What Is a Thesis Proposal? Structure and Writing Guide

What is a thesis proposal?

You can think of a thesis proposal as the blueprint for a research project. Before you collect questionnaire responses, open your SPSS file, or write the results chapter, you need to state what you plan to study, why it is worth studying, what data you will use, and how you will analyse it. The proposal lets your supervisor see the complete direction of the project before you invest time in the next steps.

For an undergraduate thesis, a proposal is usually shorter than the completed thesis, but it still needs enough information for someone to assess whether the project is feasible. A good proposal answers these questions: What will you study, what objectives must you achieve, who will be surveyed, which variables will be measured, where will the data come from, which analysis methods will be used, and when will each part be completed?

A proposal is not a few-line summary of the topic. It is not a copy of the table of contents either. For quantitative research, the proposal should show the connection between the research model, hypotheses, scales, questionnaire, sample size, and analysis plan in SPSS or SmartPLS. If you are starting with a topic title, you can also review the undergraduate thesis topic list to check the scope and whether data collection is realistic.

The required length depends on your department. A brief proposal may be only a few pages, while a detailed research proposal is longer because it includes the literature review, model, scales, and methods. Prioritise clarity instead of adding unrelated theory simply to increase the page count.

Standard structure of a thesis proposal

Your university or department may use slightly different names for these sections. A quantitative research proposal usually contains the groups of content below. The page counts are only reference ranges, so check the template provided by your supervisor.

Your topic title should be narrow enough for you to collect data within the available time. For example, “Factors influencing students’ intention to use e-wallets in Ho Chi Minh City” identifies the survey group, context, and outcome variable. It is easier to turn this title into objectives, hypotheses, and a questionnaire than a broad title such as “A study of e-wallets”.

The rationale should move from the practical problem to the research problem. You can describe the context, how the problem appears, the group affected, and why it should be tested with data. If you use external statistics, record the source while drafting, then standardise it using the guidance on how to write references and how to cite references.

The methods section needs to be specific enough for the reader to know what you will do with the data file. For example, you might state that the study uses a 5-point Likert questionnaire, surveys students who have used e-wallets, cleans the data, and runs Cronbach's Alpha, EFA, and multiple linear regression. If you use PLS-SEM, state clearly that you will assess the measurement model before assessing the structural model.

Steps for writing a thesis proposal

Step 1: Finalise the problem and topic title

Start with a problem that can be observed and measured. You need to know who belongs to the survey population, which outcome you want to explain, and what the research context is. A quantitative topic usually has independent variables and a dependent variable, and it may also include a mediator or moderator. Avoid placing too many concepts in the title before you have suitable scales and data.

Check three things before finalising the title. Can you reach the respondents? Can you measure the variables with a questionnaire? Is there enough time left to clean the data, run the analysis, and write the chapters? If the answer to any of these is no, narrow the scope instead of keeping a large-sounding title that you cannot carry out.

Step 2: Write the rationale and research gap

The rationale should follow a clear structure. The opening paragraph introduces the context. The next paragraph describes the specific problem. You then explain what previous studies have covered and what remains missing in your context, population, or model. Finally, connect that gap to your research objective.

A research gap does not mean that nobody has ever studied the topic. You may identify a gap in the Vietnamese context, a particular student group, a specific service industry, a relationship that has not been tested, or findings that have not been consistent across earlier studies. Every statement about previous research needs a source, so do not write it from memory.

Step 3: Turn the problem into objectives and questions

The general objective describes the largest result the project aims to achieve. Specific objectives must be testable with data. For quantitative research, verbs such as “measure”, “assess”, “test”, and “identify” are usually more suitable than broad verbs such as “explore”.

For example, the general objective could be to assess the factors influencing students’ intention to use e-wallets in Ho Chi Minh City. Specific objectives could include identifying the factors in the model, testing the effect of each factor, comparing the strength of the effects, and proposing managerial implications based on the results.

Each research question should correspond to one objective. If the objective is to test the effect of service quality on usage intention, the question should ask whether service quality affects intention and in which direction. Create a table matching objectives, questions, hypotheses, and methods so that you can find missing parts before submission.

