Data Management and Tests 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, NVivo, WinBUGS, Minitab, Match!3, and Access.


AMSTAT combines deep expertise with cutting-edge capabilities across product design, digital, advanced analytics, and artificial intelligence, to help medtech companies better understand unmet patient, user, and customer needs, identify growth opportunities and create new business models. From strategy development to commercialization, we help build innovative products, devices, equipment, software, and solutions that bring customer value and enhance patient outcomes.

22 of top 50 pharmaceutical companies served

22 of top 50 pharmaceutical companies served

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5+ of the top 10 medical device companies served

5+ of the top 10 medical device companies served

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200+ fast-growth startups served

200+ fast-growth startups served

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Examples of our work

Relative Risk of Lung Cancer 

September 21st, 2020|2 Comments

We compared the difference in relative risk (RR) of lung cancer between the intervention and control groups. The hypothesis was as follows: H1: The intervention group will have an increase in relative risk (RR) of [...]

Blood Pressure

September 20th, 2020|0 Comments

We examined the difference in blood pressure between patients with cancer and patients without cancer. The hypothesis was as follows: H1. There is a significant difference in blood pressure between patients with cancer and [...]

Bending Stiffness

September 19th, 2020|0 Comments

We measured the difference among the 2.0mm RP and 2.0mm SP in bending stiffness, bending strength, and bending structural stiffness. The hypothesis was as follows: H1: There is a significant difference among the 2.0mm RP [...]

Compound D-600

September 18th, 2020|0 Comments

We examined the effect of compound D-600 (methoxyverapamil) on gluconeogenesis. The hypothesis was as follows: H1: There is a significant effect of compound D-600 on gluconeogenesis. A regression analysis was conducted. There was a [...]

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Discover our PhD-level experts’ secret tutorials

Secret Tutorial: Linear Regression Analysis

September 21st, 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 20th, 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 18th, 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 17th, 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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