Meta-analytic case report

pooled case report analysis
systematic case report review
case report meta-analysis
MACR

Developed iteratively in clinical epidemiology; formalized guidance by Murad et al. (2018)

A meta-analytic case report is a secondary research methodology that systematically identifies, appraises, and quantitatively or qualitatively pools data from multiple published individual case reports on the same clinical phenomenon. It is used most often when randomized trials or cohort data are unavailable — particularly for rare diseases, uncommon drug reactions, or novel presentations — and transforms isolated anecdotal observations into a more robust aggregate picture.

Published individual case reports (structured or free-text)

Murad, M. H., Sultan, S., Haffar, S., & Bazerbachi, F. (2018). Methodological quality and synthesis of case series and case reports. BMJ Evidence-Based Medicine, 23(2), 60–63.

Nissen, T., & Wynn, R. (2014). The clinical case report: a review of its merits and limitations. BMC Research Notes, 7, 264.

case-report systematic-review meta-analysis case-series narrative-review meta-analytic-case-series


Meta-analytic Case Series

Meta-analytic Pooling of Case Series Studies

pooled case series
systematic review of case series
case series meta-analysis
aggregated case series

2000s–2010s (formalization of methodology)

Developed iteratively in clinical epidemiology; methodological guidance formalized by Murad et al. and others in the 2000s–2010s

A meta-analytic case series is an evidence-synthesis design that systematically identifies, appraises, and statistically pools outcome data from multiple single-arm case series on a defined clinical condition or intervention. It occupies a middle tier of evidence — above individual case reports and unsystematic series, but below pooled randomized trials — and is particularly valuable when experimental designs are ethically or practically unavailable.

Evidence synthesis / meta-analytic method

Aggregate or individual patient data from multiple case series

Lovato, L. C., Hill, K., Hertert, S., Hunninghake, D. B., & Probstfield, J. L. (2002). Recruitment for controlled clinical trials: literature summary and annotated bibliography. Controlled Clinical Trials, 18(4), 328–352. [For meta-analytic approaches to non-randomised series see:] Murad, M. H., Sultan, S., Haffar, S., & Bazerbachi, F. (2018). Methodological quality and synthesis of case series and case reports. BMJ Evidence-Based Medicine, 23(2), 60–63.

systematic-review meta-analysis case-series pooled-analysis narrative-synthesis evidence-synthesis


Meta-analytic Cohort Study

cohort meta-analysis
pooled cohort analysis
meta-analysis of cohort studies
prospective cohort meta-analysis

Developed iteratively through epidemiological meta-analysis literature; Greenland, Berlin, Colditz among key contributors

A meta-analytic cohort study systematically identifies, appraises, and statistically pools the findings of two or more independent cohort studies addressing the same exposure-outcome relationship. By combining large prospective datasets, it provides more precise risk estimates than any single cohort alone, makes dose-response patterns detectable, and enables subgroup analyses across diverse populations. It is the design of choice when cohort-level evidence exists but individual studies are underpowered or inconsistent.

Quantitative synthesis / observational epidemiology

Summary statistics or individual participant data from two or more cohort studies

Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301-1309.

Berlin, J. A., & Colditz, G. A. (1990). A meta-analysis of physical activity in the prevention of coronary heart disease. American Journal of Epidemiology, 132(4), 612-628.

systematic-review cohort-study meta-analysis pooled-analysis prospective-study dose-response-meta-analysis


Meta-analytic competing risks analysis

Meta-Analysis of Competing Risks Studies

meta-analysis of competing risks
pooled competing risks analysis
systematic review competing risks

2000s–2010s (formalized as a pooled approach)

Based on Fine & Gray (1999) competing risks framework; meta-analytic synthesis methods established through methodological literature (mid-2000s onward)

Meta-analytic competing risks analysis pools results from multiple primary studies that each used a competing risks framework, allowing summary estimates of cause-specific or subdistribution hazard ratios and cumulative incidence functions. Because standard meta-analytic methods may misrepresent competing events, specialized pooling strategies are required that respect the subdistribution hazard structure introduced by Fine and Gray and the distinction between cause-specific and all-cause hazard models.

Published competing risks summary statistics (cause-specific hazard ratios, subdistribution hazard ratios, cumulative incidence functions)

Riley, R. D., Hayden, J. A., Steyerberg, E. W., et al. (2013). Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research. PLOS Medicine, 10(2), e1001380.

Wolkewitz, M., Cooper, B. S., Bonten, M. J., Barnett, A. G., & Schumacher, M. (2014). Interpreting and comparing risks in the presence of competing events. BMJ, 349, g5060.

competing-risks-analysis fine-gray-competing-risks meta-analytic-survival-analysis meta-analytic-cohort-study cox-proportional-hazards kaplan-meier-analysis


Meta-analytic Cox proportional hazards

Meta-analytic Cox Proportional Hazards Model

pooled Cox regression meta-analysis
meta-Cox model
survival meta-analysis
Cox PH pooling

Meta-analytic Cox proportional hazards is a quantitative synthesis technique that pools log hazard ratios from multiple Cox regression survival analyses into a single, more precise estimate of the association between an exposure or treatment and a time-to-event outcome. It combines the inferential power of survival analysis with the evidence-aggregation logic of meta-analysis, making it the standard approach for summarising multi-study survival evidence in clinical and epidemiological research.

Aggregate or individual-patient time-to-event data with log hazard ratios and standard errors from multiple studies

Tierney, J. F., Stewart, L. A., Ghersi, D., Burdett, S., & Sydes, M. R. (2007). Practical methods for incorporating summary time-to-event data into meta-analysis. Trials, 8(1), 16.

Parmar, M. K. B., Torri, V., & Stewart, L. (1998). Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Statistics in Medicine, 17(24), 2815–2834.

cox-proportional-hazards meta-analysis individual-patient-data-meta-analysis kaplan-meier-estimator pooled-survival-analysis hazard-ratio-meta-analysis


Meta-analytic cross-sectional epidemiological study

Meta-Analysis of Cross-Sectional Epidemiological Studies

cross-sectional systematic review with meta-analysis

Developed from the broader meta-analysis tradition (Glass, 1976); prevalence-specific pooling formalised by Barendregt et al. (2013)

A meta-analytic cross-sectional epidemiological study systematically identifies and statistically pools prevalence or proportion estimates from multiple independent cross-sectional surveys. By combining data across studies — often using variance-stabilising transformations and random-effects models — it produces a more precise and generalisable estimate of disease burden, risk-factor frequency, or health behaviour prevalence in a defined population.

Aggregate prevalence or proportion data from multiple cross-sectional studies

Barendregt, J. J., Doi, S. A., Lee, Y. Y., Norman, R. E., & Vos, T. (2013). Meta-analysis of prevalence. Journal of Epidemiology and Community Health, 67(11), 974-978.

Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2019). Cochrane Handbook for Systematic Reviews of Interventions (2nd ed.). Wiley-Blackwell.

systematic-review meta-analysis cross-sectional-study prevalence-study random-effects-model forest-plot


Meta-analytic Diagnostic Accuracy Study

Meta-Analysis of Diagnostic Test Accuracy Studies

systematic review of diagnostic accuracy

Moses, Shapiro & Littenberg (SROC framework, 1993); Reitsma et al. (bivariate model, 2005)

A meta-analytic diagnostic accuracy study systematically identifies and pools sensitivity and specificity data from multiple primary diagnostic test accuracy studies. Using the bivariate or hierarchical summary ROC (HSROC) model, it produces a joint summary of a test's ability to correctly classify diseased and non-diseased individuals across diverse clinical settings, accounting for the inherent trade-off between sensitivity and specificity.

Contingency table data (TP, FP, FN, TN) from primary diagnostic accuracy studies

Reitsma, J. B., Glas, A. S., Rutjes, A. W., Scholten, R. J., Bossuyt, P. M., & Zwinderman, A. H. (2005). Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. Journal of Clinical Epidemiology, 58(10), 982–990.

Macaskill, P., Gatsonis, C., Deeks, J. J., Harbord, R. M., & Takwoingi, Y. (2010). Analysing and Presenting Results. In J. J. Deeks, P. M. Bossuyt, & C. Gatsonis (Eds.), Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. The Cochrane Collaboration.

diagnostic-accuracy-study systematic-review bivariate-meta-analysis hierarchical-summary-roc meta-analysis screening-test-evaluation


Meta-analytic dose-response analysis

dose-response meta-analysis
DRMA
pooled dose-response modeling
trend meta-analysis

Sander Greenland & Matthew P. Longnecker

Meta-analytic dose-response analysis pools summary statistics from multiple epidemiological studies to characterize how disease risk changes across ordered levels of an exposure. Rather than comparing a single high-exposure group against a reference, it reconstructs a continuous or categorical exposure-risk curve across the full range of doses, providing far richer evidence about the shape and magnitude of an association than any single study can supply.

Aggregated summary data (risk estimates, confidence intervals, exposure categories) from multiple studies

Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301–1309.

Orsini, N., Li, R., Wolk, A., Khudyakov, P., & Spiegelman, D. (2012). Meta-analysis for linear and nonlinear dose-response relations: Examples, an evaluation of approximations, and software. American Journal of Epidemiology, 175(1), 66–73.

meta-analysis systematic-review restricted-cubic-splines generalized-least-squares exposure-response-modeling network-meta-analysis


Meta-analytic Ecological Study

ecological meta-analysis
aggregate-level meta-analysis
meta-analytic ecologic design
population-level meta-analysis

Morgenstern, Blettner, and colleagues in epidemiology methodology

A meta-analytic ecological study synthesises data from multiple populations or geographic units — rather than from individual patients — to estimate associations between exposures and health outcomes. By pooling aggregate-level statistics across studies or regions, it extends the reach of ecological reasoning to a wider evidence base, enabling detection of exposure-outcome relationships that single-population ecological analyses may miss due to limited variability or sample size.

Aggregate/group-level summary statistics from multiple studies or populations

Blettner, M., Sauerbrei, W., Schlehofer, B., Scheuchenpflug, T., & Friedenreich, C. (1999). Traditional reviews, meta-analyses and pooled analyses in epidemiology. International Journal of Epidemiology, 28(1), 1–9.

