How to Write a Research Hypothesis?

SAER Journal · Academic Writing Guide

A research hypothesis states an expected relationship, difference, or effect in a testable form, particularly in quantitative research. A well-written hypothesis is derived from the research problem, theoretical framework, and existing literature.

A hypothesis does not mean assuming the result to be true in advance. It expresses an expectation that can be tested and may or may not be supported.

What Is a Hypothesis?

Research question: Is academic self-efficacy associated with academic procrastination among university students?

Hypothesis: Higher academic self-efficacy is associated with lower academic procrastination among university students.

Research Question vs Hypothesis

Question: Is X significantly associated with Y?

Hypothesis: X is positively and significantly associated with Y.

Characteristics of a Strong Hypothesis

  • Directly linked to the research problem
  • Grounded in literature or theory
  • Clear and specific
  • Uses measurable variables
  • Scientifically testable
  • Compatible with the study design
  • Directional where justified

Where Should Hypotheses Come From?

  • Previous empirical studies
  • Theoretical models
  • Systematic reviews and meta-analyses
  • Consistent trends in the literature
  • Conflicting prior findings
  • Plausible scientific mechanisms

A Simple Writing Formula

X variable + expected relationship/effect + Y variable

Directional and Non-Directional Hypotheses

Positive: As perceived social support increases, life satisfaction increases.

Negative: As workload increases, job satisfaction decreases.

Non-directional: Men and women differ significantly in academic self-efficacy scores.

Null and Alternative Hypotheses

H0: There is no significant relationship between X and Y.

H1: There is a significant relationship between X and Y.

Relationship, Difference, and Prediction Hypotheses

Relationship: Social support is positively associated with life satisfaction.

Difference: Shift-working nurses differ significantly from daytime nurses in burnout levels.

Prediction: Digital leadership positively predicts employees' attitudes toward digital transformation.

Effect Hypotheses

Use causal language only when the design supports causal inference. Example: Participants receiving an eight-week exercise programme have lower pain scores than those in the control group.

Mediation Hypotheses

H1: Digital leadership positively affects digital transformation.

H2: Digital leadership negatively affects AI anxiety.

H3: AI anxiety negatively affects digital transformation.

H4: AI anxiety mediates the relationship between digital leadership and digital transformation.

Moderation Hypotheses

General: Perceived organisational support moderates the relationship between workload and burnout.

Directional: The positive association between workload and burnout is weaker among employees with high perceived organisational support.

Sub-Hypotheses

H1a: Innovative leadership behaviour positively predicts digital transformation.

H1b: Supportive leadership behaviour positively predicts digital transformation.

Do Descriptive Studies Need Hypotheses?

Not always. If the aim is to describe levels, proportions, or distributions, research questions may be more appropriate.

Qualitative and Mixed-Methods Studies

Qualitative studies usually begin with open research questions rather than fixed hypotheses.

In mixed-methods studies, hypotheses may be used for the quantitative component and research questions for the qualitative component.

Cross-Sectional Studies and Causal Language

Cross-sectional and observational designs usually justify statements about association or prediction rather than strong causal claims.

Should “Significant” Appear in a Hypothesis?

It can, when statistical significance is part of the expectation, but hypotheses should not be reduced to p-values alone. Direction, magnitude, and scientific meaning also matter.

Numbering Hypotheses

Use H1, H2, H3 for main hypotheses and H1a, H1b, H1c for sub-hypotheses where needed. Keep the same numbering in Results.

Where Should Hypotheses Appear?

They are usually presented after the relevant literature and theoretical rationale, so readers understand why each expectation is scientifically justified.

Can Hypotheses Be Changed After Analysis?

No. Hypotheses should not be rewritten to fit the observed results. Unsupported hypotheses should still be reported and discussed.

Should We Say a Hypothesis Was “Accepted”?

Prefer cautious wording such as:

  • The hypothesis was supported.
  • The hypothesis was not supported.
  • The findings were consistent with H1.
  • H2 did not receive sufficient support.

Match Hypotheses to Analyses

  • Association → correlation or another appropriate association analysis
  • Group difference → t test, ANOVA, or an appropriate non-parametric alternative
  • Prediction → regression
  • Mediation → mediation analysis or SEM
  • Moderation → interaction/moderation analysis
  • Model hypothesis → appropriate SEM or other modelling approach

Common Mistakes

  • Writing a hypothesis as a question
  • Using directional hypotheses without literature support
  • Including unmeasured variables
  • Making causal claims in cross-sectional designs
  • Combining too many relationships in one hypothesis
  • Creating inconsistent main and sub-hypotheses
  • Adding hypotheses unrelated to the study aim
  • Creating unnecessary hypotheses for every demographic variable
  • Changing hypotheses after analysis
  • Omitting unsupported hypotheses from the report

Practical Templates

Association: X is positively/negatively and significantly associated with Y.

Difference: Groups A and B differ significantly on Y.

Prediction: X positively/negatively predicts Y.

Mediation: M mediates the relationship between X and Y.

Moderation: Z moderates the relationship between X and Y.

Experimental effect: The intervention group differs significantly from the control group on Y.

Before Submission, Check

  • Are hypotheses directly linked to the research problem and aim?
  • Is each hypothesis justified by literature or theory?
  • Are variables explicit?
  • Is the direction scientifically justified?
  • Are hypotheses compatible with the design?
  • Are causal claims within the limits of the design?
  • Does each hypothesis have an appropriate analysis?
  • Are hypotheses unnecessarily repetitive?
  • Is numbering consistent?
  • Will every hypothesis be reported in Results?