Mixed effects · Hierarchical linear models · Growth curves
50 multilevel modelling dissertation topics across students in schools, longitudinal cohorts, daily diaries, random slopes, communities, countries, hospitals and three-level designs.
Multilevel modelling is designed for nested, longitudinal or otherwise clustered observations. Patients can be grouped in hospitals, pupils in schools and repeated daily reports within individuals. Choosing the right level structure is essential for meaningful coefficients and uncertainty estimates.
These 50 topics pair each research question with a study design, statistical model, starting data source and related university thesis. Review the data-feasibility notes wherever identifiers, timing, scales or administrative variables may be missing.
Which multilevel model fits your data?
Start with a diagram of which observations belong to which groups. The need to address clustering does not depend on a universal ICC threshold, and random slopes should be supported by the number and variability of groups.
Data feasibility: TIMSS links students with teachers and schools, but teacher confidence items and mathematics scores must be present in the same survey cycle; use the correct complex-survey design.
Data feasibility: PISA school-location variables and mathematics self-efficacy scales vary by year and country. Check whether the proposed item set is comparable.
Data feasibility: Teacher support and self-concept are often both student-reported. Separate within-school and between-school components and consider shared-method bias.
Data feasibility: PISA provides plausible values, sampling weights and replicate weights. An ordinary unweighted empty model can misstate the school-level ICC.
Data feasibility: Principal leadership measures in TIMSS may refer to specific school climates rather than the precise construct proposed. Verify questionnaire and aggregation.
Data feasibility: Club participation, school belonging and depression measures vary by Add Health wave; confirm temporal ordering before a growth model.
Data feasibility: A spline at age 75 needs sufficient within-person observation around 75; separating ageing from cohort effects takes careful model design.
Data feasibility: Test–retest reliability should distinguish real cognitive change from measurement error; repeated HRS waves are years apart, not short retests.
Data feasibility: Understanding Society may measure commuting behaviour with changing questionnaire coverage; moving house can alter survey eligibility and available fields.
Data feasibility: Job loss timing should be tied to observed waves; sparse annual surveys may make an immediate event-time change difficult to identify.
Data feasibility: Verify whether the MIDUS/NSDE diary records volunteering separately from general help or social support; match daily affect and event measurements.
Data feasibility: Lagged daily mood models need at least consecutive daily observations and must separate within-person fluctuations from stable person differences.
Data feasibility: The country identifiers, teacher leadership scale and teacher confidence scale must be available together; country-level random slopes need enough countries.
Data feasibility: Person-specific slopes for income over time require multiple within-person income changes. Random slope models can be unstable with sparse waves.
Data feasibility: Check whether MIDUS measures the proposed impulsivity trait as well as day-level stress and mood. Without it the cross-level interaction is infeasible.
Data feasibility: Gender interactions in random stress-buffering slopes require enough diary days and participants within groups, and clear within-person centring.
Data feasibility: DHS dietary measures for children are not available in every country-year. Community wealth proxies based on individual survey responses can be compositionally biased.
Data feasibility: Neighbourhood social cohesion and detailed residential geography may be unavailable in RAND HRS public-use files; restricted data may be required.
Data feasibility: County Health Rankings alone may not contain comparable county homelessness and house-price or rent histories; merge HUD and housing data.
Data feasibility: County-level walkability may need to be merged from a separate built-environment source; CHR alone is not a comprehensive walkability panel.
Data feasibility: FEMA disaster declarations and dates must be obtained separately; County Health Rankings contains the county health outcome series, not disaster exposure.
Data feasibility: County Health Rankings may not provide acute-myocardial-infarction-specific deaths, and CDC social-vulnerability indicators may need merging.
Data feasibility: Check annual county primary-care-rate definitions and Appalachian county classifications, including structural changes in source reporting.
Data feasibility: County Health Rankings is not a complete COVID-19 county death surveillance source; merge CDC or another vetted public health series.
