Quantitative market research aims to identify standardized facts and statistics that can reliably guide business decisions. But no matter how well data is analyzed, if it’s not accurate or complete findings based on it are flimsy at best.
Think of it like baking a cake – if you use poor ingredients or measure incorrectly, the final product won’t be good. The same concept applies to data. That’s why ensuring you’re using quality data in quantitative research is so critical.
What is Quality Data?
Data quality gauges the value of data based on accuracy, completeness, consistency, reliability, and whether it’s current. Defining data quality can be complex, but it basically means that the data fit their intended purpose and are closely related to the construct it’s intended to measure.
Quantitative, Qualitative Data Focus on Different Points
Quantitative research is sometimes preferred over qualitative research because it is more scientific, objective, and focused. However, qualitative research also yields valuable results because it helps define an issue or develop a solution to a problem. In a nutshell, quantitative data is countable or measurable and numbers based. Qualitative data are descriptive and more related to words.
Examples of quantitative data are a company’s financial data, marketing results, statistical data on demographics, or revenue in dollars. Quantitative research takes a deep dive into specific markets and customer trends and provides insights that help companies better understand industry patterns.
Qualitative data is less concrete than its quantitative counterpart because it looks at certain qualities or characteristics. It is collected using questionnaires, interviews, and observations, and is generally summarized in narrative form. The data may include descriptive words that can be reviewed for patterns or meaning, sometimes through coding. Coding allows researchers to categorize qualitative data to identify themes related to the research questions and analyze them.
Methods to Measure Data Quality in Quantitative Research
Once the research phase of a project is underway and data collection begins, it’s important to take a close look at how reliable and accurate that data is before doing an in-depth analysis or coming to any conclusions. Here are some key areas used to gauge the quality of data to make sure findings are adjusted and weighed appropriately.
Correctness of answers:
Correct data accurately measures a construct of interest such as customer satisfaction or people’s intention to purchase a particular product or service.
Assessing the correctness of data involves evaluating whether people provided consistent responses to similar items. Researchers may ask survey questions that are either synonymous (“I would buy this product” or “I plan to buy this product in the future.”) or antonymous (I’d consider paying for this service” or “I would probably never need this service.”) The distance between each person’s responses to each question would then be reviewed.
Consistency in survey responses:
Evaluating the consistency of individual responses at the group level comes down to measures of reliability. This tells researchers how well a test measures what it should measure. Researchers look at response consistency by people surveyed by identifying logical contradictions in their responses or inconsistent answers to specific questions designed to elicit the same information (e.g., “What is your age?” “What year were you born?”). People who give too many inconsistent responses can be dropped from the dataset to avoid skewing overall results.
Completeness of responses:
Complete datasets are where most people answer all the survey questions and those who start the survey finish it. Researchers may specify before collecting data that people must have viewed or responded to specific questions that align with key outcome measures.
Respondent credibility:
Having credible datasets means respondents answer the survey questions honestly. Researchers use several tools to uncover individual participant responses that lack credibility. These tools range from measures designed to detect overly positive or negative self-presentation to methods that assess people’s attention, effort, unusual response patterns, speed of completing the survey, and intentional false responses to questions.
Expert Quantitative Marketing Research
ResponsivMR’s team of experts has decades of experience providing advanced analytics consulting support to market research agencies of all sizes. Our goal is to make the data work for you by leveraging standard statistical tools to analyze data and provide a one-stop solution for all facets of fieldwork. To find out how ResponsivMR can help your firm get the most value from survey data contact us at 561-983-4949.