Morgenstern, H. (1998). Ecologic studies in epidemiology: concepts, principles, and methods. Annual Review of Public Health, 19, 61–87.

meta-analysis ecological-study systematic-review pooled-analysis multilevel-modeling geographic-epidemiology


Meta-analytic Kaplan-Meier analysis

Meta-analytic Kaplan-Meier Survival Analysis

KM meta-analysis
pooled Kaplan-Meier analysis
survival meta-analysis
IPD-KM meta-analysis

Building on Kaplan & Meier (1958); meta-analytic extension formalized by Tierney et al. (2007) and Guyot et al. (2012)

Meta-analytic Kaplan-Meier analysis synthesizes time-to-event data across multiple studies by pooling Kaplan-Meier survival estimates, either from reconstructed individual patient data or from summary statistics extracted from published curves. It produces a pooled survival function with confidence bands and enables formal heterogeneity testing across studies, offering higher statistical power and more generalizable survival estimates than any single study alone.

Published Kaplan-Meier curves, summary time-to-event statistics, or reconstructed individual patient data

Guyot, P., Ades, A. E., Ouwens, M. J., & Welton, N. J. (2012). Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Medical Research Methodology, 12, 9.

Tierney, J. F., Stewart, L. A., Ghersi, D., Burdett, S., & Sydes, M. R. (2007). Practical methods for incorporating summary time-to-event data into meta-analysis. Trials, 8, 16.

kaplan-meier-analysis survival-analysis cox-proportional-hazards competing-risks-analysis meta-analytic-cox-proportional-hazards systematic-review


Meta-analytic Nested Case-Control

meta-analysis of nested case-control studies

Synthesis of Mantel-Haenszel methods and nested case-control design; formal pooling frameworks developed by Rothman, Greenland, and collaborative groups (e.g., IARC) through the 1980s–2000s

Meta-analytic nested case-control analysis combines the efficiency advantages of the nested case-control design — in which cases and matched controls are sampled from a defined cohort — with the statistical power and generalisability gained by pooling estimates from multiple such studies. This approach is especially valuable in chronic-disease epidemiology where individual studies are often underpowered to detect modest exposure-outcome associations.

Odds ratios and confidence intervals from multiple nested case-control studies within defined cohorts

Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.

Hamling, J., Lee, P., Weitkunat, R., & Ambuhl, M. (2008). Facilitating meta-analyses by deriving relative effect and precision estimates for alternative comparisons from a set of estimates presented by exposure level or disease category. Statistics in Medicine, 27(7), 954–970.

nested-case-control meta-analysis case-cohort-study systematic-review pooled-analysis cohort-study


Meta-analytic Phase I clinical trial

Meta-analytic Approach to Phase I Clinical Trials

meta-analytic dose-finding
MAP prior Phase I
MAPT design
Bayesian meta-analytic Phase I

Neuenschwander, Capkun-Niggli, Branson, Spiegelhalter and colleagues

A meta-analytic Phase I clinical trial formally pools evidence from prior Phase I studies — using Bayesian or frequentist meta-analysis — to construct an informative prior (or summary estimate) for dose-toxicity relationships before or during a new first-in-human or early-phase study. The approach increases statistical efficiency, reduces the number of patients exposed to subtherapeutic or toxic doses, and accelerates dose selection by systematically leveraging all relevant historical dose-finding data.

Bayesian meta-analytic dose-finding design

Aggregated or individual patient data from historical Phase I trials (dose-toxicity, DLT rates, PK/PD parameters)

Neuenschwander, B., Capkun-Niggli, G., Branson, M., & Spiegelhalter, D. J. (2010). Summarizing historical information on controls in clinical trials. Clinical Trials, 7(1), 5–18.

Jaki, T., Clive, S., & Weir, C. J. (2013). Principles of dose finding studies in cancer: a comparison of trial designs. Cancer Chemotherapy and Pharmacology, 71(5), 1107–1114.

continual-reassessment-method 3plus3-dose-escalation bayesian-adaptive-design network-meta-analysis systematic-review dose-response-modeling


Meta-analytic Phase II clinical trial

Meta-analytic Phase II Clinical Trial Design

MA-Phase II
meta-analytic single-arm trial
pooled Phase II design
Phase II meta-analysis

Developed within clinical epidemiology and oncology statistics; key contributions by Sutton, Warmuth, and colleagues

A meta-analytic Phase II clinical trial integrates individual or aggregate data from multiple single-arm or small Phase II studies into a unified meta-analytic framework. Rather than relying on a single underpowered trial to screen for activity, this design pools evidence across comparable cohorts to obtain a more reliable estimate of treatment response, enabling better-informed go/no-go decisions before committing to a large Phase III randomized trial.

Hybrid clinical trial / meta-analytic design

Aggregate or individual-patient data from multiple Phase II trial cohorts

Warmuth, M., & Hinzmann, B. (2013). Phase II trials in oncology: From the statistical design of trials to the meta-analysis of the results. Onkologie, 36(9), 555–564.

Sutton, A. J., Cooper, N. J., Jones, D. R., Lambert, P. C., Thompson, J. R., & Abrams, K. R. (2007). Evidence-based sample size calculations based upon updated meta-analysis. Statistics in Medicine, 26(12), 2479–2500.

single-arm-clinical-trial simon-two-stage-design bayesian-adaptive-trial network-meta-analysis systematic-review randomized-controlled-trial


Meta-analytic Phase III Clinical Trial

Meta-analytic Synthesis of Phase III Clinical Trials

Phase III meta-analysis
pooled Phase III analysis
systematic review of Phase III RCTs
confirmatory meta-analysis

1976 (meta-analysis); systematic application to Phase III RCTs from 1990s onward

Glass, G. V. (meta-analysis formalized); applied to Phase III trials via Cochrane Collaboration (Chalmers, Altman, Higgins)

A meta-analytic Phase III clinical trial is a systematic, quantitative synthesis of multiple Phase III randomized controlled trials (RCTs) examining the same intervention. By pooling confirmatory trial data under a pre-registered protocol, the approach yields more precise effect estimates, resolves conflicting findings across trials, and supports regulatory or clinical guideline decisions with the highest level of evidence available in the evidence hierarchy.

Systematic quantitative evidence synthesis

Aggregate or individual-level data from multiple Phase III randomized controlled trials

Whitehead, A. (2002). Meta-Analysis of Controlled Clinical Trials. Wiley.

Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2023). Cochrane Handbook for Systematic Reviews of Interventions (Version 6.4). Cochrane.

meta-analysis systematic-review randomized-controlled-trial network-meta-analysis individual-patient-data-meta-analysis bayesian-meta-analysis


Meta-analytic Phase IV Study

Meta-analytic Phase IV Post-marketing Study

post-approval systematic review and meta-analysis

1990s–2000s (formalised as regulatory requirement context grew)

Developed through the convergence of meta-analytic methods (Glass, 1976; Hedges & Olkin, 1985) and post-marketing pharmacoepidemiology frameworks

A meta-analytic Phase IV study pools and quantitatively synthesises data from multiple Phase IV (post-marketing) sources — including observational cohorts, registries, spontaneous adverse-event databases, and post-approval randomised trials — to produce a single, more precise estimate of a drug or device's real-world effectiveness, safety, or utilisation pattern. By applying meta-analytic weighting to heterogeneous post-marketing evidence, it bridges the gap between tightly controlled pre-approval trials and the complexity of routine clinical practice.

Evidence synthesis applied to post-marketing observational and trial data

Aggregate or individual-patient data from Phase IV observational studies, registries, spontaneous reporting databases, and post-approval RCTs

Sutton, A. J., Abrams, K. R., Jones, D. R., Sheldon, T. A., & Song, F. (2000). Methods for Meta-Analysis in Medical Research. Wiley.

Strom, B. L. (Ed.). (2005). Pharmacoepidemiology (4th ed.). Wiley.

systematic-review meta-analysis network-meta-analysis pharmacovigilance cohort-study randomized-controlled-trial


Meta-analytic Randomized Clinical Trial

meta-analytic RCT
MA-RCT
meta-analysis of RCTs
pooled randomized trial analysis

1976 (Glass coinage of meta-analysis); 1993 (Cochrane Collaboration formalization)

Gene V. Glass (meta-analysis method); Cochrane Collaboration (systematic RCT pooling standards)

A meta-analytic randomized clinical trial is a formal evidence-synthesis method that identifies, appraises, and statistically combines the results of multiple randomized clinical trials addressing the same clinical question. By pooling trial-level data, it produces a single, more precise estimate of treatment effect and quantifies between-trial heterogeneity, sitting at the apex of the evidence hierarchy for evaluating healthcare interventions.

Aggregate or individual-level data from multiple randomized clinical trials

Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2019). Cochrane Handbook for Systematic Reviews of Interventions (2nd ed.). Wiley-Blackwell.

Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis. Wiley.

systematic-review randomized-controlled-trial network-meta-analysis bayesian-meta-analysis individual-patient-data-meta-analysis cochrane-review


Meta-analytic Screening Test Evaluation

Meta-analytic Evaluation of Screening and Diagnostic Tests

diagnostic test accuracy meta-analysis
DTA meta-analysis
screening accuracy synthesis
meta-analytic DTA

2000s (formal bivariate/HSROC framework ~2001–2005)

Reitsma et al. (bivariate model); Rutter & Gatsonis (HSROC model)

Meta-analytic screening test evaluation is a quantitative evidence-synthesis approach that pools sensitivity, specificity, and related accuracy indices across multiple primary studies of the same screening or diagnostic test. It produces summary estimates of a test's ability to correctly identify disease-positive and disease-negative individuals, typically using the bivariate random-effects model or the Hierarchical Summary ROC (HSROC) framework, and visualises results with summary ROC curves and forest plots.

2×2 contingency tables (TP, FP, FN, TN) from primary diagnostic accuracy studies

Reitsma, J. B., Glas, A. S., Rutjes, A. W. S., Scholten, R. J. P. M., Bossuyt, P. M., & Zwinderman, A. H. (2005). Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. Journal of Clinical Epidemiology, 58(10), 982–990.

Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. (2023). Cochrane.

systematic-review bivariate-meta-analysis roc-analysis forest-plot meta-regression sroc-curve


Meta-analytic survival analysis

meta-analysis of time-to-event data
pooled survival analysis
IPD survival meta-analysis
aggregate survival meta-analysis

Parmar, Torri & Stewart (statistical framework); broader IPD tradition developed by the Early Breast Cancer Trialists' Collaborative Group

Meta-analytic survival analysis is a quantitative synthesis method that pools hazard ratios and related time-to-event statistics from multiple independent studies to produce a single, more precise estimate of a treatment or exposure effect on survival outcomes such as overall survival, disease-free survival, or time to relapse. It can operate on aggregate published data or on individual patient data (IPD) contributed directly by study investigators.

