What Is Qualitative Research? Qualitative vs. Quantitative

What is qualitative research

Qualitative research is an approach used to understand the meanings, experiences, perspectives, and reasons behind a phenomenon. Qualitative data usually takes the form of speech, text, images, or notes rather than the numbers used to run Cronbach's Alpha, EFA, regression, or SEM.

When you conduct qualitative research, you ask questions such as: What are participants experiencing, how do they explain the phenomenon, why do they choose a particular behaviour, and which factors appear in a specific context. The result is usually a set of themes, groups of meanings, or a detailed description of the phenomenon being studied.

For example, if your topic examines why students use e-wallets, qualitative research may focus on perceived convenience, security concerns, peer influence, and payment habits. You read the responses, identify repeated ideas, and explain the relationships among those ideas.

Qualitative research does not have a fixed pass threshold expressed as a number. You cannot look at an output table and conclude that a theme reaches 0.7 or that a data code reaches 0.5. Research quality depends on the research question, the suitability of the data, the analysis process, and how clearly you explain the findings.

If you are building an overall picture of scientific research, first decide whether you need to understand a phenomenon in depth or measure relationships among variables. That decision directly affects your questionnaire, sample size, data type, and the analysis section of your thesis.

The role of qualitative research in a quantitative thesis

In a quantitative thesis, qualitative work usually appears before you distribute the questionnaire. Its purpose is to help you understand the research context, identify the language participants use, and check whether your proposed variables fit the real situation.

For example, the literature may suggest the variable “perceived ease of use”, but students in your context may describe it through ideas such as easy registration, few steps to complete a task, or easy problem resolution when an error occurs. This information helps you adjust the wording of your indicators before creating the final questionnaire.

Qualitative work can also help you develop the research model and hypotheses. If several data sources show that users care about employee response speed, you may consider adding support quality to the model. An idea appearing in qualitative data does not automatically prove that the variable has a statistically significant effect in the population.

You need to keep this boundary clear when writing your thesis. Qualitative research helps you explore and explain. Quantitative research helps you measure, test hypotheses, and estimate relationships using numerical data. If your main objective is to test hypotheses H1, H2, and H3 with a questionnaire and a .sav or .csv file, read what is quantitative research before choosing the procedure.

You also need to distinguish a research method from a data collection technique. An open-ended questionnaire produces text data, but the presence of open-ended items alone does not turn the entire study into qualitative research. Conversely, qualitative research can use several types of data, while its core remains the analysis of meaning and context.

What counts as adequate qualitative research

Qualitative research has no common threshold table like Cronbach's Alpha, KMO, or p-value. You should not state that a study must include a particular number of participants before it is acceptable without considering the research question, the diversity of the participants, and how much the information repeats.

For a thesis written by a student conducting quantitative research, you can use the table below as a checklist to assess whether the qualitative section serves the correct purpose. These are criteria for checking the logic of the design, not a statistical scoring scale.

If your topic only requires quantitative data analysis, do not add a long qualitative section just to make the thesis look more complete. The committee may ask which question the data answers, who collected it, which process was used to analyse it, and how it relates to the model. You need to answer each point with material contained in your own research file.

How to read qualitative data in an output table

Qualitative research does not have a standard output like SPSS or SmartPLS. When you open SPSS, you will see tables such as Reliability Statistics, KMO and Bartlett's Test, Rotated Component Matrix, or Coefficients. These tables belong to quantitative analysis. A coding table or theme summary is usually created by you in Excel, Word, or text-processing software.

The table below illustrates how responses can be converted into groups of themes. This is illustrative output. The numbers only illustrate the presentation and are not the results of a real study.

Read the table in three layers. First, retain a response or a short excerpt that accurately represents the participant's meaning. Next, assign a descriptive initial code such as “few steps to complete a task” or “security concern”. Then group codes with similar content into a broader theme.

“Frequency” only indicates how many times an idea appears in the illustrative data. It does not mean that the theme definitely has a stronger effect than another theme in the population. If you want to test the strength of an effect, you need to convert the concept into indicators, collect quantitative data, and run an appropriate model.

With an SPSS file, you can use qualitative data to review variable names and questionnaire content before entering the data. With SmartPLS, the final data still needs a clear column structure, with one row for each respondent and one column for each indicator. Qualitative content does not replace the numerical data file required by SmartPLS.

What to do when the qualitative section is inadequate

When the qualitative section is not convincing, work from the design problem through to the presentation. First, return to the research question. If the question asks you to measure the level of influence, satisfaction, or differences between groups, qualitative data may not be the main type of data that fits the study.

