What Is a Research Field and How to Define It Correctly
What Is a Research Field
A research field is the body of knowledge or group of problems that you want to study systematically. It is usually connected to a discipline, a specific context, and a particular type of phenomenon, such as online shopping behavior, technology acceptance, banking service quality, or employees' turnover intention.
You should distinguish among three levels of concepts. A research field is the broad scope. A research topic is the specific problem you choose within that scope. A research question is what you need to answer with data. For example, “marketing” is a broad field, “factors affecting students' intention to buy cosmetics through TikTok Shop in Ho Chi Minh City” is a research topic, and “does information quality affect purchase intention?” is a research question.
In quantitative research, a field is usually made specific through independent variables, dependent variables, mediators, or moderators. You can read more about the basic concept in what is scientific research and what is scientific research before finalizing your topic.
You can describe a research field using the following practical formula:
Research field = broad discipline or topic + phenomenon to explain + context + target group.
For example: “business administration + intention to continue using e-wallets + users in Vietnam + young working adults”. This is not a statistical formula. It is a quick way to check whether your idea is clear enough to become a research topic and questionnaire.
Why the Research Field Matters in Quantitative Research
When you open your data file and see columns such as SAT1, SAT2, USE1, and USE2, the research field helps you understand which problem those variables are meant to address. If you do not define the scope at the beginning, you can easily combine scales that do not follow the same logic, select the wrong respondents, or run a model with many variables that still does not answer your research question.
First, the research field limits the content of the study. “E-commerce” can include purchasing behavior, trust, logistics, user experience, payment, and seller management. A student thesis usually cannot handle all of these directions. You need to choose a phenomenon that can be measured with a questionnaire and for which you can collect data within a realistic timeframe.
Next, the research field guides your choice of respondents. If you study intention to use an online learning application, you need to specify whether respondents are students who currently use it, have used it before, or only know about it. The section on research subjects helps you separate the research subject from the study area and survey sample.
The field also affects your research model. Within the same topic of “banking services”, you could study the effect of service quality on satisfaction, the effect of trust on continuance intention, or the effect of digital experience on loyalty. Each choice leads to different hypotheses, scales, sample-size requirements, and analysis procedures.
Finally, the research field helps the committee understand why your topic has a reasonable scope. You need to answer four questions: what phenomenon is being studied, who are the respondents, what context is being considered, and what data can be collected. A topic may sound broad and current but still carry a high risk if you cannot obtain suitable data.
How Broad Should a Research Field Be
There is no fixed number that determines the appropriate breadth of a research field. A field is usable when it helps you identify the variables, respondents, context, and method. In a quantitative study, the clearer the scope, the less likely you are to lose direction while designing the questionnaire and testing the model.
Use the following table as a self-check. The statistical thresholds apply only after you have turned the field into a specific model and measurement scale.
| Item to check | Reference level or decision question | Source |
|---|---|---|
| Observations per item | About 5 to 10 observations for each item | (Hair et al., 2010) |
| Regression sample size | At least 50 + 8m, where m is the number of independent variables | (Tabachnick and Fidell, 2013) |
| Cronbach's Alpha | Usually accepted from 0.7; an exploratory scale may be considered from 0.6 | (Nunnally, 1978); (Hair et al., 2010) |
| Corrected Item-Total Correlation | From 0.3 | (Nunnally and Bernstein, 1994) |
| KMO when running EFA | From 0.5; Bartlett's Test has Sig. below 0.05 | (Kaiser, 1974) |
| Factor loading | From 0.5 | (Hair et al., 2010) |
| Total Variance Explained | From 50% | (Hair et al., 2010) |
| Outer loading in PLS-SEM | From 0.7 as a reference point | (Chin, 1998); (Hair et al., 2022) |
These numbers do not automatically make a field acceptable. They help you assess feasibility after you have data. For example, if you have 30 items and expect to collect 80 responses, the observation-to-item ratio may be too low for your EFA plan. At that point, review the scope, questionnaire, or data-collection capacity before running SPSS.
