regression

Multilevel Modelling

  • It is OK if few children have more waves of data as compared to others
  • It is OK if kids are measured on different schedules
  • The covariance structure is generally based on the distance (in time) from each other

multilevel modelling

Name

Must have the same number of waves of data

Must have the same measurement schedule

Covariance Structures Permitted

Format of Data

Traditional Repeated Measures

YES

YES

CS, UN

Person (Wide)

Mixed Repeated Measures

NO

YES

CS, AR, UN, etc

Person period

Regression

NO

NO

Independent

Person period

Multilevel Model

NO

NO

A type of AR and others are based on random effects

Person period

Assignment Topics Explained By Our Multilevel Longitudinal Modelling Assignment Expert

Multilevel Longitudinal Modelling assignment is a difficult task for students to cope with. The students are required to deal with various topics and few are described below by our Multilevel Longitudinal Modelling Assignment expert.

Use of Quantitative Data In Research

In this module, our Multilevel Longitudinal Modelling homework help experts provide the knowledge of the ways to collect data, provide a foundation of the firm for quantitative research. The main purpose of this module is to:

  • To provide a broad overview of different ways to answer research questions based on quantitative analysis. Students will learn the skills and ways to relate quantitative analysis to other methods, why it is required and what type of evidence is required.
  • To explain the vocabulary related to quantitative analysis and determine the general terminologies which can be used in later modules.
  • To let the students know what are the major sources of data and the concepts concerned about how it is possible to generalise results from samples collected from different populations.
  • To describe how variables are explained, their different types and the ways it may influence.

Multiple Regression

Multiple regression can be defined as a technique that is used to study the relationship between a set of predictor and explanatory variables and an outcome variable. Following are the topics covered:

    • Regression with a single continuous explanatory variable
    • Comparison of groups: Regression in a Single Categorical Explanatory Variable
    • Multiple Regression including discussion of the idea related to statistical control, the multiple regression model for categorical and continuous explanatory variables and non-linear modelling relationships.
    • What are the interaction effects?
    • Define testing for interaction effects
    • Monitoring the models' assumptions of multiple regression
    • Outliers

The university scholars who find themselves to cope with Multilevel structures and classifications, quantitative data analysis, multiple regressions, regression models for binary responses, etc or any other Multilevel Longitudinal Modelling assignment topics can reach My Assignment Services. At this platform, you will get help from experienced and skilled professionals who are available 24 hours all over the year without any stop.

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