Quantitative Analysis
We live in a data driven society where people are increasingly looking to numbers to describe, explain, and predict phenomena in our businesses, charities, sports, education, personal lives, and more. Quantitative analysis is the most comprehensive way to draw conclusions from direct observation and measurement. Our extensive quantitative analytic services empower you to collect and analyze data to answer your most pressing research needs.
Statistical Analysis
What is it? Statistical analysis is the science of examining raw data and uncovering patterns and trends. Statistical analysis helps researchers and businesses identify new opportunities and make better business decisions. There are two main statistical techniques for data analysis: descriptive statistics and inferential statistics. Descriptive statistics describe the nature of data to be analyzed. This type of statistics presents raw data and simply shows what it is, but it does not allow for making conclusions. Inferential statistics studies the relationships between variables and draws a conclusion from the data; this type of statistics is a more complicated mathematical calculation.
There are numerous statistical methods, such as simple univariate analyses, multivariate analyses, factor analysis, cluster analysis, various types of regressions, hierarchical models, structural equation modeling, meta-analyses, and more. Choosing an appropriate statistical analysis is more important than selecting the fanciest and complicated statistical analysis.
If you are unsure what the best analyses should be used, let our consultants help you.
How can we help you?
- Evaluate and select the appropriate statistical methods
- Describe sample characteristics using frequency and descriptive statistics
- Explore the relationships of the data
- Create basic statistical analyses, including t test, univariate ANOVA, correlation, and chi-square
- Create advanced statistical analyses, including regression, multi-variate analysis, and multi-level modeling
- Perform longitudinal analyses, including repeated measures ANOVA/MANOVA, survival analysis, and time series
- Perform factor analysis, reliability, and cluster analysis
- Conduct nonparametric analyses
- Evaluate intervention effects (RCT)
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Learn MoreArticles and White Papers About Quantitative Statistical Analysis
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Read MoreFAQ About Statistical Analysis
A one-sample t-test compares measured values against the mean values for known (published) or hypothesized values.
Small sample sizes will have lower statistical power. This requires a greater effect size in order to obtain significant findings. This means that if the expected difference between two different groups is not large enough, it will not result in significant findings for smaller samples in certain analyses.
Data mining seeks to discover patterns in data whereas data analysis tests a hypothesis to which the answer affects processes or phonemena.
Effective database management allows you to organize, store, retrieve, and analyze data in consistent,predictable and repeated manner with the highest levels of efficiency and integrity of data.
Analytics projects generally require research design/methodology, data collection, data cleaning, analysis, results, presentation, and implementation. There are numerous intermediate processes for various steps depending on how customized and complicated the project is.
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