## 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

#### Banking Fees & Financial Performance

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

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

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

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 [...]

## 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 [...]