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Brian Hunter

Name: Brian Hunter
Photo - Brian Hunter
Location: Houston
United States 77006
Contact: Brian Hunter 832-577-2963

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Additional Info: My name is Brian Hunter, M.A., C.L.S.S.B.B. and I offer Statistical and Academic Services for Graduate and Undergraduate Students, Ph.D. Candidates, and all others needing statistical and research method consultation.

I am an Accredited Member of the American Statistical Association, Certified Lean Six Sigma BB, an IASSC Certified Six Sigma Trainer, and my business is accredited by the Better Business Bureau.


Work/Tutoring Sessions are usually in person at my office or via Skype. We can also work via email, telephone. www.bhstats.com

Getting your Ph.D or Master's Degree?

Need help Starting or Completing your Dissertation, Master's Thesis, Capstone, or Master's Project?

I can help with:

Literature Review and Hypotheses Development
Research Design and Methods
Database Development
Statistical Analysis, Interpretation, and Formatting
Results Analysis Write-up
Discussion Section Interpretation

Statistical Analysis

I conduct many different statistical analyses to meet your needs. Below is a list of some of the analyses I provide. Please note this is not an exhaustive list.

ANOVA (Analysis of Variance)
Factorial ANOVA
Repeated Measures ANOVA
ANCOVA (Analysis of Covariance)
Factorial ANCOVA
Repeated Measures ANCOVA
Binary Logistic Regression
Bivariate Correlation
Chi-Square Goodness-of-Fit
Chi-Square Test of Independence
Dependent Samples t-test
Discriminate Analysis
Exploratory Factor Analysis
Hierarchical Multiple Regression
Independent Samples t-test
Linear Regression
MANOVA (Multivariate Analysis of Variance)
Factorial MANOVA
Repeated Measures MANOVA
MANCOVA (Multivariate Analysis of Covariance)
Factorial MANCOVA
Repeated Measures MANCOVA
Mediation Analysis
Multinomial Logistic Regression
Multiple Regression
Multivariate Regression
Phi Correlation
Point-Biserial Correlation
Reliability Analysis
Spearman Correlation
Stepdown Regression
Stepwise Regression
Structural Equation Modelling
Test for Moderation

Premium Statistical Services:

Dissertations, Thesis, and Proposals
Literature Review
Database Development
Full Statistical Analysis
Results Analysis
Discussion Section
APA Statistical Formatting


All Statistics Courses available to be taught to you!
Some Examples are:

Inferential Statistics
Descriptive Statistics
Behavioral Sciences Statistics
Business Statistics
Demand Forecasting
Research Design
Elementary Statistics
Introduction to Statistics
Quantum Methods
Data Mining
Aggregate Planning
Multiple and Linear Regression
Linear Programming
ANOVA Models
Sampling Methods
Experimental Design
Experimental Statistics

Inferential Statistics

With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data.

Here, I concentrate on inferential statistics that are useful in experimental and quasi-experimental research design or in program outcome evaluation. Perhaps one of the simplest inferential test is used when you want to compare the average performance of two groups on a single measure to see if there is a difference. You might want to know whether eighth-grade boys and girls differ in math test scores or whether a program group differs on the outcome measure from a control group. Whenever you wish to compare the average performance between two groups you should consider the t-test for differences between groups.

Most of the major inferential statistics come from a general family of statistical models known as the General Linear Model. This includes the t-test, Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), regression analysis, and many of the multivariate methods like factor analysis, multidimensional scaling, cluster analysis, discriminant function analysis, and so on. Given the importance of the General Linear Model, it's a good idea for any serious social researcher to become familiar with its workings. The discussion of the General Linear Model here is very elementary and only considers the simplest straight-line model. However, it will get you familiar with the idea of the linear model and help prepare you for the more complex analyses described below.

One of the keys to understanding how groups are compared is embodied in the notion of the "dummy" variable. The name doesn't suggest that we are using variables that aren't very smart or, even worse, that the analyst who uses them is a "dummy"! Perhaps these variables would be better described as "proxy" variables. Essentially a dummy variable is one that uses discrete numbers, usually 0 and 1, to represent different groups in your study. Dummy variables are a simple idea that enable some pretty complicated things to happen. For instance, by including a simple dummy variable in an model, I can model two separate lines (one for each treatment group) with a single equation. To see how this works, check out the discussion on dummy variables.

One of the most important analyses in program outcome evaluations involves comparing the program and non-program group on the outcome variable or variables. How we do this depends on the research design we use. research designs are divided into two major types of designs: experimental and quasi-experimental. Because the analyses differ for each, they are presented separately.

Experimental Analysis. The simple two-group posttest-only randomized experiment is usually analyzed with the simple t-test or one-way ANOVA. The factorial experimental designs are usually analyzed with the Analysis of Variance (ANOVA) Model. Randomized Block Designs use a special form of ANOVA blocking model that uses dummy-coded variables to represent the blocks. The Analysis of Covariance Experimental Design uses, not surprisingly, the Analysis of Covariance statistical model.

Quasi-Experimental Analysis. The quasi-experimental designs differ from the experimental ones in that they don't use random assignment to assign units (e.g., people) to program groups. The lack of random assignment in these designs tends to complicate their analysis considerably. For example, to analyze the Nonequivalent Groups Design (NEGD) we have to adjust the pretest scores for measurement error in what is often called a Reliability-Corrected Analysis of Covariance model. In the Regression-Discontinuity Design, we need to be especially concerned about curvilinearity and model misspecification. Consequently, we tend to use a conservative analysis approach that is based on polynomial regression that starts by overfitting the likely true function and then reducing the model based on the results. The Regression Point Displacement Design has only a single treated unit. Nevertheless, the analysis of the RPD design is based directly on the traditional ANCOVA model.
When you've investigated these various analytic models, you'll see that they all come from the same family -- theGeneral Linear Model. An understanding of that model will go a long way to introducing you to the intricacies of data analysis in applied and social research contexts.
Fees: $ 35.00 Dollars Per Hour
Subjects: Psychology, Sociology, Statistics
Additional: CRB / Police Check, References, Can Travel, Can Tutor Online
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