## 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.

# Dissertation Consulting Services for Doctoral Students

We provide highly customized writing services. Our experience enables us to write your dissertation’s introduction chapter, assisting with:

- Stating the research problem.
- Stating the purpose of the study
- Stating the research questions
- Providing a concise rationale for the selection of the design and tradition
- Identifying potential contributions of the study that advance knowledge in the discipline.

Our experience enables us to write your dissertation’s literature review chapter, assisting with:

- Identifying the theory or theories and provide the origin or source.
- Describing studies related to the constructs of interest and chosen methodology and methods that are consistent with the scope of the study.
- Describing ways researchers in the discipline have approached the problem and the strengths and weaknesses inherent in their approaches.
- Justifying from the literature the rationale for selection of the variables or concepts.
- Reviewing and synthesizing studies related to the key concepts and/or phenomena under investigation to produce a description of what is known about them, what is controversial, and what remains to be studied.
- Reviewing and synthesizing studies related to the research questions and why the approach selected is meaningful.

Our experience enables us to write your dissertation’s methods section, assisting with:

- Stating the study variables (e.g., independent, dependent), as appropriate.
- Identifying and justifying the type of sampling strategy.
- Using a power analysis to determine sample size and include: Justification for the effect size, alpha level, and power level chosen.
- Describing recruiting procedures and particular demographic information that will be collected.
- For published instruments providing:

Name of developer(s) and year of publication.

Appropriateness to the current study.

Published reliability and validity values relevant to their use in the study. - For all researcher instruments providing:

Basis for development (literature sources or other bases for development)

Plan to provide evidence for reliability (e.g., internal consistency and test/retest) and validity (e.g., predictive and construct validity). - Identifying software used for analyses.
- Providing the explanation of data cleaning and screening procedures.
- Describing in detail the analysis plan, including the elements below including:

Statistical tests that will be used to test the hypothesis (es).

Procedures used to account for multiple statistical tests, as appropriate.

The rationale for inclusion of potential covariates and/or confounding variables.

How results will be interpreted.

Our experience enables us to write your dissertation’s results chapter, assisting with:

- Reporting descriptive statistics that appropriately characterize the sample.
- Evaluating statistical assumptions as appropriate to the study (quantitative).
- Reporting statistical analysis findings, organized by hypotheses – We can report any additional statistical tests of hypotheses that emerged from the analysis of main hypotheses (quantitative).

Our experience enables us to write your dissertation’s discussion chapter, assisting with:

- Describing in what ways findings confirm, disconfirm, or extend knowledge in the discipline by comparing them with what has been found in the peer-reviewed literature described in chapter 2.
- Describing the limitations to trustworthiness that arose from the execution of the study.
- Describing recommendations for further research that are grounded in the strengths and limitations of the current study. We can ensure recommendations do not exceed study boundaries.
- Describing the potential impact for positive social change at the appropriate level (individual, family, organizational, and societal/policy).

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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 [...]