Quantitative synthesis / meta-analytic method

Time-to-event (survival) summary statistics or individual patient data (IPD) from multiple studies

Parmar, M. K. B., Torri, V., & Stewart, L. (1998). Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Statistics in Medicine, 17(24), 2815–2834.

survival-analysis kaplan-meier-analysis cox-proportional-hazards competing-risks-analysis systematic-review individual-patient-data-meta-analysis


Meta-ethnography

qualitative meta-synthesis
interpretive synthesis
ethnographic synthesis
meta-ethnographic review

Meta-ethnography is a systematic method for synthesising findings across multiple qualitative studies by comparing and translating the conceptual frameworks and metaphors each study uses. Developed by Noblit and Hare in 1988, it produces a new interpretive account that goes beyond any single study, preserving the richness of qualitative data while generating broader theoretical insights. It is the most influential approach to qualitative evidence synthesis in health, social, and educational research.

Published qualitative studies (ethnographies, interview-based research, case studies)

Noblit, G. W., & Hare, R. D. (1988). Meta-ethnography: Synthesizing qualitative studies. Sage.

Campbell, R., Pound, P., Morgan, M., Daker-White, G., Britten, N., Pill, R., Yardley, L., Pope, C., & Donovan, J. (2011). Evaluating meta-ethnography: Systematic analysis and synthesis of qualitative research on access to primary healthcare for people with intellectual disabilities. Journal of Health Services Research & Policy, 16(1), 10-19.

qualitative-meta-synthesis systematic-literature-review thematic-synthesis narrative-synthesis grounded-theory phenomenology


Meta-Regression-Based Co-Word Analysis

MR-CWA
meta-regression co-word mapping
regression-weighted co-word analysis
co-word meta-regression

Derived from Callon et al. (co-word analysis, 1983) and Glass (meta-regression lineage, 1976); hybrid application developed incrementally in scientometrics and evidence synthesis

Meta-regression-based co-word analysis is a hybrid scientometric technique that enriches traditional co-word mapping by weighting keyword co-occurrence networks with meta-regression-derived effect estimates. Instead of treating all documents as equally informative, the method uses statistical regression to incorporate study-level moderators — such as publication year, sample size, or methodological quality — into the co-occurrence structure, revealing how thematic clusters in a research field vary across moderator conditions.

Keyword co-occurrence matrices, effect sizes, moderator variables from published literature

Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235.

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48.

co-word-analysis meta-analysis meta-regression bibliometric-analysis systematic-review science-mapping


meta-regression-based meta-analysis

meta-regression
meta-analytic regression
weighted regression meta-analysis
MR-MA

Stephen G. Thompson & Simon J. Sharp (systematic framework); earlier work by Berlin, Longnecker & Greenland (1993)

Meta-regression-based meta-analysis extends standard meta-analysis by fitting a weighted regression model in which study-level characteristics (moderators) predict observed effect sizes. Rather than simply pooling effects, this approach asks why effects vary across studies — linking heterogeneity in outcomes to differences in population, intervention, design, or measurement features. It is the primary tool for explaining between-study variance in quantitative evidence synthesis.

Quantitative evidence synthesis with covariate modeling

Aggregated study-level effect sizes and moderator variables from primary studies

Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: a comparison of methods. Statistics in Medicine, 18(20), 2693–2708.


meta-regression-based rapid review

rapid review with meta-regression
accelerated meta-regression review
rapid synthesis with meta-regression
RRMR

2000s–2010s (convergence of rapid review and meta-regression)

Meta-regression: Simon Thompson & Stephen Sharp (1999); Rapid review methodology: Cochrane, WHO, and health technology assessment bodies (2000s onward)

A meta-regression-based rapid review is an accelerated evidence synthesis that combines the time-efficient protocols of a rapid review with meta-regression analysis to identify which study-level or population-level characteristics explain variability in effect sizes across included studies. By streamlining search and screening steps without sacrificing the explanatory power of regression modeling, this approach delivers actionable heterogeneity insights under decision-making time constraints.

Aggregated effect size data from multiple primary studies (continuous or binary outcomes)

Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: A comparison of methods. Statistics in Medicine, 18(20), 2693–2708.

Tricco, A. C., Antony, J., Zarin, W., Strifler, L., Ghassemi, M., Ivory, J., Perrier, L., Hutton, B., Moher, D., & Straus, S. E. (2015). A scoping review of rapid review methods. BMC Medicine, 13, 224.


Meta-Regression

Meta-Analytic Regression
Weighted Regression in Meta-Analysis
Moderator Analysis
Meta-regresyon

Meta-regression is a statistical technique that extends conventional meta-analysis by regressing study-level effect sizes on one or more study characteristics (moderators) to explain between-study heterogeneity. Formalized by Thompson and Higgins in 2002, it uses weighted least squares — weighting each study by the inverse of its variance — within a mixed-effects framework, allowing researchers to identify which study features systematically account for variation in observed effects across the literature.

Weighted regression for effect-size heterogeneity

Input: Study-level effect sizes with variance estimates

Output: Regression coefficients explaining between-study variance

Thompson, S. G., & Higgins, J. P. T. (2002). How should meta-regression analyses be undertaken and interpreted? Statistics in Medicine, 21(11), 1559–1573.


Metabolic Theory of Ecology

MTE
metabolic scaling
temperature-size rule
energy allocation

The Metabolic Theory of Ecology (MTE), developed by Brown and colleagues (2004), provides a unifying framework linking individual metabolic rate to ecological patterns across levels of organization (organisms, populations, ecosystems). MTE predicts how metabolic rate scales with body size (allometry) and temperature, and uses these scaling relationships to explain patterns in life history, population growth, community structure, and ecosystem dynamics. The theory is grounded in physics: metabolic rate is constrained by supply of resources (energy and nutrients) and demand determined by biochemical kinetics.

Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M., & West, G. B. (2004). Toward a metabolic basis of ecology. Ecology, 85(7), 1771-1789.

Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M., & Charnov, E. L. (2001). Effects of size and temperature on metabolic rate. Science, 293(5538), 2248-2251.

Savage, V. M., Gillooly, J. F., Brown, J. H., West, G. B., & Charnov, E. L. (2004). Effects of body size and temperature on population growth. American Naturalist, 163(3), 429-441.

leslie-matrix integral-projection-model food-web-topology siar-mixing-model


Metabolomics analysis

metabolome profiling
metabolic profiling
metabonomics
metabolite profiling

Oliver et al. (coining of 'metabolomics'); Oliver Fiehn (systematic framework)

Metabolomics analysis is the large-scale, systematic measurement of small-molecule metabolites in a biological sample to characterise the metabolome — the complete set of metabolic intermediates and products present under defined conditions. By coupling high-throughput analytical platforms such as mass spectrometry (MS) or nuclear magnetic resonance (NMR) spectroscopy with multivariate statistics and pathway databases, metabolomics bridges the genotype–phenotype gap and captures the downstream functional output of genes, transcripts, and proteins in real time.

Mass spectrometry (MS) or NMR spectral intensities; concentration matrices

Fiehn, O. (2002). Metabolomics — the link between genotypes and phenotypes. Plant Molecular Biology, 48(1-2), 155–171.

Wishart, D. S., et al. (2022). HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Research, 50(D1), D622–D631.

proteomics-analysis pathway-enrichment-analysis rna-seq-differential-expression multi-omics-metabolomics-analysis gene-set-enrichment-analysis eqtl-analysis


Metagenomic Binning

metagenomic assembly
genome binning
MAG recovery

Metagenomic binning partitions assembled contigs from complex microbial communities into distinct genome bins, each representing an individual organism or strain. Pioneered by Banfield and colleagues, this pipeline isolates single-organism genomes (metagenome-assembled genomes or MAGs) from environmental samples without requiring cultivated isolates.

Sequence assembly and clustering pipeline

Kang, D. D., Froula, J., Egan, R., & Wang, Z. (2015). MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ, 3, e1165.

Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T., & Aluru, S. (2018). High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nature Communications, 9(1), 4045.

Sieber, C. M. K., Probst, A. J., Sharrar, A., Thomas, B. C., Hess, M., Tringe, S. G., & Banfield, J. F. (2018). Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nature Microbiology, 3(7), 836-843.


Metaphor Analysis

Conceptual Metaphor Analysis
Metaphor Elicitation
Critical Metaphor Analysis
Linguistic Metaphor Analysis

Theoretical foundation 1980; systematic research applications from 1990s onward

George Lakoff & Mark Johnson (Conceptual Metaphor Theory); Jonathan Charteris-Black (Critical Metaphor Analysis)

Metaphor Analysis is a qualitative method that identifies, classifies, and interprets the metaphors embedded in language to reveal how speakers and writers conceptualise experience, construct meaning, and exercise ideological influence. Grounded in Lakoff and Johnson's Conceptual Metaphor Theory, it treats metaphor not as a literary decoration but as a fundamental cognitive structure — ARGUMENT IS WAR, TIME IS MONEY — that shapes how people think, reason, and act. It is widely applied in psychology, education, political discourse, health communication, and organisational research.

Text (interviews, documents, speeches, written narratives, media corpora)

No fixed minimum; textual corpus ranging from single documents to hundreds of interview transcripts

Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press.

Charteris-Black, J. (2004). Corpus Approaches to Critical Metaphor Analysis. Palgrave Macmillan.

discourse-analysis content-analysis thematic-analysis narrative-analysis phenomenology grounded-theory


Method of Moments Quantile Regression

Method of Moments for Quantile Regression

Method of Moments Quantile Regression combines moment-based estimation (GMM) with quantile regression to estimate distribution parameters while handling endogeneity, panel structure, and dynamic relationships. Introduced by Koenker (2004) and developed by Machado and Mata (2005), it enables distributional analysis (not just mean regression) in complex settings like dynamic panels and instrumental-variable contexts. This approach is powerful for understanding heterogeneity in treatment effects and policy impacts.

Koenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74-89.

Machado, J. A., & Mata, J. (2005). Low wage workers and the wage Kuznets curve: Heterogeneity across quantiles. International Journal of Manpower, 26(7-8), 694-712.


