Tips for collecting qualitative and quantitative data including available platforms for survey research, strategies for conducting interviews, ways of coding and organizing different types of data, guidance for entering data into a database, and transcription services recommended by UW-Madison. Links to training guides for various software for data analysis and guidance for handling missing data. Main menu | Comments/Suggestions
| Quantitative and qualitative data | Qualitative vs Quantitative Data – What’s the Difference? |
| Types of data measures – The kind of data you have determines the kind of analyses that you can perform | Types of statistical data: Numerical, categorical and ordinal |
| Qualitative and quantitative data collection | An overview of quantitative and qualitative data collection methods
A nurses’ guide to quantitative research Questionnaire design: Theory and best practice Data collection in qualitative research Qualitative research: Data collection, analysis, and management Community Toolbox: Conducting Focus Groups Community Toolbox: Conducting Interviews Community Toolbox: Conducting Surveys Preparing to conduct an interview or focus group Sample Protocol for Individual Interviews, Focus Groups, and Community Meetings |
| Using Qualtrics for survey research | Building Surveys in Qualtrics |
| Manual data entry – Use a template to ensure data fields are configured appropriately to reduce error and support statistical analyses | Contact Roger Brown (rlbrown3@wisc.edu) in the School of Nursing Research Office to have a template created for data entry for your research project. |
| Participant tracking tables | These tables are used to track research participants over the course of a study. Information included will be tailored to each study but may include contact dates and methods, eligibility, participation (or opted out), receipt of materials (informed consent), completion of study phases, and other key data. Examples of participant tracking tables and recruitment log. |
| Missing data – What it is and what to do when you find it | There are two categories of missing data, missing values and missing cases. Missing data are values for variables that were not collected from a study participant whereas missing cases are values for entire sets of variables that were not collected from a participant. If a participant completing a survey chooses not to answer a question or if the answer for a question does not get recorded, this is an example of a missing value. For missing cases, consider the situation where a participant is enrolled in a 10-year longitudinal study where they are asked to complete a survey once every two years (five waves – once every two years over 10 years). If the participant completes the survey for all waves except the third, the researcher would have a missing case. Missing data can occur for many reasons, the most common is that participants choose not to answer a question. What to do with missing data is more complicated that you’d expect – you need to determine whether the data are missing at random or not at random because this will have significant effects on your data analysis, therefore your findings. When you come across missing data, let your faculty researcher know and consider meeting with Roger Brown (rlbrown3@wisc.edu) in the School of Nursing Research Office to discuss the best way to handle the missing data in your statistical analyses For more information: |
| Transcription and translation services | Services are available for researchers who collect audio recorded data that need to be transcribed from an audio file to a text file. These vendors also provide translation services for data that are collected in a different language than what the data will analyzed and findings reported in. Contact NRSP for a current list of approved vendors.
UW-Madison has a list of AI tools approved by UW-Madison for transcription that may include PHI. Review this carefully. Use a contracted vendor or UW-Madison employee (e.g. student hourly) if these are insufficient for your needs. Transcribing yourself? Contact uwsonit@son.wisc.edu to inquire about availability of a foot pedal to control the recording while typing and review your PI’s example of a format or use this sample as a guide. |
| Using databases and spreadsheets to organize and analyze data | Databases vs. spreadsheets – what is the difference?
The beginner’s guide to Excel – Excel Basics Tutorial Microsoft Access for Beginners Videos EpiData – An Introduction to EpiData; Tutorials NCSS – Quick Start Manual; Training Videos Databases and statistical software available through UW-Madison Campus Software Library: – QSR-NVivo (qualitative data) – NVivo Resources; NVivo Tutorials – R – Online learning; R Manuals – SAS – SAS Tutorials – SPSS – Beginners Tutorials – STATA – Resources for learning STATA UW-Madison Social Science Computing Cooperative Training Classes |
| Coding and codebooks – The process for coding qualitative and quantitative data are quite different, yet both are critical steps in the research process. How data are coded have significant implications for how and what analyses can be conducted as well as how to interpret findings. Maintaining a code book allows you to keep track of what you did and particular decisions you made about the data or variables. As a result, it is one of the most important means of ensuring rigor in your analytic methods. | LinkedIn Learning: www.linkedin.com/learning – search “coding data”
Qualitative Coding: What is a Code? Qualitative Research Methods video Basics of qualitative coding video What does coding look like? Qualitative Research Methods video The importance of memo writing with qualitative data analysis and coding video
Quantitative Coding: Coding a survey video Preparing questionnaire data video |