## HOW WE HELP CLIENTS

# 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.

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## Examples of our work

#### Relative Risk of Lung Cancer

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

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

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

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 [...]

## Discover our PhD-level experts’ secret tutorials

#### Secret Tutorial: Linear Regression Analysis

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

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

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

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 [...]