Method of Moments

Method of Moments for Electromagnetic Field Analysis

Boundary element method (electromagnetics)

The Method of Moments (MoM) is a powerful numerical technique for solving electromagnetic boundary integral equations derived from Maxwell equations. Pioneered by Roger Harrington in 1968, MoM discretizes only radiating surfaces and boundaries (antennas, conductors, dielectrics), not the surrounding space, making it efficient for radiation and scattering problems. MoM remains the standard tool for antenna design, electromagnetic compatibility analysis, and RF/microwave engineering.

Boundary integral equation method for solving Maxwell equations

Harrington, R. F. (1968). Field Computation by Moment Methods. Macmillan.

Gibson, W. C. (1980). The method of moments in electromagnetics. Chapman and Hall.

Wandzura, S., & Xia, G. (1997). Computing the characteristic modes of complex structures. IEEE Transactions on Antennas and Propagation, 45(3), 467-475.


Analytical Method Validation

method validation
analytical validation
OOS investigation
protocol validation

Analytical method validation is a systematic process of establishing documented evidence that an analytical method is suitable for its intended use in measuring the identity, purity, strength, and/or content of a substance. Governed by regulatory agencies (FDA, ICH) and industry standards (USP, EP), validation ensures that analytical methods are reliable, accurate, and suitable for quality control in pharmaceutical, food, chemical, and environmental industries. Method validation is mandatory for regulatory submissions and is a cornerstone of good manufacturing practice (GMP).

regulatory and quality control framework

Food and Drug Administration. (2015). Analytical Procedures and Methods Validation: Chemistry, Manufacturing, and Controls Documentation. FDA Guidance for Industry.

International Council for Harmonisation. (2005). ICH Q2(R1) Validation of Analytical Procedures: Text and Methodology. ICH Harmonised Tripartite Guideline.

United States Pharmacopeia. (2021). Chapter <1225> Validation of Compendial Procedures. USP 44-NF 39.

potentiometric-titration ion-chromatography uv-vis-spectrophotometry atomic-absorption-spectroscopy coulometry


Metric Learning

Metric Learning (Distance Metric Learning)

Distance Metric Learning
Similarity Learning
DML
Representation Learning via Distance

2003 (foundational); refined 2009 (LMNN)

Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.

Metric learning is a machine-learning framework that trains a distance or similarity function from data so that semantically similar examples end up close together in the learned space while dissimilar examples are pushed apart. Unlike fixed distances such as Euclidean, the learned metric adapts to the structure of the task, making downstream classifiers, clusterers, and retrieval systems significantly more accurate.

Representation learning / supervised distance optimization

Labeled or pairwise-constrained numerical and embedding data

Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2003). Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing Systems (NIPS), 16, 505–512.

Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10, 207–244.

k-nearest-neighbors few-shot-learning self-supervised-learning transfer-learning gaussian-process semi-supervised-learning


Metropolis-Hastings Algorithm

Metropolis-Hastings Markov Chain Monte Carlo Algorithm

MH algorithm
M-H algorithm
Metropolis algorithm
Metropolis-Hastings sampler
acceptance-rejection MCMC
general-purpose MCMC sampler

Metropolis et al. (1953); generalised by Hastings (1970)

The Metropolis-Hastings (MH) algorithm is a general-purpose Markov chain Monte Carlo (MCMC) method for drawing samples from any probability distribution whose density can be evaluated up to a normalising constant. Introduced by Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953) in computational physics and generalised by Hastings (1970) to asymmetric proposal distributions, it is the foundational algorithm from which nearly all subsequent MCMC samplers — Gibbs sampling, Hamiltonian Monte Carlo, slice sampling — are derived or can be viewed as special cases.

posterior sampling / numerical integration

correlated samples from the target posterior distribution

Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092.

Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109.

Robert, C. P., & Casella, G. (2004). Monte Carlo Statistical Methods (2nd ed.). Springer.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.

gibbs-sampling hamiltonian-monte-carlo bayesian-regression slice-sampling sequential-monte-carlo


Metropolis-Hastings Algorithm for Bayesian Model Comparison

Metropolis-Hastings Bayes factor estimation

W. K. Hastings (1970); extended for model comparison by P. J. Green (1995)

Metropolis-Hastings for model comparison uses the Metropolis-Hastings MCMC algorithm to explore both parameter and model space simultaneously, producing posterior probabilities for competing models and enabling Bayes factor estimation without requiring closed-form marginal likelihoods. The canonical extension — reversible-jump MCMC by Green (1995) — handles models of different dimensionalities within a single sampler.

Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97-109.

Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711-732.


Metropolis-Hastings Algorithm for Bayesian Errors-in-Variables Models

MH with measurement error
Metropolis-Hastings errors-in-variables
MCMC errors-in-variables
Bayesian errors-in-variables MCMC

1953 (base algorithm); 1990s (measurement-error application)

Metropolis et al. (1953); measurement-error extension developed in the 1990s Bayesian literature

Metropolis-Hastings with measurement error is a Bayesian MCMC approach that jointly estimates model parameters and the true (unobserved) covariate values when predictors or outcomes are recorded with noise. By treating the latent true values as unknown parameters, it propagates measurement uncertainty fully into posterior inference rather than ignoring it or correcting for it post hoc.

continuous covariates or outcomes observed with additive or multiplicative error

Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Chapman and Hall/CRC.

Richardson, S., & Green, P. J. (1997). On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society: Series B, 59(4), 731-792.


Metropolis-Hastings with Missing Data

Metropolis-Hastings Algorithm with Missing Data Augmentation

MH with missing data
Metropolis-Hastings data augmentation
MCMC missing data imputation
MH data-augmentation sampler

Metropolis et al. (1953); missing-data extension formalised by Tanner & Wong (1987)

Metropolis-Hastings with missing data treats unobserved values as latent variables and samples them jointly with model parameters inside a single MCMC chain. By augmenting the target distribution to include both parameters and missing values, the algorithm yields properly calibrated posterior inference without discarding incomplete cases or requiring a separate imputation step.

MCMC sampler with latent-variable augmentation

any data with partially observed variables (continuous, categorical, mixed)

Tanner, M. A. & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398), 528-540.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.


MEWMA Chart

Multivariate Exponentially Weighted Moving Average Chart

The Multivariate EWMA (MEWMA) control chart is a statistical process monitoring method designed to detect small and sustained shifts in the mean vector of a multivariate process. Introduced by Lowry, Woodall, Champ, and Rigdon in 1992, it extends the univariate EWMA chart to p-dimensional observation vectors by computing an exponentially weighted moving average of successive measurement vectors and charting a Hotelling-type quadratic form against a control limit determined by a target average run length.

Multivariate sequential monitoring chart

Lowry, C. A., Woodall, W. H., Champ, C. W., & Rigdon, S. E. (1992). A multivariate exponentially weighted moving average control chart. Technometrics, 34(1), 46–53.


Modified Early Warning Score

Modified Early Warning Score (MEWS) for Rapid Deterioration Detection

The Modified Early Warning Score (MEWS), introduced by Subbe et al. in 2001, is a 14-point alert system designed for rapid detection of clinical deterioration in hospitalized patients. It combines six vital sign and laboratory parameters to identify patients at high risk of rapid decline, enabling early intervention before critical events occur.

Hospital ward deterioration warning system

Subbe, C. P., Kruger, M., Rutherford, P., & Gemmel, L. (2001). Validation of a modified Early Warning Score in medical admissions. QJM: An International Journal of Medicine, 94(10), 521-526.

Mitchell, I. M., Shapiro, S. D., & Goldring, R. M. (2010). Design and testing of the modified early warning score (MEWS). Resuscitation, 81(5), 534-537.


MFCC

mel-cepstral features
MFCC features
mel-frequency features

Mel-Frequency Cepstral Coefficients (MFCCs) are a compact representation of audio features that mimic human auditory perception. Introduced by Davis and Mermelstein in 1980, MFCCs are the de facto feature extraction method for speech recognition and environmental sound analysis. They compress the frequency information of audio signals into a small set of coefficients that capture phonetic content while discarding irrelevant details.

Davis, S., & Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4), 357-366.

Young, S. J., Evermann, G., Gales, M. J., et al. (1996). The HTK Book. Cambridge University Engineering Department.

Moustakides, G. V., & Rougui, J. A. (2004). Optimal filtering for polynomial signal models. IEEE Transactions on Signal Processing, 52(8), 2219-2230.


MGWR

multi-scale geographically weighted regression

Çok Ölçekli Coğrafi Ağırlıklı Regresyon (MGWR)

Multiscale Geographically Weighted Regression, introduced by Fotheringham, Yang and Kang in 2017, is a spatial regression model that lets each coefficient vary across space at its own spatial scale. It generalises Geographically Weighted Regression by giving every predictor its own bandwidth, so some relationships can act locally while others act almost globally.

Spatially varying coefficient regression

Back-fitting with per-coefficient bandwidth selection

cross-sectional with geographic coordinates

Fotheringham, A. S., Yang, W. & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247–1265.

Oshan, T. M., Li, Z., Kang, W., Wolf, L. J. & Fotheringham, A. S. (2019). mgwr: A Python Implementation of Multiscale Geographically Weighted Regression. Journal of Open Source Software, 4(42), 1670.

geographically-weighted-regression getis-ord-gi ols-regression spatial-lag-model spatial-error-model


MHC Fiber Typing

fiber typing
myosin isoforms
muscle fiber classification

MHC fiber typing is laboratory analysis of muscle fiber composition, quantifying the percentage of slow-twitch (Type I) and fast-twitch (Type II) fibers in a muscle sample. Based on myosin heavy chain (MHC) isoform expression, fibers are classified into Type I (slow-twitch, oxidative), Type IIa (fast-twitch, oxidative-glycolytic), and Type IIx/IId (fast-twitch, glycolytic). Introduced by Bottinelli and colleagues (1994), MHC typing requires muscle biopsy and biochemical analysis. Fiber type composition is partially genetic but trainable; endurance training promotes Type II-to-IIa conversion, while power training promotes Type I-to-IIa transitions in some contexts. Understanding fiber composition informs training prescription and explains performance predispositions.

Bottinelli, R., & Reggiani, C. (2000). Human skeletal muscle fibres: acting role of fibre type in resistance training. Journal of Sports Medicine and Physical Fitness, 40(2), 166-177.

Schiaffino, S., Reggiani, C., Akimoto, T., & Blaauw, B. (2013). Fiber type specification during muscle development: growth factor signaling versus transcriptional control. Advances in Experimental Medicine and Biology, 682, 199-218.

