AMSTAT’s Expertise

AMSTAT has become nationally recognized for providing its consulting and expert testimony services to Fortune 500 companies, major law firms, government agencies, and small businesses. Our successful clients frequently cite these reasons for choosing to work with AMSTAT.

All of our principals have PhDs in statistics at leading universities, including Harvard, Stanford, and Columbia. They include nationally renowned scholars. They have extensive backgrounds in statistics and over 100 years of practical experience in quantitative methods. Their experience includes analysis and testimony work for the Department of Justice, major law firms, and dozens of Fortune 500 firms. They have been admitted as experts in the fields of statistics and economics in a variety of federal and state courts throughout the United States, testifying on questions of statistical liability and damages in a variety of civil matters, including Medicare and Medicaid reimbursement cases, utility fee differentials, statistical methodology disputes, and statistical model disputes. Outside of direct testimony, they also provide more targeted statistical consulting support, and have authored independent analyses for numerous matters resolved via mediation or arbitration or otherwise outside of the litigation arena. They are experts in statistical analysis (e.g., ANOVA, linear regression, logistic regression, hierarchical regression analysis, correlation analysis, chi-square test, t-test, MANOVA, structural equation modeling, confirmatory factor analysis, factor analysis, principal components analysis, time series analysis, Cox regression, Kaplan-Meier survival analysis, hierarchical linear modeling, meta-analysis, predictive modeling, cluster 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, and interim analysis). 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 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 companies. We offer personalized, comprehensive, and friendly support during and after your consultation with us. We offer ultra-fast turnaround times. We are members in good standing of the Statistical Consulting section of the American Statistical Association.

Our Services

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.

Data Management and Test Results for Reliability and Validity

AMSTAT is dedicated to detecting and correcting corrupt or inaccurate records from a recordset, table, or database. The process of data cleaning includes data auditing, workflow specification, workflow execution, post-processing, and controlling. We can use popular methods. Those 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 run Outlier Analysis such as a run-sequence plot,  a scatter plot, a histogram, and a box plot.

We can test 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).

Statistical Analysis

We can design and perform the required statistical analyses.  Here is a sample of some of the analytical tools with which we are familiar:

  • Correlation Analysis, T-test, Chi-square Test, Regression Analysis, Logistic Regression, Hierarchical Regression Analysis, Factor Analysis, Principal Components Analysis, One-way ANOVA/ANCOVA, One-way MANOVA/MANCOVA, Factorial ANOVA/ANCOVA, Repeated Measures ANOVA/ANCOVA, Repeated Measures MANOVA/MANCOVA, Nonparametric Test (Wilcoxon signed rank test, Friedman test, Kruskal-Wallis test, Mann-Whitney test, Spearman Rank Correlation), Structural Equation Modeling (SEM), Multilevel SEM, Confirmatory Factor Analysis (CFA), Multilevel CFA, Exploratory Factor Analysis, Mediation Analysis, Moderation Analysis, Time Series Analysis, Spatial Time-series Modeling, Cluster Analysis, Cox Regression, Kaplan-Meier Survival Analysis, Trend Analysis, Sensitivity Analysis, Hierarchical Linear Modeling (HLM), Bayesian Analysis, Bayesian Cox Regression, Joint Hierarchical Bayesian Modeling, Latent Class Analysis, Longitudinal Growth Modeling, Mixed-Effects Regression Model, Meta-Analysis, Mixture Model, Linear Mixed Models, Predictive Modeling, Distribution Analysis (e.g., Lognormal, Weibull, Gamma), Decision Tree, Ensemble Analysis, De-identification, Interim Analysis, Item Analysis, Discriminant Analysis, Binomial Test, Heterogeneity Test, Multidimensional Scaling, Tau-U Analysis, Negative Binomial Regression, and Advanced Statistical Analysis

We have expertise in virtually every statistical and qualitative software package, including but not limited to:

  • SPSS, SAS, Stata, HLM, Mplus, R, SPSS Amos, SPSS Modeler, Azure, JMP, WinBUGS, Minitab, Match!3, Access, NVivo, Nud*ist, HyperResearch, and Atlas.

This list shows a sample of the past clients.

Examples of our work

Banking Fees & Financial Performance

September 14th, 2020|1 Comment

We measured the impact of banking fees on financial performance. The hypothesis was as follows: H1: Banking fees significantly affect financial performance. We performed a regression analysis to examine the hypothesis. Banking fees significantly and positively affected [...]

Internet Use & Perceived Performance

September 13th, 2020|1 Comment

We measured the impact of internet use on perceived performance in technology companies and age differences in internet usage. The hypotheses were as follows: H1:  Internet use significantly affects perceived performance in technology companies. [...]

Marketing Strategy & Performance

September 12th, 2020|0 Comments

We measured the effect of marketing strategy on sales performance. The hypothesis was as follows: H1. Marketing strategy significantly affects sales performance. A regression analysis was performed to examine the hypothesis. Marketing strategy significantly [...]

Motivation & Mathematics Performance

September 11th, 2020|0 Comments

We developed and tested a model, based on Self-Determination Theory (SDT), describing the effects of motivational resources on mathematics performance. The model was tested using data from the Third International Mathematics and Science Study-Revised [...]

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Discover our PhD-level experts’ secret tutorials

Secret Tutorial: Linear Regression Analysis

September 14th, 2020|0 Comments

A simple linear regression assesses the linear relationship between two continuous variables to predict the value of a dependent variable based on the value of an independent variable. More specifically, it will let you: [...]

Secret Tutorial: Independent Samples T-Test

September 13th, 2020|0 Comments

The independent-samples t-test is used to determine if a difference exists between the means of two independent groups on a continuous dependent variable. More specifically, it will let you determine whether the difference between [...]

Secret Tutorial: Binomial Logistic Regression

September 11th, 2020|0 Comments

A binomial logistic regression attempts to predict the probability that an observation falls into one of two categories of a dichotomous dependent variable based on one or more independent variables that can be either [...]

Secret Tutorial: One-Way ANOVA

September 10th, 2020|0 Comments

If you want to determine whether there are any statistically significant differences between the means of two or more independent groups, you can use a one-way analysis of variance (ANOVA). For example, you could [...]

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