Our successful clients cite these reasons for choosing to work with AMSTAT Consulting.

• All of our principals have PhDs in statistics at leading universities, including Harvard, Stanford, and Columbia. They include nationally renowned experts.
• They have extensive backgrounds in statistics and biostatistics. They are experts in statistical analysis (e.g., linear regression, logistic regression, hierarchical regression analysis, correlation analysis, chi-square test, t-test, ANOVA, MANOVA, structural equation modeling (SEM), multilevel SEM, confirmatory factor analysis (CFA), multilevel CFA, factor analysis, principal components analysis, time series analysis, Cox regression, Kaplan-Meier survival analysis, hierarchical linear modeling (HLM), meta-analysis, Bayesian analysis, latent class analysis, longitudinal growth modeling, mixture model, linear mixed models, distribution analysis, ensemble analysis, de-identification, trend analysis, sensitivity analysis, negative binomial regression, interim analysis, decision tree, item analysis, nonparametric test, statistical and decision modeling, maxDiff, segmentation and cluster analysis, conjoint/discrete choice, predictive analytics).
• Our programmers have 3,700 years of accumulated programming experience across the company. They are experts in statistical programming languages such as SPSS, SAS, Stata, R, HLM, Mplus, SPSS Amos, SPSS Modeler, Azure, JMP, WinBUGS, Minitab, Match!3, and Access.
• Our number one priority is ensuring on time, quality work: every project undertaken is supervised on methodology and utilizes internal processes designed to guarantee optimal quality.
• Our fees usually pale in comparison to the savings and additional profits that our work produces for our clients. Over 90% of our clients request our assistance more than once, as our clients are almost universally happy with our different brands of consulting.
• We are more reasonably priced than most other consultants. We offer personalized, comprehensive, and friendly support during and after your consultation with us. We offer ultra-fast turnaround times.

We are happy to provide the help you need at any or all of the following steps in earning the data-based answers you request:

• Establishing and operationalizing your hypotheses and research questions
• Providing ample instruction on the methods used
• Inputting, organizing, and cleaning the data
• Implementing the statistical analyses
• Testing reliability (such as Cronbach’s alpha, test-retest reliability, split-half reliability, and inter-rater reliability) and validity (such as content validity, construct validity, criterion validity, internal validity, and external validity)
• Writing up all results, including APA tables and figures
• Providing syntax and raw output file
• Explaining the results
• Allowing unlimited e-mail and phone support to ensure that you completely understand the results of the analysis
• Conducting two rounds of incidental statistics (i.e., if you would like additional statistics)
• Preparing an effective PowerPoint Presentation.

We can correct corrupt or inaccurate records from a recordset or database. The process of data cleaning includes data auditing, workflow specification, workflow execution, post-processing, and controlling. These include parsing, data transformation, duplicate elimination, and statistical methods. By analyzing the data using the values of mean, standard deviation, range, and clustering algorithms, we can find values that are unexpected and thus erroneous. We can examine any standardized residual greater than about 3 in absolute value, Hat element greater than 3p/n (p=k+1, k degrees of freedom), a Cook’s distance > 1, and Mahalanobis’s distance. We can run outlier analysis such as a run-sequence plot, a scatter plot, a histogram, and a box plot. We can test reliability and validity.

We are experts in statistical analysis (e.g., linear regression, logistic regression, hierarchical regression analysis, correlation analysis, chi-square test, t-test, ANOVA, MANOVA, structural equation modeling (SEM), multilevel SEM, confirmatory factor analysis (CFA), multilevel CFA, factor analysis, principal components analysis, time series analysis, Cox regression, Kaplan-Meier survival analysis, hierarchical linear modeling (HLM), meta-analysis, Bayesian analysis, latent class analysis, longitudinal growth modeling, mixture model, linear mixed models, distribution analysis, ensemble analysis, de-identification, trend analysis, sensitivity analysis, negative binomial regression, interim analysis, decision tree, item analysis, nonparametric test, statistical and decision modeling, maxDiff, segmentation and cluster analysis, conjoint/discrete choice, predictive analytics).