Capstone projects are culminating academic experiences that students undertake at the end of their studies. These projects allow students to demonstrate their knowledge and skills by undertaking an independent research or design project. When conducting research or evaluation for a capstone project, students will typically gather both quantitative and qualitative data.
Quantitative data refers to any data that is in numerical form such as statistics, percentages, counts, rankings, scales, etc. Quantitative data is based on measurable factors that can be analyzed using statistical techniques. Some examples of quantitative data that may be collected for a capstone project include:
Survey results containing closed-ended questions where respondents select from preset answer choices and their selections are counted. The surveys would provide numerical data on frequencies of responses, average scores on rating scales, percentages agreeing or disagreeing with statements, etc.
Results from psychological or skills tests given to participants where their performance or ability levels are measured by number or score.
Financial or accounting data such as sales figures, costs, profits/losses, budget amounts, inventory levels that are expressed numerically.
Counts or frequencies of behavioral events observed through methods like timed sampling or duration recording where the instances of behaviors can be quantified.
Content analysis results where the frequency of certain words, themes or concepts in textual materials are counted to provide numerical data.
Numerical ratings, rankings or scale responses from areas like job performance reviews, usability testing, customer satisfaction levels, or ratings of product qualities that are amenable to statistical analyses.
The advantage of quantitative data for capstone projects is that it lends itself well to statistical analysis methods. Quantitative data allows for comparisons and correlations to be made statistically between variables. It can be easily summarized, aggregated and used to test hypotheses. Large amounts of standardized quantitative data also facilitate generalization of results to wider populations. On its own quantitative data does not reveal the contextual factors, personal perspectives or experiences behind the numbers.
In contrast, qualitative data refers to non-numerical data that is contextual, descriptive and explanatory in nature. Some common sources of qualitative data for capstone projects include:
Responses to open-ended questions in interviews, focus groups, surveys or questionnaires where participants are free to express opinions, experiences and perspectives in their own words.
Field notes and observations recorded through methods like participant observation where behaviors and interactions are described narratively in context rather than through numerical coding.
Case studies, stories, narratives or examples provided by participants to illustrate certain topics or experiences.
Images, videos, documents, or artifacts that require descriptive interpretation and analysis rather than quantitative measurements.
Transcripts from interviews and focus groups where meanings, themes and patterns are identified through examination of word usages, repetitions, metaphors and concepts.
The advantage of qualitative data is that it provides rich descriptive details on topics that are difficult to extract or capture through purely quantitative methods. Qualitative data helps give meaning to the numbers by revealing contextual factors, personal perspectives, experiences and detailed descriptions that lie behind people’s behaviors and responses. It is especially useful for exploring new topics where the important variables are not yet known.
Qualitative data alone does not lend itself to generalization in the same way quantitative data does since a relatively small number of participants are involved. It also requires more time and resources to analyze since data cannot be as easily aggregated, compared or statistically tested. Researcher subjectivity also comes more into play during qualitative analysis and interpretation.
Most capstone projects will incorporate both quantitative and qualitative methods to take advantage of their respective strengths and to gain a more complete perspective on the topic under study. For example, a quantitative survey may be administered to gather statistics followed by interviews to provide context and explanation behind the numbers. Or observational data coded numerically may be augmented with field notes to add descriptive detail. The quantitative and qualitative data are then integrated during analysis and discussion to draw meaningful conclusions.
Incorporating both types of complementary data helps offset the weaknesses inherent when using only one approach and provides methodological triangulation. This mixed methods approach is considered ideal for capstone projects as it presents a more robust and complete understanding of the research problem or program/product evaluation compared to what a single quantitative or qualitative method could achieve alone given the limitations of each. Both quantitative and qualitative data have important and distinct roles to play in capstone research depending on the research questions being addressed.