Data feasibility: Diabetes and hypertension outcomes may be modelled estimates rather than direct county observations. Use a valid adjacency matrix for spatial correlation.
Data feasibility: World Values Survey covers individual happiness and income, but GDP per capita is a separate country-year series; add World Bank or comparable data.
Data feasibility: Multilevel measurement-invariance analysis needs an item battery with adequate common item wording across the selected countries and sufficient between-country samples.
Data feasibility: PISA does not directly provide a harmonised country education-expenditure series; merge suitable OECD indicators and account for PISA plausible values.
Data feasibility: A methods simulation based on eICU data should explicitly vary the number of clusters and compare small-sample corrections with a known generating truth.
Data feasibility: Length of stay is non-negative, often skewed and affected by discharge/death; compare suitable GLMM or survival formulations before interpreting group variance.
Data feasibility: PISA bullying items and school IDs may support an ICC, but trial design effects require assumptions about future sampling, cluster sizes and outcome scales.
Data feasibility: PISA item-level assessment data may be incomplete for custom multilevel IRT fitting. Check the released cognitive-item response files and design features.
Data feasibility: TIMSS/PIRLS are not ordinary longitudinal pupil–teacher records across class changes. Teacher-effect persistence requires linked pupils and teachers across years.
Data feasibility: Understanding Society’s household composition changes over waves; a three-level structure needs a clear treatment of people moving households.
Data feasibility: TIMSS/PIRLS may have class or teacher identifiers, but verify that they support a distinct classroom level beyond the school level in chosen cycles.
Data feasibility: County-year measures are not independent and state grouping may be insufficient by itself. Compare state, county and time random/fixed effects.
Data feasibility: Three-level measurement-burst models need repeat bursts with stable person IDs and comparable diary items in the selected MIDUS/NSDE cohorts.
The listed sources provide education, health, household, daily-diary and cross-national data. Verify grouping IDs, weights, time periods and access restrictions: a dataset with repeated observations is not automatically suitable for every three-level or cross-classified model.
Over 200,000 ICU stays from 208 US hospitals with outcomes and severity scores.
Printable multilevel modelling dissertation guide
Get all 50 topics in one PDF
A printable guide with the 50 research questions, study designs, multilevel models, data sources and related thesis references will be linked here after the branded PDF is created and uploaded.
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How to develop a multilevel modelling dissertation
Draw the data hierarchy. Clarify whether repeated observations are nested in people, patients in hospitals, or pupils in schools. Some designs are cross-classified.
Start with an appropriate baseline model. Estimate variation between units and an intraclass correlation where meaningful, while accounting for survey design.
Centre predictors deliberately. Distinguish within-cluster and between-cluster associations when that distinction matters.
Add random slopes selectively. Assess model identification, convergence and uncertainty; likelihood-ratio testing of variance components may require special treatment at boundary values.
Check your group count. No single minimum fits every design. With few clusters, examine small-sample corrections and robustness.
Bring your shortlisted topics, clustering structure and possible data sources to a free 15-minute consultation about research design and modelling choices.
Multilevel modelling is regression for grouped data. It handles pupils in schools, patients in hospitals, people in countries and repeated measures in people.
When should I use a multilevel model instead of regression?
Use it when your data are nested and the ICC is above about 0.05. Ordinary regression then gives standard errors that are too small.
How many groups do I need for a multilevel model?
Aim for at least 30 groups for school or hospital models and 20 to 25 countries for cross-national models. Fewer groups need small-sample corrections.
What is the difference between a random intercept and a random slope?
A random intercept lets each group have its own average. A random slope lets the effect of a predictor change from group to group.
Which software is best for multilevel modelling?
R with lme4 is free and flexible. Stata, HLM and MLwiN are also common. SPSS MIXED handles simple two-level models.
Is there a multilevel modelling topics PDF?
Yes. Download the full Topic and Model list from this page. It includes every topic, model and dataset link.