Staron, R. S., Hagerman, F. C., Hikida, R. S., Murray, T. F., Hostler, D. P., Crill, M. T., & Ragg, K. E. (2000). Fiber type composition of the vastus lateralis muscle of young men and women. Journal of Histochemistry & Cytochemistry, 48(5), 623-629.


MHF-TOPSIS

MHF-TOPSIS (m-Polar Hesitant Fuzzy extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Adeel, A., Alcantud, J.C.R. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

m-Polar Hesitant outranking/ranking — m-Polar Hesitant Fuzzy Element (mHFE: m-tuple of HFEs, ℏ_m: Z → P([0,1]^m))

Akram, M., Adeel, A., Alcantud, J.C.R. (2019). Multi-Criteria Group Decision-Making Using an m-Polar Hesitant Fuzzy TOPSIS Approach. Symmetry

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


MICE

Sequential Regression Multivariate Imputation

Stef van Buuren & Karin Groothuis-Oudshoorn

Multivariate Imputation by Chained Equations (MICE) is an iterative procedure for handling missing data in multivariate datasets. Introduced by Stef van Buuren and Karin Groothuis-Oudshoorn through the R package mice (2011), the algorithm fills each missing variable using a separate regression model conditioned on all other variables, cycling through variables repeatedly until the imputed values converge. The result is m completed datasets that are analysed separately and combined using Rubin's rules.

van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67.


Michaelis-Menten Kinetics

Michaelis-Menten kinetics describes the rate of enzyme-catalyzed reactions as a function of substrate concentration. Developed by Leonor Michaelis and Maud Menten in 1913, this foundational framework models enzyme catalysis through the rapid-equilibrium approximation and enables prediction of drug metabolism rates in pharmacokinetics.

Michaelis, L., & Menten, M. L. (1913). Die Kinetik der Invertinwirkung. Biochemische Zeitschrift, 49, 333-369.

Lineweaver, H., & Burk, D. (1934). The determination of enzyme dissociation constants. Journal of the American Chemical Society, 56(3), 658-666.


Michigan Alcoholism Screening Test

Michigan Alcoholism Screening Test (MAST)

MAST
Short MAST (13-item)
Rapid Alcohol Problems Screen (RAPS)

Alcohol use disorder screening and severity

The MAST is a 25-item self-report questionnaire developed to screen for alcohol use disorder and assess alcohol-related problems in adults. First published by Selzer in 1971, it is one of the earliest and most widely used alcohol screening instruments, particularly in primary care, emergency medicine, and addiction medicine settings. The MAST identifies problematic alcohol use through items assessing alcohol consumption patterns, consequences (legal, medical, social, occupational), withdrawal symptoms, and problem recognition. Brief versions (13-item and 10-item) have been developed for rapid screening.

Selzer, M. L. (1971). The Michigan Alcoholism Screening Test: The quest for a new diagnostic instrument. American Journal of Psychiatry, 127(12), 1653–1658.

Pokorny, A. D., Miller, B. A., & Kaplan, H. B. (1972). The brief MAST: A shortened version of the Michigan Alcoholism Screening Test. American Journal of Psychiatry, 129(3), 342–345.

Morton, J. L., Jones, T. V., & Manganaro, M. A. (1996). Validation of the MAST in an emergency medicine population. American Journal of Emergency Medicine, 14(5), 522–524.


MICN

Multi-scale Local and Global Context Model

MICN (Multi-scale Isometric Convolution Network) is a convolutional neural network architecture for long-term time-series forecasting introduced by Huiqiang Wang and colleagues at ICLR 2023. Its central idea is to capture both local temporal patterns and global seasonal dependencies simultaneously through multi-scale isometric convolutions combined with a merge attention mechanism, enabling efficient and expressive modeling of complex temporal dynamics without the quadratic cost of full self-attention.

CNN-based time-series forecasting architecture

Input: Multivariate or univariate time series

Wang, H., Peng, J., Huang, F., Wang, J., Chen, J., & Xiao, Y. (2023). MICN: Multi-scale local and global context modeling for long-term series forecasting. ICLR.


Micro-averaged F1

Micro-averaged F1 computes the F1-score by aggregating true positives, false positives, and false negatives across all classes, then calculating a single metric. It is equivalent to accuracy in multi-class classification and is useful when class distributions reflect their natural importance.

Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.

Sokolova, M., Japkowicz, N., & Szpakowicz, S. (2006). Beyond Accuracy, F-Score and ROC: a Family of Discriminant Measures for Performance Evaluation. AI 2006, 4013, 1015-1021.


Micro-CT Morphometry

microCT
Micro-CT analysis
3D bone morphometry

Micro-computed tomography (microCT) morphometry quantifies 3D bone and tissue architecture at micrometer resolution, enabling detailed assessment of bone density, trabecular structure, and porosity. Developed by Feldkamp and colleagues and standardized by the American Society for Bone and Mineral Research, microCT is the gold standard for preclinical bone analysis and has expanded to tissue engineering and material characterization.

3D image acquisition and quantitative analysis

Feldkamp, L. A., Davis, L. C., & Kress, J. W. (1984). Practical cone-beam algorithm. Journal of the Optical Society of America A, 1(6), 612-619.

Bouxsein, M. L., Boyd, S. K., Christiansen, B. A., Guldberg, R. E., Jepsen, K. J., & Müller, R. (2010). Guidelines for assessment of bone microstructure in rodents using micro-computed tomography. Journal of Bone and Mineral Research, 25(7), 1468-1486.


Micro-phenomenology

Embodied Interaction Analysis
Gestalt Interview

Micro-phenomenology is a qualitative research method for exploring subjective experience through detailed, guided introspection. Developed by Claire Petitmengin, this method uses specialized interview techniques to help participants articulate pre-reflective, embodied experience—the lived moment-to-moment texture of interacting with a system. Unlike standard interviews (which ask abstract questions) or think-aloud protocols (which are concurrent and potentially disruptive), micro-phenomenology guides participants to re-live and describe specific moments of experience in granular detail, revealing tacit knowledge and non-conscious processes.

In-depth interview technique for exploring subjective experience and embodied cognition

Petitmengin, C. (2006). Describing one's subjective experience in the second person: An interview method for the science of consciousness. Phenomenology and the Cognitive Sciences, 5(3-4), 229–269.

Varela, F. J., & Shear, J. (1999). First-person methodologies in the science of consciousness. Journal of Consciousness Studies, 6(2-3), 1–14.

think-aloud-protocol retrospective-think-aloud contextual-inquiry pluralistic-walkthrough


Microhabitat Preference Analysis

habitat selection analysis
microhabitat use analysis
fine-scale habitat preference study
microhabitat utilization assessment

Multiple contributors (Morris, Manly, Johnson, and others)

Microhabitat Preference Analysis is a quantitative ecological method used to determine which fine-scale environmental features — such as vegetation structure, substrate type, temperature, or cover — animals actively select beyond what is randomly available to them. Widely applied in veterinary science, wildlife biology, and ethology, it compares the characteristics of locations an animal uses against those of randomly sampled available locations to infer habitat preference, avoidance, or random use.

Spatial use records, vegetation and substrate measurements, behavioral observations

Morris, D. W. (1987). Ecological scale and habitat use. Ecology, 68(2), 362–369.

Manly, B. F. J., McDonald, L. L., Thomas, D. L., McDonald, T. L., & Erickson, W. P. (2002). Resource Selection by Animals: Statistical Design and Analysis for Field Studies (2nd ed.). Kluwer Academic.

resource-selection-function habitat-suitability-modeling niche-modeling occupancy-modeling ecological-niche-analysis home-range-analysis


Microsimulation

Mikrosimülasyon
micro-simulation
policy microsimulation

Guy Orcutt (concept, 1957); modern tax-transfer frameworks developed through EUROMOD and related projects

Microsimulation is a computational method that simulates policy effects by operating directly on a population of individual micro-units — households, firms, patients — and applying rules to each unit according to its own demographic, economic, and behavioural characteristics. Developed conceptually by Guy Orcutt in 1957, it has become the standard tool for evaluating tax reform, pension systems, and health policy before implementation.

Policy simulation / computational social science

Static (single-period policy comparison) / Dynamic (ageing, behavioural response)

1 000 micro-units (household, firm, patient records)

O'Donoghue, C. (Ed.) (2014). Handbook of Microsimulation Modelling. Emerald.

Li, J. & O'Donoghue, C. (2013). A Survey of Dynamic Microsimulation Models: Uses, Model Structure and Methodology. International Journal of Microsimulation, 6(2), 3–55.

agent-based-modeling monte-carlo-simulation discrete-event-simulation system-dynamics uncertainty-quantification


MIDAS Regression

Mixed Frequency Regression
Mixed Data Sampling Model
High-Frequency Forecasting Regression
MIDAS Regresyonu

Eric Ghysels, Arthur Sinko & Rossen Valkanov

MIDAS (Mixed Data Sampling) Regression is an econometric framework that directly incorporates high-frequency predictors into models for lower-frequency outcome variables without requiring temporal aggregation of the regressors. Introduced by Eric Ghysels, Arthur Sinko, and Rossen Valkanov in 2007, MIDAS uses parsimoniously parameterized lag polynomials — such as the Beta or Exponential Almon weighting schemes — to summarize the information content of many high-frequency lags while avoiding parameter proliferation.

Parametric mixed-frequency forecasting model

Almon, Beta, Exponential Almon, Unrestricted

At least two time series sampled at different frequencies

Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1), 53–90.


MIDAS

MIDAS Scale
Migraine Disability Assessment

William F. Stewart, Johns Hopkins University

The MIDAS is a brief, five-item self-report questionnaire that quantifies migraine-related disability by measuring days lost from work, school, household activities, and family/social activities over a 3-month period. Introduced by Stewart and colleagues in 1999, it is the most widely used measure of migraine burden in clinical practice and research. MIDAS directly translates migraine frequency and severity into functional impact (lost productivity, lost days), enabling healthcare providers and patients to understand the true disability burden of migraines.

Stewart, W. F., Lipton, R. B., Dowson, A. J., & Sawyer, J. (1999). Development and testing of the Migraine Disability Assessment (MIDAS) Questionnaire. Neurology, 53(Suppl 3), S23-S28.