Step 4: Build the model and hypotheses

The research model shows how the variables are related. Define each variable, explain its theoretical basis, and state why you have drawn an arrow between the variables. If your topic uses TAM, TPB, or UTAUT, read the original model source and studies that adapted it to a similar context. For example, TAM connects perceived usefulness and perceived ease of use according to (Davis, 1989), while TPB is presented in (Ajzen, 1991).

A hypothesis should be written as a sentence with a clear direction of relationship. For example: “H1: Perceived usefulness has a positive effect on students’ intention to use e-wallets.” Do not present only an arrow diagram without explaining why the relationship is proposed.

At this stage, consider the analysis software as well. A model with observed variables and simple regression relationships can be handled in SPSS. A model with several latent variables, mediators, or complex relationships may suit SmartPLS, depending on the design and your department’s requirements. Do not place CFI, TLI, or RMSEA in a SmartPLS assessment section because these indices belong to CB-SEM.

Step 5: Select the scales and design the questionnaire

Each variable in the model needs to be measured with observed variables that have sources. You can adopt a scale from earlier research, translate it, and adjust the wording for your context. A reference translation does not automatically become a valid scale. You still need to check the content, how respondents understand the items, and the test results from your actual data.

The questionnaire should include an introduction, screening questions, demographic information, and groups of observed variables. Record codes such as PU1, PU2, or BI1 in the data file so that you do not confuse the items when entering them into SPSS. If you have reverse-coded items, mark them during questionnaire design and record the coding procedure.

For an exploratory scale, Cronbach's Alpha from 0.6 may be considered according to (Hair et al., 2010), while an Alpha threshold from 0.7 is commonly cited from (Nunnally, 1978). A Corrected Item-Total Correlation from 0.3 or above is stated in (Nunnally and Bernstein, 1994). These are reference criteria for planning, not a reason to delete items arbitrarily after seeing an unattractive result.

Step 6: Write the methods, sample size, and timeline

State clearly whether the research uses primary or secondary data. If you use a questionnaire, specify the respondents, access method, collection period, Likert scale, and criteria for excluding responses. For the number of observed variables, the rule of 5 to 10 observations per item is cited from (Hair et al., 2010). For regression, the minimum sample-size formula of n at or above 50 + 8m is stated in (Tabachnick and Fidell, 2013), where m is the number of independent variables.

The timeline should be linked to concrete outputs. For example, week one finalises the model and scales, week two completes the questionnaire, week three collects data, week four cleans the .sav or .csv file, week five runs the analysis, and week six writes the results chapter. If time is limited, divide the work into smaller milestones instead of writing one general line saying “complete the thesis”.

Step 7: Check consistency before submission

Read the proposal vertically. The topic title must match the objectives. The objectives must match the questions. The questions must match the hypotheses. The hypotheses must match the model, observed variables, and analysis method.

Then check it horizontally. Does every variable in the model have a definition and a source for its scale? Is there data to answer every objective? Does each method produce the result needed for the objective? Finally, check the citations, formatting, and file name against your department’s template.

Usable sentence templates

The sentences below are frameworks that you can adapt to your topic. Do not keep them unchanged if your context, population, or variables are different.

How to write this in your thesis: “This study is conducted to test the effect of [independent variables] on [dependent variable] among [population] in [location]. Data will be collected using a questionnaire and analysed with [software] to test hypotheses H1 to H[n].” You can use this paragraph as a framework for the methods section, then replace every placeholder with actual information.

Keep author names and years consistent when citing sources in the proposal. In-text citations must match the reference list at the end. If you are unsure whether a source exists or have not checked its publication details, temporarily remove the claim instead of adding a citation that you cannot defend before the committee.

Short proposal example

Suppose you choose the topic “Factors influencing students’ intention to use digital banking applications in Hanoi”. The research problem is that many students know about digital banking, but their willingness to use it may differ. The proposed model includes perceived usefulness, perceived ease of use, trust, and usage intention.

