Public Administration Numerical Dataset Creation

I will elaborate further once we actually have a chance to talk, but here are the quick summaries. Job Description: My research involves analyzing institutional isomorphism and its influence on contracting-out decisions by local health departments (LHDs). I have multiple datasets that need to be cleaned, merged, and prepared for statistical analysis. I would be looking for: Data Cleaning: Remove duplicates and inconsistencies. Handle missing data appropriately (e.g., imputation, removal based on criteria). Standardize variable names and formats across datasets. Possibly collecting the data for me if there is any missing from data files I have. Data Merging: Combine datasets from multiple sources (e.g., 2016 and 2019 NACCHO Profile Studies, socioeconomic data). Use shared identifiers (e.g., zip codes, county names) to match data accurately. Resolve mismatched or ambiguous records during the merging process. Variable Development: Create new variables based on specific instructions, including: Dependent Variable: Percentage of public health services contracted out. Independent Variables: Mimetic, coercive, and normative isomorphism measures. Control Variables: Population characteristics (e.g., % 65+, % non-white, median income), geographic classification (urban, suburban, rural), and jurisdiction type (city, county, multicounty). Generate interaction terms for independent variables as specified (e.g., mimetic × coercive). Validation and Documentation: Verify the accuracy of merged datasets and developed variables. Provide clear documentation of the steps taken, including: Data cleaning rules. Merging logic. Calculation methods for new variables. Statistical Preparation: Ensure the final dataset is ready for statistical analysis (e.g., in SPSS, R, or Python format). Check for issues like normality, outliers, and multicollinearity (optional but appreciated). Deliverables: Cleaned and merged Master Data File. Documentation detailing the cleaning, merging, and development processes. Any intermediate files or scripts used during the process. Qualifications: Expertise in data cleaning, merging, and variable development. Experience with academic research datasets (public health, social sciences, etc.). Proficiency in statistical software (e.g., Python, R, SPSS, or Stata). Strong communication skills for documenting processes.