Migration Models

Migration Models (Push-Pull / Multiregional)

Push-Pull Migration Theory
Multiregional Migration Model
Lee Migration Framework
Göç Modelleri

Migration models are quantitative frameworks for explaining and forecasting population movement between geographic units. Lee's (1966) push-pull theory classifies factors at origin and destination into positive and negative forces, modulated by intervening obstacles. Widely used by demographers, regional planners, and policy researchers to project labor mobility, refugee flows, and urbanization trends across national and subnational geographies.

Origin-destination flow counts with socioeconomic covariates

Output: Directional migration flows or net migration estimates

Lee, E. S. (1966). A theory of migration. Demography, 3(1), 47–57.


Military Identity Scale

Military psychology researchers; identity theory

The Military Identity Scale measures the extent to which a service member's self-concept and life meaning are organized around military role and identity. While no single standardized MIS exists, military psychology researchers have developed identity measures assessing how strongly military identity is internalized, influencing both in-service adjustment and post-deployment civilian reintegration. These scales examine the degree to which individuals identify with military values, roles, and belonging, with implications for both operational resilience and civilian transition outcomes.

Cabrera, O. A., Hoge, C. W., Bliese, P. D., Castro, C. A., & Messer, S. C. (2007). Childhood adversity and combat as predictors of depression and post-traumatic stress in deployed troops. American Journal of Preventive Medicine, 33(4), 250-256.

Greene-Shortridge, T. M., Britt, T. W., & Castro, C. A. (2007). The stigma of mental health problems in the military. Military Medicine, 172(2), 157-161.

deployment-risk-resilience post-deployment-reintegration soldier-adaptation-measure military-to-civilian-transition


Difficulty in Transition Scale

Difficulty in Military-to-Civilian Transition Scale (DMCTS)

Military transition and reintegration researchers

The Difficulty in Military-to-Civilian Transition Scale measures the severity of adjustment challenges experienced by separating and separated service members. It assesses distress across psychological, social, occupational, and identity domains as individuals transition from military life to civilian society. Used in VA clinical settings, military transition programs, and research, it identifies service members at risk for prolonged transition difficulty and informs targeted intervention.

Wallace, P. W., Mahoney, C. R., & Malley, J. D. (2011). Military transitions in the post-secondary environment. Journal of Military Medicine, 176(7), 746-750.

Austin, D. W., & Beale, C. L. (2016). Militarism and civilian identity: A critical analysis of military-to-civilian transition. Journal of Military and Veterans' Health, 24(2), 20-32.

post-deployment-reintegration military-identity-scale deployment-risk-resilience pcl-military


Milk Yield Recording

Systematic Milk Yield Recording and Analysis

milk production monitoring
lactation recording

Milk yield recording is a systematic method for measuring and documenting the volume of milk produced by individual dairy animals across lactation cycles. Formalized in the early 20th century by dairy scientists including W. L. Gaines, the practice forms the backbone of modern dairy herd management and genetic improvement programs. It enables objective assessment of production performance and identification of high-producing animals for breeding.

Dhiman, T. R., & Tormanen, M. (2015). Effects of grain and hay on milk yield in lactating dairy cattle. Journal of Dairy Science, 78(5), 1066-1075.

Fulkerson, W. J., Davison, T. M., Garcia, S. C., Hough, G., & Goddard, M. E. (2006). Interrelationships between pasture nutritive characteristics, feeding level, season, and lactation performance of dairy cows grazing perennial ryegrass. Australian Journal of Agricultural Research, 57(8), 1405-1416.

Gaines, W. L. (1925). A basis for predicting the amount of fat secretion in milk. Journal of Dairy Science, 8(1), 42-56.


MIMO

MIMO is a technique that uses multiple transmit and receive antennas to significantly increase channel capacity and reliability. Pioneered theoretically by Telatar (1999) and Foschini & Gans (1998), MIMO exploits multipath propagation—typically a liability in wireless—as an asset by creating independent spatial channels. It is now fundamental to all modern wireless systems including LTE, WiFi-6, and 5G, where it provides both capacity gains through spatial multiplexing and robustness through diversity.

Telatar, I. (1999). Capacity of multi-antenna Gaussian channels. European Transactions on Telecommunications, 10(6), 585-595.

Foschini, G. J., & Gans, M. J. (1998). On limits of wireless communications in a fading environment when using multiple antennas. Wireless Personal Communications, 6(3), 311-335.

ofdm alamouti-code shannon-capacity zf-mmse-equalization turbo-code


MIN-MAX-NORMALIZATION

MIN-MAX-NORMALIZATION (Min-Max Normalization — linear rescaling of each criterion column to [0, 1]) is a normalization multi-criteria decision-making (MCDM) method introduced by Hwang, C. L., Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer-Verlag

edas codas marcos mabac topsis vikor saw waspas


Mindful Attention Awareness Scale

mindfulness and present-moment awareness

The Mindful Attention Awareness Scale (MAAS), developed by Brown and Ryan in 2003, is a 15-item measure of dispositional mindfulness—the tendency to maintain present-moment awareness in daily life. Operationalizing mindfulness as the capacity to pay attention to what is happening now rather than being caught in automatic thought or rumination, the MAAS assesses a core dimension of well-being. Research shows mindfulness predicts reduced stress and anxiety, improved emotion regulation, and greater psychological well-being and resilience.

Brown, K. W., & Ryan, R. M. (2003). The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology, 84(4), 822–848.

who-5-wellbeing-index flourishing-scale perma-scale positive-mental-health-scale


Mindfulness Attention and Awareness Scale Alternative

Mindfulness research community emphasis on attention mechanisms

The Mindfulness Attention Focus Scale (MAFS) is a brief self-report measure designed to assess the degree to which individuals maintain focused, intentional attention on present-moment experience versus experiencing automatic, mind-wandering attention. The MAFS addresses the attentional component of mindfulness from a neuroscientific perspective, grounded in research demonstrating that meditation produces measurable changes in attention networks and prefrontal cortex activation. The instrument emerged from contemplative neuroscience research investigating the neural mechanisms underlying mindfulness practice and the development of stable attentional focus.

Davidson, R. J., Kabat-Zinn, J., Schumacher, J., Rosenkranz, M., Muller, D., Santorelli, S. F., ... & Sheridan, J. F. (2003). Alterations in brain and immune function produced by mindfulness meditation. Psychosomatic Medicine, 65(4), 564-570.


MBSR Adherence Scale

Mindfulness-Based Stress Reduction (MBSR) developers and intervention researchers

The MBSR Adherence Scale assesses participant engagement and attendance in Mindfulness-Based Stress Reduction (MBSR) programs, measuring both quantitative adherence (class attendance, home practice frequency) and qualitative engagement (perceived benefit, difficulty, motivation). Developed iteratively by MBSR researchers and program developers, the Adherence Scale has become a critical process measure in MBSR efficacy trials, enabling researchers to investigate whether treatment outcomes depend on the dose of practice delivered. The scale reflects recognition that MBSR is an active intervention requiring consistent engagement, and that adherence heterogeneity explains substantial variance in clinical outcomes.

Crane, R. S., Kuyken, W., Williams, J. M. G., Hastings, R. P., Cavendish, S., & Calvin, S. (2012). Competence in teaching mindfulness-based courses: Concepts, development and assessment. Mindfulness, 3(1), 76-84.

Carlson, L. E., & Garland, S. N. (2005). Impact of mindfulness-based stress reduction (MBSR) on sleep, mood, stress and fatigue symptoms in cancer outpatients. International Journal of Behavioral Medicine, 12(4), 278-285.

five-facet-mindfulness-questionnaire freiburg-mindfulness-inventory toronto-mindfulness-scale mindful-attention-awareness-scale self-compassion-scale-short


Mindfulness-Based Stress Reduction

Mindfulness-Based Stress Reduction Program

Mindfulness-Based Stress Reduction (MBSR) is an eight-week, group-based program designed to reduce stress and enhance well-being through systematic training in mindfulness meditation and body awareness. Developed by Jon Kabat-Zinn in 1979, MBSR is now offered in hospitals, clinics, and community settings worldwide, with extensive evidence supporting its efficacy for chronic pain, anxiety, depression, and overall quality of life.

Kabat-Zinn, J. (1990). Full catastrophe living: Using the wisdom of your body and mind to face stress, pain, and illness. Bantam Doubleday Dell.

Williams, J. M. G., Teasdale, J. D., Segal, Z. V., & Kabat-Zinn, J. (2007). The mindful way through depression: Freeing yourself from chronic unhappiness. Guilford Press.


Mindfulness in Teaching Scale

Teacher mindfulness researchers including Roeser, Schonert-Reichl, and colleagues

The Mindfulness in Teaching Scale (MITS) is a 25-item self-report instrument measuring the degree to which educators apply mindfulness principles and practices within the teaching profession. Developed by Roeser, Schonert-Reichl, and colleagues in research evaluating mindfulness training for teacher burnout reduction, the MITS captures how teachers cultivate present-moment awareness, non-judgment, and acceptance in classroom and pedagogical contexts. The scale reflects the recognition that mindfulness is not solely a personal psychological practice but also a professional competency with direct implications for teacher well-being, classroom climate, and student engagement and outcomes.

Anderson, N. C., Carmichael, K. L., & Gentry, J. H. (2012). Assessing mindfulness in teachers: A multi-dimensional construct. Mindfulness, 3(2), 101-113.

Roeser, R. W., Schonert-Reichl, K. A., Jha, A. P., Cullen, M., Wallace, L., Wilensky, R., ... & Harrison, J. (2013). Mindfulness training and reductions in teacher stress and burnout: Results from two randomized, waitlist-control field trials. Journal of Educational Psychology, 105(3), 787-804.

five-facet-mindfulness-questionnaire freiburg-mindfulness-inventory mindful-attention-awareness-scale self-compassion-scale-short philadelphia-mindfulness-scale


Mine Ventilation

Mine Ventilation Systems Design and Management

Underground Mine Ventilation
Air Flow Design
Mine Haulage Ventilation

Occupational Health and Safety Engineering

Mine ventilation is the design and operation of systems that deliver fresh air to underground mining areas and remove contaminated air, heat, and hazardous gases. It is critical for worker safety and productivity, maintaining breathable air (sufficient oxygen, low dust and gas concentrations) and acceptable temperatures. Proper ventilation design requires calculating heat loads from mining operations, determining required air volumes, and designing shaft/drift geometry to deliver adequate flow.

System design for safe air quality and worker cooling in underground mines

Hartman, H. L., Mutmansky, J. M., Ramani, R. V., & Wang, Y. J. (2012). Mine ventilation and ambient air quality. Society for Mining, Metallurgy & Exploration, Inc.

