Quantitative and Statistical Services
Get help with quantitative and statistical services! Are you planning on including quantitative analysis in your thesis or dissertation but aren’t sure where to start?
We have experts in statistical analysis who can help you at any stage of the process, from determining what tests to use, collecting and cleaning data, and completing the analysis portion.
If your thesis statement has led you to an analytical approach to your research questions, we’ll help you design and execute that section.
You can also call on us to help you through interpretation or maybe just run some simple statistics.
There are many possibilities for statistical analysis; here are some of the more common and a few more complex quantitative and statistical services we can execute.
Exploratory Data Analysis (EDA): Tests for Normality (e.g., Kolmogorov-Smirnov, Shapiro-Wilks, (Kurtosis/SE)>2, (Skewness/SE)>2), Histograms, Q-Q Plots, Log-Log Plots, Mean/Median/Mode/Standard Deviation/Variance/Standard Error
G*Power Analysis or Cochran's formula: Ensure you choose the proper sample size for your statistical tests, including chi-square, exact, t-tests, F-tests, and z-tests.
Difference of Means (t, z, Wilcoxon): One of the most commonly used types of tests; determines if a significant difference exists between the mean of two groups.
Tukey's Range Test (aka Tukey's Honestly Significant Difference, or Tukey's HSD): Multiple comparison modeling (pairwise) in which the means of a group are compared to all other means of groups in the model (similar to a t-test, with an extra correction component for standard error).
ANOVA (Analysis of Variance): Also a difference-of-means test, but for multiple columns. A very popular method in the social sciences, ANOVA will hold up well to violations of pre-test assumptions such as equal variance or a normal distribution of your data (a shortcoming of other difference-of-means tests, such as a t-test or Tukey's HSD).
Chi-square: Another commonly used test that determines if a set of observations occurs more frequently or less frequently than expected.
Correlation (Pearson's r, Spearman's rho, Kendall's T): The one almost everyone has at least heard of. Correlation analysis determines if two variables exhibit a linear relationship; as one goes up, does the other follow along (up or down) at some level of statistical significance? While correlation cannot tell you the cause, it can tell you quickly if your two phenomena are related in some way.
Regression (Binary, Multivariate): Widely-used and relatively straightforward, this method works to uncover the influence a group of variables (the independents) has on a single variable (the dependent).
Composite Index Construction: Using weights derived from analysis (e.g., PCA, MCDA), virtually any continuous, interval, or ratio data set can be transformed into a composite scaled and ranked index, which can greatly enhance the statistical section of your thesis. We can essentially construct a highly customized rating system for some quantitative component of your results.
Dimensionality (data) Reduction: Principal Components Analysis (PCA) and Linear or General Discriminant Analysis (LDA or GDA) reveal relationships hidden within very large datasets and reduce the data to groups of components or factors that help explain some assumption or relationship. These methods are very useful if you have a lot of data and are unsure how the pieces might be connected. Factors, components, and eigenvalues can be used in additional model building, such as regression or generalized linear modeling.
Multi-Criteria Decision Analysis (MCDA) is a broad term for one of several methods to reduce complex decision-making processes to understandable results. The Analytic Hierarchy Process (AHP) is a powerful and flexible method of MCDA, using expert elicitation to transform the qualitative opinions of experts into useable weights and measures.