Next, check the data collection instrument. A question as broad as “What do you think about this service?” often produces responses that are difficult to compare. You can write more specific questions about experiences, causes, advantages, barriers, or usage situations. The question should connect to a concept in the model while still allowing respondents to express themselves in their own language.

Then check how you created the codes and themes. If one response is assigned to several groups without a reason, or if a theme name is too broad, such as “other opinions”, the analysis will be difficult to defend. Record the criteria used to place content in each group and apply them consistently.

If the data is not clear enough, you can collect additional information that fits the approved design. Do not change the research question, participants, or collection method after collecting data without updating your supervisor. Changing software settings to make the results look better does not fix a design problem.

In a quantitative thesis, the more acceptable approach is often to use exploratory qualitative findings to adjust the questionnaire and then run a transparent quantitative procedure. State which variables were retained, which were revised, and why. If you remove an indicator after running Cronbach's Alpha or EFA, save each analysis round instead of keeping only the final results table.

Qualitative versus quantitative research

Qualitative and quantitative research first differ in the type of question they answer. Qualitative research fits questions such as “why”, “how”, and “how do participants understand the phenomenon”. Quantitative research fits questions such as “how much”, “is there a relationship”, or “which factors affect the dependent variable”.

You can review the difference quickly in the table below.

A study can combine both approaches, but you must assign a clear role to each part. For example, qualitative work can adjust the wording of indicators, while quantitative research uses a questionnaire to test the model on a larger sample. Calling a study “mixed” only because the questionnaire contains one open-ended item is not enough.

You should also distinguish qualitative research from “qualitative data”. Data is what you collect. A method is how you ask questions, select data, process it, and build an argument. A few text responses can appear in a quantitative study without changing the study's primary method.

If you have not finalised the participants, research variables, and scope, you can also read the guidance on research subjects and research fields. These sections help you avoid choosing a method that is too broad for a topic whose scope is still unclear.

Common mistakes

The first mistake is using “qualitative” as a general label for every type of data that is not numerical. Describe where the data came from, how it was organised, and which question it was used to answer.

The second mistake is writing a qualitative conclusion as a causal conclusion. The statement “participants often mentioned convenience” is completely different from “convenience has a positive effect on usage intention”. The second statement requires an appropriate quantitative model and test.

The third mistake is mixing the procedures of the two methods. You cannot use a p-value to decide whether a qualitative theme passes or fails. You should also not use the frequency of an idea as the only evidence for a relationship between two variables.

The fourth mistake is failing to preserve an audit trail of data processing. Keep the original responses, the initial coding table, the theme grouping table, and the questionnaire version after adjustment. When your supervisor asks why a variable was included in the model, you need to open the documents that show how that decision was made.

The final mistake is choosing a method because it sounds academic when the research question actually requires measurement. If your topic requires hypothesis testing with SPSS or SmartPLS, focus on cleaning the numerical data, checking the scale, and reporting the correct output. You can read more about what scientific research is to review the general structure of a study before writing the methods chapter.

Frequently asked questions

Is qualitative research research without numbers

Qualitative research mainly analyses meaning and content rather than using statistical tests. You can still count how many times a theme appears to describe the data. That number is supporting information and does not automatically turn the study into quantitative research.

Does qualitative research require SPSS

A purely qualitative study usually does not require SPSS. If your topic uses a questionnaire, scales, and hypotheses that need to be tested, SPSS can be used for the quantitative section. Describe the two sections separately so readers know which output table answers which objective.

Can qualitative and quantitative research be used in the same thesis

Yes, if each approach has a clear role. A common design uses exploratory information to adjust concepts and indicators, then uses questionnaire data to test the model. You need to agree on the scope, schedule, and reporting approach with your supervisor.

Does qualitative research require a fixed sample size

There is no single number that applies to every qualitative study. Suitability depends on the research question, the participant group, and the richness of the data. If your work is a quantitative thesis, the survey sample size needs to be determined according to the relevant quantitative design rather than by replacing it with a qualitative rule.

Is an open-ended question in a questionnaire qualitative research

An open-ended question produces text data, but the entire study is not necessarily qualitative. If the main part of your study still consists of Likert variables, scale analysis, and hypothesis testing, it can remain quantitative with an additional open-ended question. State clearly whether the open-ended question is used for reference or analysed as a primary data source.

Open your proposal and data file now. Write one sentence stating whether your main objective is exploration or testing, then check each variable and analysis table against that objective. If you need to run the analysis on your own .sav or .csv file, see M4 analysis by DoThesis to complete this step with control over the process.