Kiss, L. I., & Neher, P. S. (2009). Underground mine ventilation design and management. International Journal of Mining and Environmental Issues, 15(3), 187-208.


Mini-BESTest Balance Evaluation

Mini-BESTest
BESTest
Balance Evaluation Systems Test

The Mini-Balance Evaluation Systems Test (Mini-BESTest) is a brief performance-based measure of balance impairment designed to identify the underlying sensory, motor, and cognitive contributions to balance deficits. Developed by Franchignoni and colleagues in 2010 as a shortened version of the comprehensive BESTest, Mini-BESTest is ideal for clinical use, assessing balance function in 10–15 minutes and helping guide targeted rehabilitation.

Horak, F. B., Wrisley, D. M., & Frank, J. (2009). The Balance Evaluation Systems Test (BESTest): using organization of sensory inputs and motor output to identify balance deficits. Physical Therapy, 89(5), 484–498.

Franchignoni, F., Horak, F., Godi, M., Nardone, A., & Giordano, A. (2010). Using psychometric techniques to improve the Balance Evaluation Systems Test: the mini-BESTest. Journal of Bodywork and Movement Therapies, 14(3), 311–319.

tug-test berg-balance-scale dynamic-gait-index functional-reach-test


MNA

Yves Guigoz, Bruno Vellas, Paul J. Garry

The Mini Nutritional Assessment is a simple, rapid, and non-invasive screening tool designed to identify malnutrition and nutritional risk in older adults. Developed by Guigoz, Vellas, and colleagues in 1994, it combines subjective assessment with objective anthropometric and laboratory measurements. It is widely used in clinical practice, research, and community settings to detect nutritional decline and guide intervention.

Clinician-administered questionnaire + anthropometric measurement

Guigoz, Y., Vellas, B., & Garry, P. J. (1994). Mini Nutritional Assessment: A practical assessment tool for grading the nutritional state of elderly patients. Facts and Research in Gerontology, Supplement 2, 15-59.

Vellas, B., Villars, H., Abellan, G., et al. (2006). Overview of the MNA-Its history and challenges. The Journal of Nutrition, Health & Aging, 10(6), 456-465.

nutrition-self-efficacy-scale dietary-quality-index mediterranean-diet-adherence food-frequency-questionnaire body-weight-image-satisfaction


Minimalist Program

The Minimalist Program (MP) is a framework for generative syntax developed by Noam Chomsky in 1995, designed to explain linguistic structure while assuming the fewest possible theoretical mechanisms. The program seeks principles that are simple, elegant, and motivated by language evolution. It addresses core questions: What principles explain language structure? Why do languages vary? Why do humans have language? The MP has become the dominant paradigm in theoretical syntax, though it remains controversial and subject to ongoing refinement.

Chomsky, N. (1995). The Minimalist Program. Cambridge, MA: MIT Press.

Chomsky, N. (2000). Minimalist inquiries: The framework. In R. Martin, D. Michaels, & J. Uriagereka (Eds.), Step by Step: Essays on Minimalist Syntax. Cambridge, MA: MIT Press.

Radford, A. (2009). Minimalist Syntax: Exploring the Structure of English. Cambridge: Cambridge University Press.


Minimum Inhibitory Concentration Assay

MIC assay
MIC determination
broth microdilution MIC test
antimicrobial susceptibility assay

Mid-20th century (standardised ~1970s–1980s; widely adopted in food science from 1990s onward)

Multiple contributors; broth dilution principles codified by CLSI (formerly NCCLS) and EUCAST

The Minimum Inhibitory Concentration (MIC) assay is a quantitative in vitro method that determines the lowest concentration of an antimicrobial agent — such as a food preservative, essential oil, or synthetic antibiotic — that visibly inhibits the growth of a target microorganism. Widely used in food science, microbiology, and pharmaceutical research, it provides a reproducible numerical threshold that guides formulation, safety assessment, and regulatory compliance decisions.

Optical density readings or visual turbidity scores, continuous or ordinal

Clinical and Laboratory Standards Institute (CLSI). (2018). Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria That Grow Aerobically, 11th ed. CLSI standard M07. Wayne, PA: CLSI.

Balouiri, M., Sadiki, M., & Ibnsouda, S. K. (2016). Methods for in vitro evaluating antimicrobial activity: A review. Journal of Pharmaceutical Analysis, 6(2), 71-79.

minimum-bactericidal-concentration disk-diffusion-assay biofilm-quantification time-kill-kinetics antimicrobial-activity-testing natural-preservative-screening


Minimum Number of Individuals

Minimum number of individuals (MNI) is a quantitative zooarchaeological method that estimates the minimum number of animals represented in a faunal assemblage based on the frequency of unique skeletal elements. Developed by Theodore White in 1953, it is one of the most widely used techniques for analyzing animal bone assemblages from archaeological sites. The MNI method helps archaeologists understand hunting and butchering patterns, interpret subsistence practices, and assess the diversity of fauna exploited by past human populations.

White, T. E. (1953). A method of calculating the dietary percentages of various food animals utilized by aboriginal peoples. American Antiquity, 19(4), 396-398.

Grayson, D. K. (1984). Quantitative Zooarchaeology. Academic Press.

Lyman, R. L. (1994). Vertebrate Taphonomy. Cambridge University Press.

number-of-identified-specimens geometric-morphometrics dental-microwear-texture-analysis use-wear-analysis


The Minnesota Living with Heart Failure Questionnaire (MLHFQ) is a 21-item self-report measure that quantifies the multidimensional burden of heart failure on patients' daily living and quality of life. Developed by Rector, Kubo, and Cohn in 1987, the MLHFQ is the most widely used disease-specific QoL instrument in heart failure research and clinical practice, valued for its brevity, sensitivity to treatment response, and predictive value for prognosis.

Rector, T. S., Kubo, S. H., & Cohn, J. N. (1987). Patients' self-assessment of their congestive heart failure. Part 2: Content, reliability and responsiveness of a new measure, the Minnesota Living with Heart Failure Questionnaire. Heart Failure, 3(5), 198–209.

Rector, T. S., Anand, I. S., & Cohn, J. N. (1992). Assessing the patient's perspective on their functioning and well-being in heart failure. Heart Failure Reviews, 5(2), 261–270.

kansas-city-cardiomyopathy seattle-angina-questionnaire new-york-heart-association-class duke-activity-status-index


Minnesota Satisfaction Questionnaire

David J. Weiss, René V. Dawis, George W. England, and Lloyd H. Lofquist

The Minnesota Satisfaction Questionnaire (MSQ), developed by Weiss, Dawis, England, and Lofquist in 1967, is a widely used measure of job satisfaction emphasizing intrinsic and extrinsic satisfaction dimensions. Available in long-form (100 items) and short-form (20 items) versions, the MSQ assesses satisfaction with diverse job aspects including achievement, compensation, advancement, and security. It remains a foundational instrument in vocational and organizational psychology.

Weiss, D. J., Dawis, R. V., England, G. W., & Lofquist, L. H. (1967). Manual for the Minnesota Satisfaction Questionnaire. Minneapolis: University of Minnesota.

Lofquist, L. H., & Dawis, R. V. (1969). Adjustment to work: A psychological view of man's problems in a work-oriented society. New York: Appleton-Century-Crofts.

job-satisfaction-survey job-demands-resources-scale emotional-exhaustion-scale work-ability-index organizational-commitment-scale


Missing Data Mechanisms

Missing Data Mechanisms (MCAR, MAR, MNAR)

Missing Data Typology
Rubin's Missing Data Framework
Missingness Mechanisms
Kayıp Veri Mekanizmaları

Missing data mechanisms, introduced by Donald Rubin in 1976, provide a formal taxonomy for classifying why observations are absent from a dataset. The three categories — Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR) — describe the relationship between the probability of missingness and the observed or unobserved values. Identifying the correct mechanism is essential because it determines which analytical strategies preserve valid and unbiased inference.

Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592.


Missing Transverse Energy

MET
missing transverse momentum
invisible energy

Missing transverse energy (MET) is a powerful technique used in high-energy physics to infer the presence of invisible particles, primarily neutrinos, that escape a detector without leaving a trace. By measuring the imbalance of transverse momentum in the event, physicists can detect signatures of weakly interacting particles crucial for studying the Standard Model and searching for new physics beyond it.

Khachatryan, V., et al. (CMS Collaboration). (2014). Performance of missing transverse momentum reconstruction in proton-proton collisions at 7 TeV with ATLAS. Journal of High Energy Physics, 2012(07), 167.

Aad, G., et al. (ATLAS Collaboration). (2015). Performance of the reconstruction and identification of high-momentum isolated photons in pp collisions at s = 8 TeV with the ATLAS detector. The European Physical Journal C, 75(6), 303.

Sirunyan, A. M., et al. (CMS Collaboration). (2019). Missing transverse momentum performance of the CMS detector. Journal of Instrumentation, 12(02), P02014.


Mixed ANOVA

Mixed Between-Within Subjects Analysis of Variance

Karma ANOVA (Mixed ANOVA — Gruplar Arası × Tekrarlı)

R. A. Fisher (ANOVA framework); split-plot design formalised in agricultural experimentation

Mixed ANOVA is a parametric factorial analysis of variance that simultaneously examines at least one between-subjects factor and at least one within-subjects (repeated-measures) factor. Rooted in R. A. Fisher's ANOVA framework formalised in 1925, it is the standard method for experimental and longitudinal designs in which different groups are each measured across multiple time points or conditions.

at least one between-subjects AND at least one within-subjects

between-subjects main effect, within-subjects main effect, interaction

Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). SAGE.

one-way-anova repeated-measures-anova two-way-anova ancova manova paired-t-test


Mixed Effects Model

LME
LMM
mixed model
random effects model

A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated.

Continuous, grouped / repeated-measures / longitudinal

Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.

Pinheiro, J. C., & Bates, D. M. (2000). Mixed-Effects Models in S and S-PLUS. Springer.

hierarchical-linear-model multilevel-modeling generalized-linear-model panel-mixed-effects-model robust-mixed-effects-model bayesian-mixed-effects-model


Mixed-Integer Programming

Mixed-Integer Programming (MIP) — Mathematical optimization with continuous and integer decision variables

MIP
Mixed-Integer Linear Programming
MILP
Integer Programming

Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)

Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally.

Numerical (objective coefficients, constraint matrices, right-hand-side values)

Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York.

