What is it? Data preparation is a frequently misunderstood or skipped stage prior to analysis that is critical for ensuring the right data is analyzed in the right way. Before analyzing data, it is necessary to understand the characteristics of the data and key variables in order to determine the appropriate analysis. Invalid data, patterns of missing data, and unmet normality assumptions are likely to result in analysis that is misleading or inaccurate. Our data preparation process cleans, organizes, and prepares your data for comprehensive and accurate analysis. Data removal or transformation may be required for desired analysis that reduces the probability of making a Type I or Type II error.

Data preparation involves specific technical steps of managing different forms and sources of data. There are many statistical software packages and languages used in personal, professional, academic, and technical settings. Observations that reflect inaccurate, inattentive, or careless response values can seriously bias your study and should be removed prior to analysis. Missing data can also have a profound effect on a study’s validity depending on how severe the missing data is within a dataset. Assumptions testing is a crucial step before conducting inferential analyses to avoid bias in your study’s findings. The validity of conclusions drawn from a statistical analysis depends on the validity of any assumptions made regarding normal distributions of values and residuals.  All of these separate data preparation steps serve to clean and prep the data for robust and accurate analysis.

The strongest analysis comes from the best foundation of data preparation. Our team of analysts and consultants have experience having prepared hundreds of datasets for optimal analysis impact. They can help streamline and smooth the process of collecting, cleaning, and analyzing data across different data sources and analytic software packages/programming languages. Our team will deliver reports highlighting invalid data, missing data, reliability of variable constructs, and assumptions testing that determines viable analysis options. Processes to address potential problems in this stage of research will save you time and money while also delivering maximum results for the analysis and results.

How can we help you?

• Merge and analyze datasets in different statistical analysis software packages/languages
• Establish research methodology and data collection practices to safeguard against invalid data
• Determine survey participants that should be excluded from the analysis based on criteria and nature of responses
• Determine problematic patterns of missing data
• Recode categorical and continuous variables to better suit analysis
• Data cleaning and preparation to accurately test assumptions
• Reduce the impact of unmet expectations through nonparametric analysis
• Determine and reduce the probability of Type I or Type II errors