You also need to distinguish between a field that is sufficiently narrow and one that has been cut down too far. If you survey only a very small group in a location that is difficult to access, the results may not provide enough data for analysis. Choose a scope that gives you real access to respondents instead of putting a broad area in the title without a clear sampling plan.
How to Read the Research Field in the Output
The research field usually does not appear word for word in an SPSS output table. You read it indirectly through the variable names, scales, factor groups, and regression model. If the topic concerns purchase intention, for example, the variable groups might be information quality, trust, perceived value, and purchase intention.
Check the output in this order: whether the data come from the correct respondents, whether the items group into the intended concepts, and whether the independent variables explain the dependent variable. The table below is illustrative output, not the result of a real study.
| SPSS table name | Statistic or variable | Illustrative value | How to read it within the topic scope |
|---|---|---|---|
| Reliability Statistics | Cronbach's Alpha, Information Quality | 0.812 | The items for the concept show good internal consistency against the reference point |
| Item-Total Statistics | Corrected Item-Total Correlation, IN4 | 0.468 | IN4 correlates with the total scale above the 0.3 reference point |
| KMO and Bartlett's Test | KMO | 0.781 | The data are suitable for considering EFA |
| KMO and Bartlett's Test | Bartlett's Test Sig. | 0.000 | The correlation matrix differs from an identity matrix at the selected test level |
| Rotated Component Matrix | Factor loading, IN2 | 0.724 | IN2 loads on the expected factor at an illustrative level above 0.5 |
| Total Variance Explained | Cumulative % | 61.430 | The extracted factors explain an illustrative cumulative variance above 50% |
| Coefficients | Beta, Information Quality → Purchase Intention | 0.287 | The illustrative model shows a positive coefficient, but you must also read Sig. |
| Coefficients | Sig. | 0.004 | If the selected test threshold is 0.05, the illustrative relationship is statistically significant |
Do not look at one value in isolation and then conclude that the field is suitable. If Alpha meets the reference point but the items split into several factors that conflict with the theoretical model, check the scale content and coding. If the regression coefficient is positive but Sig. does not meet the selected threshold, you cannot write that the hypothesis is supported.
In the SPSS file, compare the variable labels with the data codebook. IN1, IN2, and IN3 must describe the same concept according to the questionnaire. A column entered with the wrong scale, an item that should have been reverse-coded but was not recoded, or incorrectly handled missing data can all make the output diverge from the field you intended to study.
What to Do When the Research Field Does Not Fit
If the field feels too broad, work from the scope toward the data instead of immediately deleting items from the output.
Narrow the Phenomenon You Need to Explain
Write one sentence using this pattern: “This study examines the factors affecting [dependent variable] among [target group] in [context].” If you cannot fill in a specific dependent variable, the topic is still too broad. “Digital marketing research” needs to become a measurable phenomenon, such as purchase intention or satisfaction.
Set the Target Group and Study Area
State clearly who can answer the questionnaire and what conditions qualify them for the sample. “Vietnamese consumers” is often too broad for an individual thesis. You can narrow the group by age, product-use status, region, or type of organization, provided that the restriction fits the research question and your ability to collect data.
Reduce the Model According to the Hypotheses
Each independent variable needs a theoretical reason and a corresponding hypothesis. If you have eight variables but cannot explain the relationship involving two of them, consider removing them from the model before designing the questionnaire. When the field concerns a causal model, you can refer to what is quantitative research to distinguish measured variables from the conclusions that must be tested.
Check the Scales Before Running the Analysis Again
In SPSS, run Analyze > Scale > Reliability Analysis to check each scale group. If an item has a low Corrected Item-Total Correlation, review the item wording, coding, and missing data. Removing an item only because it increases Alpha is a decision that needs to be explained, not a default operation.