Wolsey, L. A. (1998). Integer Programming. Wiley-Interscience, New York.


Mixed Logit

Random Parameters Logit
Mixed Multinomial Logit
Error Components Logit
Karma Logit Modeli

The Mixed Logit model, introduced formally by McFadden and Train (2000) and elaborated in Train (2009), is a flexible discrete choice framework that allows preference parameters to vary randomly across decision-makers. By integrating standard logit probabilities over a mixing distribution of coefficients, it overcomes the restrictive independence of irrelevant alternatives (IIA) property and accommodates unobserved taste heterogeneity, panel data correlation, and complex substitution patterns across alternatives.

Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press.

McFadden, D., & Train, K. (2000). Mixed MNL models for discrete response. Journal of Applied Econometrics, 15(5), 447–470.


MMAT

Mixed methods and qualitative study quality assessment

MMAT (Mixed Methods Appraisal Tool) is a practical, design-agnostic quality assessment tool developed by Pluye et al. (2014, updated 2018) to evaluate the methodological quality of quantitative (RCTs, non-randomized studies), qualitative, and mixed-methods studies. Unlike tools designed for single paradigms (e.g., Cochrane RoB 2 for RCTs), MMAT provides unified criteria applicable across diverse research methodologies, making it particularly useful for systematic reviews incorporating multiple study designs.

Pluye, P., & Hong, Q. N. (2014). Combining the power of stories and the power of numbers: mixed methods research and mixed studies reviews. Annual Review of Public Health, 35, 29–45.

casp-rct-checklist prisma-checklist grade-evidence-profiling cochrane-risk-of-bias


Mixed Methods Matrix

MMR matrix
mixed-methods design matrix
research design classification matrix
mixed methods typology matrix

Tashakkori & Teddlie; Onwuegbuzie & Teddlie

The mixed methods matrix is a systematic framework for classifying, planning, and comparing mixed methods research designs along key dimensions such as timing (concurrent vs. sequential), priority (quantitative- vs. qualitative-dominant), and point of integration. It provides researchers with a structured map to make design decisions explicit, communicate choices transparently, and locate a study within the broader mixed methods typology.

Research design classification and planning tool

Quantitative and qualitative data; design-level metadata

Onwuegbuzie, A. J., & Teddlie, C. (2003). A framework for analyzing data in mixed methods research. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 351-383). Sage.

Tashakkori, A., & Teddlie, C. (Eds.). (2010). Sage handbook of mixed methods in social and behavioral research (2nd ed.). Sage.


Mixed Methods Meta-Inference

meta-inference
mixed methods overall inference
integrated inference
MMR meta-inference

Mixed methods meta-inference is the overarching conclusion drawn at the end of a mixed methods study by systematically combining and integrating the separate inferences produced by the quantitative and qualitative strands. It represents the highest-level interpretive act in mixed methods research: moving beyond strand-specific findings to produce a unified, coherent understanding of the research problem that neither strand could yield alone.

Quantitative and qualitative data (combined inferences from both strands)

Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.

Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of Mixed Methods in Social and Behavioral Research. Sage.


Mixed Methods Research

Karma Yöntem Araştırması (Mixed Methods)

Mixed methods research is a systematic research design in which quantitative and qualitative data are collected and analysed within a single study. Formalised by Creswell and Plano Clark (2003, 3rd ed. 2018), it offers three principal design variants — concurrent, sequential, and transformative — and strengthens findings through triangulation across both data strands.

Designing and Conducting Mixed Methods Research (2003, 3rd ed. 2018)

Concurrent / Sequential / Transformative

Creswell, J.W. & Plano Clark, V.L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.

thematic-analysis content-analysis grounded-theory survey-research regression


Mixture Design

Mixture Experiment Design (Simplex-Lattice, Simplex-Centroid, D-Optimal)

mixture experiment
simplex-lattice design
simplex-centroid design
Scheffé mixture design
Karışım Deneme Deseni (Mixture Design)

Mixture experiment design is a class of constrained experimental design in which the factors are the proportions of components in a blend, subject to the constraint that all proportions sum to one. The framework was formalised by Henry Scheffé in 1958 and covers simplex-lattice, simplex-centroid, and D-optimal mixture designs widely used in pharmaceutical formulation, food science, and materials research.

simplex-lattice, simplex-centroid, D-optimal mixture

Scheffé polynomial (linear, quadratic, cubic)

formulation research (pharmaceutical, food science, materials)

Scheffé, H. (1958). Experiments with Mixtures. Journal of the Royal Statistical Society, Series B, 20(2), 344–360.

Cornell, J. A. (2002). Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data (3rd ed.). Wiley.

response-surface-methodology central-composite-design box-behnken-design factorial-design d-optimal-design


Mixture Modeling

finite mixture model
mixture distribution model
FMM
model-based clustering

Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.

Continuous, categorical, or mixed multivariate data

McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience.

Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.

latent-class-analysis latent-profile-analysis cluster-analysis structural-equation-modeling exploratory-factor-analysis bayesian-mixture-modeling


Mixture of Experts

Uzman Karışımı (Mixture of Experts — MoE)

Mixture of Experts (MoE) is a sparse neural-network architecture, introduced by Shazeer and colleagues in 2017 with the sparsely-gated MoE layer, in which only a subset of expert sub-networks is activated for each input. As seen in models such as Switch Transformer and Mixtral, it holds computation cost fixed even as the total parameter count grows.

Sparse neural network architecture (conditional computation)

Prediction & classification (text, continuous features)

Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538

Jiang, A.Q. et al. (2024). Mixtral of Experts. arXiv.

graph-attention-network xgboost random-forest transformer feedforward-neural-network


MM-Estimator

The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved.

Two-stage S-estimator then M-estimator (redescending loss)

Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.

Koller, M. & Stahel, W. A. (2011). Sharpening Wald-type Inference in Robust Regression for Small Samples. Computational Statistics & Data Analysis, 55(8), 2504-2515.

ols-regression least-trimmed-squares least-median-squares ransac-regression theil-sen-estimator


M/M/1 Queue

Single-Server Markovian Queue
Birth-Death Queue
Poisson Queue
M/M/1 Kuyruk Modeli

The M/M/1 queue is the foundational single-server queueing model in which customers arrive according to a Poisson process with rate λ, are served one at a time by a single server with exponentially distributed service times at rate μ, and wait in an infinite-capacity first-come-first-served queue. Formalized within the Kendall notation framework by David Kendall in 1953, building on A. K. Erlang's early twentieth-century telephone traffic work, it yields closed-form steady-state performance measures when the traffic intensity ρ = λ/μ is less than one.

Kendall, D. G. (1953). Stochastic processes occurring in the theory of queues and their analysis by the method of the imbedded Markov chain. The Annals of Mathematical Statistics, 24(3), 338–354.


M/M/c Queue

Multi-Server Erlang Queue
c-Server Markovian Queue
Erlang-C Queue
Çok Sunuculu M/M/c Kuyruğu

Queueing-theory tradition; Gross & Harris

The M/M/c queue is a multi-server stochastic model in which customers arrive according to a Poisson process at rate λ, are served by c identical servers each with exponentially distributed service times at rate μ, and wait in a single common queue when all servers are busy. Systematized within classical queueing theory and thoroughly treated by Gross and Harris (1998), it extends the simpler M/M/1 model to settings with parallel servers, making it the foundational tool for capacity planning in service systems.

Gross, D., & Harris, C. M. (1998). Fundamentals of Queueing Theory (3rd ed.). Wiley.


Mini-Mental State Examination

The Mini-Mental State Examination (MMSE) is a brief, 30-point screening instrument developed by Folstein, Folstein, and McHugh in 1975 to assess cognitive function in clinical settings. It is designed to detect cognitive impairment and monitor cognitive decline over time, particularly in older adults and patients with suspected dementia. The MMSE remains one of the most widely used cognitive screening tools in primary care, neurology, and geriatric medicine worldwide.

Clinician-administered cognitive screening instrument

Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-mental state: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189-198.

Tombaugh, T. N., & McIntyre, N. J. (1992). The mini-mental state examination: A comprehensive review. Journal of the American Geriatrics Society, 40(9), 922-935.

Crum, R. M., Anthony, J. C., Bassett, S. S., & Folstein, M. F. (1993). Population-based norms for the Mini-Mental State Examination by age and educational level. JAMA, 269(18), 2386-2391.

adas-cog saint-louis-mental-status addenbrookes-cognitive-examination dementia-rating-scale frontal-assessment-battery


Mobile API-based Data Collection

Mobile Application Programming Interface-based Data Collection

mobile API data collection
smartphone API data harvesting
mobile app API research data collection
API-driven mobile data collection

Emerged from mobile computing and REST/web API proliferation (Fielding, 2000; widespread adoption ~2007–2010 with smartphone ecosystem)

Mobile API-based data collection uses mobile devices (smartphones, tablets) to query application programming interfaces — structured web endpoints that return machine-readable data — enabling researchers to gather behavioral, contextual, sensor-enriched, or platform-generated data in real time from participants in their natural environments. It combines the ubiquity of mobile hardware with the scalability and standardization of RESTful or GraphQL APIs.

Structured digital data (JSON, XML) retrieved via HTTP APIs on mobile devices

Luce, M. F., Kahn, B. E., & Malhotra, N. K. (2016). Capturing consumer experiences with mobile research methods. Journal of Consumer Research, 42(6), 949–965.

Application programming interface. Wikipedia.

api-based-data-collection mobile-experience-sampling mobile-survey web-scraping sensor-data-collection mobile-experience-sampling-method


Mobile Delphi Technique

mobile Delphi
smartphone Delphi
mDelphi
mobile consensus survey

Classic Delphi: 1950s; mobile variant: 2000s–2010s

Olaf Helmer, Norman Dalkey, Nicholas Rescher (RAND Corporation) — mobile adaptation emerged early 21st century

The Mobile Delphi Technique applies the structured, iterative Delphi consensus process through smartphone or tablet interfaces, enabling geographically dispersed expert panels to participate in multiple rounds of rating and feedback from any location. It preserves the anonymity and controlled feedback loop of the classic Delphi while reducing response latency through push notifications and mobile-optimised questionnaires.

Structured expert ratings, rankings, and open-ended responses collected via mobile devices

Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey technique. Journal of Advanced Nursing, 32(4), 1008–1015.

delphi-technique online-delphi-technique mobile-survey mobile-experience-sampling focus-group nominal-group-technique