Research Data Analysis

We accept a wide variety of data formats to accommodate different research needs:

Our team provides data preparation guidance and can assist with dataset cleaning if needed.

Statistical test selection follows a systematic decision-making process based on several key factors:

• Research Questions: We begin by clearly defining your research objectives and hypotheses to understand what you want to test or explore.
• Data Characteristics: We assess variable types (continuous, categorical, ordinal), sample sizes, and distribution properties to guide test selection.
• Study Design: We consider whether you have independent or paired samples, cross-sectional or longitudinal data, and experimental or observational designs.
• Assumption Testing: We evaluate statistical assumptions (normality, homoscedasticity, independence) and select appropriate parametric or non-parametric alternatives.
• Effect Size Considerations: We recommend tests that provide meaningful effect size measures and confidence intervals for practical significance interpretation.

Our statistical consultation includes detailed justification for all analytical choices and alternative approaches when assumptions are violated.

Our comprehensive analysis package includes multiple deliverables designed to support your research objectives:

All deliverables are provided in multiple formats (PDF, Word, Excel) and include source files for future reference.

Yes, we provide comprehensive support for results interpretation and academic writing:

Our writing support ensures your results are communicated clearly and meet academic publication standards.

Missing data and outlier management follows established statistical best practices:

• Missing Data Assessment: We evaluate missing data patterns (MCAR, MAR, MNAR) using Little's MCAR test and pattern analysis to understand missingness mechanisms.
• Imputation Methods: We employ appropriate imputation techniques including mean/median substitution, multiple imputation, or model-based approaches depending on data characteristics and missingness extent.
• Outlier Detection: We use multiple methods including z-scores, interquartile range, Mahalanobis distance, and visual inspection to identify potential outliers.
• Outlier Treatment: Treatment options include transformation, winsorization, robust statistical methods, or sensitivity analysis comparing results with and without outliers.
• Transparency: All missing data and outlier handling decisions are documented with rationale and impact assessment on final results.
• Sensitivity Analysis: We conduct analyses with different handling approaches to assess robustness of findings.

Our approach ensures data integrity while maximizing analytical power and maintaining statistical validity.

Reproducibility is fundamental to our analytical approach and scientific integrity:

• Code Documentation: All analyses include thoroughly commented code with step-by-step explanations of procedures, parameter choices, and decision rationale.
• Version Control: We maintain detailed records of software versions, package versions, and analysis scripts with timestamps and modification history.
• Data Provenance: Complete documentation of data sources, preprocessing steps, variable transformations, and quality control procedures.
• Replication Files: Provision of complete replication packages including data, code, and detailed instructions for independent verification.
• Validation Procedures: Independent verification of results by secondary analysts and cross-validation using alternative software when appropriate.
• Methodology Documentation: Comprehensive documentation of analytical decisions, assumption testing, and sensitivity analyses for complete transparency.

Our reproducibility framework ensures that your analysis can be independently verified and extended for future research.