Collect More Data Only When the Problem Is Sample Size
Collecting more responses can increase the number of observations, but it cannot fix a scale that measures the wrong concept, an unsuitable respondent group, or an incorrectly entered variable. If the field is still unclear, more data will only give you a larger file that still does not answer the research question.
Distinguishing a Research Field from a Research Topic
The research field is the broad frame, while the topic is the specific choice within that frame. “Research on human resources” only identifies the field. “Factors affecting turnover intention among retail employees in Hanoi” is closer to a research topic because it includes the phenomenon, respondents, and context.
You also need to distinguish the field from the method. “Qualitative research” and “quantitative research” describe how data are collected, analyzed, and interpreted. They are not content fields. If you want to understand the contrasting term, you can read what is qualitative research, but for a quantitative thesis, your data need to be organized into variables, scales, and suitable analysis tables.
The field is also different from the research subject. “Final-year students” is a respondent group, not a field. “Intention to use online learning platforms among final-year students” expresses a research phenomenon connected to a specific group.
Finally, a field is not the same as the name of your degree program. You may study business administration but choose consumer behavior, human resource management, or information systems as your field. The key question is whether the research question, data, and method match the scope you claim.
Common Mistakes
The first mistake is choosing a field because its name sounds broad and current, then trying to find variables afterward. This produces a scattered literature review and a questionnaire full of items that do not serve the hypotheses. Start with a phenomenon that can be observed and measured.
The second mistake is using the field as a substitute for the topic. “Research field: marketing” does not provide enough information for the methods chapter. The reader still does not know whom you are studying, where the study takes place, what the dependent variable is, or how the data will be collected.
The third mistake is changing the field after running many tests without updating the model, hypotheses, and questionnaire. If you remove a variable or change the respondent group, check the topic title, research questions, model diagram, and sample description again.
The fourth mistake is treating every statistical threshold as an absolute rule. Alpha, KMO, factor loading, and R² must be interpreted in the context of the scale, sample size, research purpose, and cited sources. Do not delete a large number of items merely to make the results table look cleaner.
The final mistake is mixing several fields in one model. Service quality, employee behavior, technology, and financial performance may be connected in practice, but a student thesis needs a compact research logic that you can defend. State which variable explains which other variable before opening the Analyze window in SPSS.
Frequently asked questions
What is a research field in a thesis?
It is the scope of the topic and phenomenon that the thesis focuses on explaining. In a quantitative thesis, the field needs to be made specific through the respondents, context, variables, scales, and tested model.
Is a research field the same as a degree program?
No. Your degree program is your educational background, while the research field is the problem area you choose for collecting and analyzing data. A business administration student may study marketing, human resources, organizational behavior, or information systems.
How do I know whether my research field is too broad?
If you have not identified the dependent variable, respondent group, study area, or data source, the field may be too broad. Try writing the research objective in one sentence and listing the main hypotheses. If the sentence is too general or contains too many different directions, narrow the field before designing the questionnaire.
Does a research field need a citation?
The name of a field usually does not require a separate citation. However, the conceptual definitions, theoretical model, measurement scales, and analysis thresholds in your topic need appropriate sources. Numbers such as Alpha, KMO, or factor loading should appear with their sources in the methods and results chapters.
Can I change the research field after collecting data?
You can adjust the wording of the scope if the actual data show that the original label was inaccurate. Changing to a different phenomenon after data collection is a major risk because the questionnaire, hypotheses, and sample may no longer fit. Discuss the change with your supervisor before changing the dependent variable, respondents, or context.
How are qualitative and quantitative research fields different?
The main differences concern the research question, type of data, and analysis method. Quantitative research usually tests relationships among variables with numerical data, questionnaires, and software such as SPSS or SmartPLS. The same broad field can support different research questions and designs.
Open your thesis file and rewrite the field using four components: phenomenon, respondents, context, and key variables. Then compare it with the questionnaire and the columns in .sav or .csv, and use M4 data analysis to run the correct steps on your own dataset.