death surveys coder Interview Questions and Answers

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Death Survey Coder Interview Questions and Answers

  1. What is your experience with coding in various programming languages relevant to data analysis (e.g., Python, R, SQL)?

    • Answer: I have [Number] years of experience in coding, proficient in [List Languages]. I've used Python extensively for data manipulation and analysis using libraries like Pandas, NumPy, and Scikit-learn. My experience with R includes using packages like dplyr, tidyr, and ggplot2 for data cleaning, transformation, and visualization. I'm also comfortable with SQL for database management and querying large datasets. I can provide specific examples of projects where I've utilized these skills.

  2. Describe your familiarity with statistical methods used in analyzing mortality data.

    • Answer: I'm familiar with a range of statistical methods crucial for analyzing mortality data, including survival analysis (Kaplan-Meier curves, Cox proportional hazards models), regression analysis (linear, logistic), and methods for handling censored data. I understand the importance of controlling for confounding variables and interpreting hazard ratios and odds ratios in the context of mortality studies. I'm also experienced with assessing model fit and diagnostics.

  3. How would you handle missing data in a death survey dataset?

    • Answer: The approach to handling missing data depends heavily on the nature of the missingness (Missing Completely at Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR)). I would first investigate the patterns of missing data. Techniques I would consider include imputation methods (e.g., multiple imputation using chained equations, k-nearest neighbors imputation) for MCAR/MAR data. For MNAR data, more advanced techniques like selection models or pattern-mixture models might be necessary. I would also consider the impact of different imputation methods on the final results and potentially use sensitivity analysis to assess the robustness of my conclusions.

  4. Explain your experience with data cleaning and preprocessing techniques.

    • Answer: Data cleaning is a critical part of my workflow. I have extensive experience identifying and handling inconsistencies, outliers, and errors in datasets. My techniques include checking for data type mismatches, handling missing values (as described above), removing duplicates, and transforming variables as needed (e.g., recoding categorical variables, creating new variables). I typically use scripting languages (Python or R) to automate these tasks for efficiency and reproducibility.

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