The rationale should explain why digital banking is worth studying among students, then identify the specific model and context you want to test. The general objective is to assess the effects of the factors on usage intention. Specific objectives are to identify the factors, measure the strength of their effects, and propose implications based on the results.

The questionnaire may include the variable groups PU, PEU, TR, and BI, with several observed variables in each group adopted and adjusted from earlier research. The survey population consists of students in Hanoi who know about or have encountered digital banking applications. After collection, check missing data and invalid responses, then run Cronbach's Alpha, EFA, and regression if the model suits SPSS. In the proposal, state this as an analysis plan. Do not present Alpha, KMO, p-value, or R² in advance when you do not yet have data.

A short timeline may include finalising the model and scales, designing the questionnaire, collecting data, cleaning the data, analysing it, writing the results, and revising the thesis. Each milestone should have an expected date and a person responsible if the project is completed as a group.

Common problems that make the committee ask follow-up questions

The first problem is a broad topic title paired with a narrow method. You may write that the study concerns consumers in Vietnam but survey only students at one university. In that case, narrow the population or explain the scope clearly so that the title does not create expectations greater than the data can support.

The second problem is using phrases such as “comprehensively explore” in the objectives without any measurable variables or indicators. The committee will ask which data support such a comprehensive conclusion. Change the objective into a testable action such as identifying, measuring, comparing, or testing.

The third problem is a mismatch between the model and questionnaire. The diagram may contain the variable “trust”, but the questionnaire has no group of observed variables for trust. You may also have variables in the questionnaire that do not appear in the objectives or hypotheses. Prepare a list of variable codes before sending the proposal.

The fourth problem is presenting statistical thresholds as absolute rules without a source. When you state Alpha, KMO, factor loading, or sample-size thresholds, include the appropriate source and make clear that the criteria are planned. Do not delete an item because of a mechanical threshold if deletion changes the content of the construct.

The fifth problem is a thin reference list that does not match the in-text citations. Save each source as you read it, including the authors, year, title, and publication details. Do not wait until the end of the semester to check every link again.

The final problem is placing assumed results in the proposal. You can state the analysis method and assessment criteria, but you cannot write that a hypothesis will certainly be accepted, that the model will have a particular R², or that the data will meet the criteria. Those numbers appear only after you run the actual file.

Frequently asked questions

What is a thesis proposal, and is it compulsory to submit one?

A thesis proposal is an advance presentation of the research problem, objectives, model, methods, and project plan. Many programmes require students to submit or defend a proposal so that the supervisor can provide feedback before data collection begins. Check your department’s rules because the format and deadline may differ.

How many pages should a research proposal have?

There is no single length that applies to every university. The proposal needs enough detail for the reader to assess feasibility, but it does not need unrelated theory simply to become longer. Prioritise the topic title, rationale, objectives, model, methods, timeline, and references in the format required by your department.

Does a thesis proposal need research hypotheses?

If your quantitative study tests relationships between variables, you should write hypotheses. Each hypothesis needs an independent variable, a dependent variable, and a direction of relationship when the theory supports one. A purely descriptive study may not require hypotheses, but it still needs clear questions and analysis indicators.

Should you write the results section before collecting data?

You should write only the analysis plan, such as whether you will assess reliability, run EFA, use regression, or apply PLS-SEM based on the model. Do not fill in p-value, R², Alpha, or a conclusion about hypothesis acceptance. Results must come from the actual data file after collection and cleaning.

What should you do if your supervisor asks for many revisions?

Group the feedback into topic and scope, rationale and gap, model and hypotheses, scales, methods, and formatting. Revise the foundational sections first because changing the population or model may require changes to the questionnaire, sample size, and analysis plan. When you resubmit, mark the revised points so that your supervisor can check them more quickly.

Open your current proposal and check each item in the sequence of topic title, objectives, model, scales, methods, and timeline. If you need to build a quantitative model, questionnaire, and scales for your topic, you can use M3 for the model, hypotheses, scales, and questionnaire.