Explainable Support Vector Machine

Explainable Support Vector Machine (XAI-augmented SVM)

Explainable SVM
Interpretable SVM
XAI-SVM
Transparent Support Vector Machine

Cortes & Vapnik (SVM); explainability layer via Lundberg & Lee (SHAP, 2017) and Ribeiro et al. (LIME, 2016)

Explainable SVM combines a trained Support Vector Machine with a post-hoc interpretability layer — typically SHAP or LIME — to produce feature-level explanations for individual predictions and global importance rankings. It retains the discriminative power of SVM while meeting transparency requirements in high-stakes domains such as medicine, finance, and law.

Tabular (continuous, binary, categorical features)

Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). 'Why should I trust you?': Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.

support-vector-machine explainable-random-forest explainable-gradient-boosting explainable-logistic-regression explainable-decision-tree explainable-naive-bayes


Explainable Text Summarization

Explainable Text Summarization (XAI-augmented Abstractive and Extractive Summarization)

XAI text summarization
interpretable summarization
transparent summarization
faithfulness-aware summarization

Explainable Text Summarization augments automatic summarization models — extractive or abstractive — with post-hoc or built-in explanation methods that reveal which source sentences, tokens, or attention patterns drove each output sentence. The goal is to audit faithfulness, detect hallucinations, and build trust in model outputs in high-stakes settings such as medical or legal document review.

Atanasova, P., Simonsen, J. G., Lioma, C., & Augenstein, I. (2020). A diagnostic study of explainability techniques for text classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3256–3274. Association for Computational Linguistics.

Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 1906–1919.


Explainable Topic Modeling

Explainable Topic Modeling (Interpretable Latent Topic Discovery)

XTM
interpretable topic modeling
transparent topic modeling
explainable LDA

Community practice (Blei et al. seminal; explainability extensions 2010s–present)

Explainable Topic Modeling combines unsupervised topic discovery — such as LDA, NMF, or neural variants like BERTopic — with interpretability tools (top-word lists, coherence scores, SHAP, attention weights) that make the learned topics transparent, auditable, and communicable to domain experts and stakeholders beyond the modeling team.

Unsupervised topic discovery + interpretability layer

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.

Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.

lda-topic-model nmf-topic-model bert-based-classification sentence-embeddings explainable-bert-based-classification topic-modeling


Explainable Transformer

Explainable Transformer (Interpretability-Augmented Transformer Model)

XAI Transformer
Interpretable Transformer
Transparent Transformer
Explainable Attention Model

Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community

An Explainable Transformer combines a standard or pre-trained Transformer architecture with post-hoc or built-in interpretability techniques — such as attention rollout, gradient-weighted attention, or SHAP — to reveal which input tokens or regions drove each prediction. The approach bridges high predictive accuracy with the transparency required in high-stakes or regulated domains.

Text, images, tabular sequences, or multimodal inputs

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.

Chefer, H., Gur, S., & Wolf, L. (2021). Transformer interpretability beyond attention visualization. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 782–791.


Explainable Variational Autoencoder

Explainable Variational Autoencoder (XVAE / Interpretable VAE)

XVAE
Interpretable VAE
Disentangled Variational Autoencoder
Explainable Generative Model

Kingma, D. P. & Welling, M. (VAE); Higgins et al. (beta-VAE for disentanglement)

An Explainable Variational Autoencoder (XVAE) extends the standard VAE framework with techniques that make its latent space interpretable: disentangling latent dimensions so each corresponds to a human-understandable factor, or post-hoc attribution methods (SHAP, integrated gradients) that trace reconstructions back to input features. It retains the VAE's generative power while adding transparency required in scientific and high-stakes applications.

Generative model with interpretable latent space

Images, time-series, tabular, or mixed continuous data

Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).

Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., & Lerchner, A. (2017). beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. In Proceedings of the 5th International Conference on Learning Representations (ICLR 2017).


Explainable Vision Transformer

Explainable Vision Transformer (XViT / ViT with Post-hoc Attribution)

XViT
Interpretable ViT
Explainable ViT
Transparent Vision Transformer

Chefer, H., Gur, S., & Wolf, L. (attribution framework); Dosovitskiy et al. (base ViT)

Explainable Vision Transformer combines the strong image-recognition performance of Vision Transformers (ViT) with attribution techniques — such as relevance propagation, attention rollout, or gradient-weighted attention — that highlight which image regions drive each prediction. The approach enables researchers and practitioners to audit model decisions and satisfy transparency requirements without sacrificing accuracy.

Post-hoc explainability applied to Vision Transformer

Image, video, or patch-structured visual data

Chefer, H., Gur, S., & Wolf, L. (2021). Transformer interpretability beyond attention visualization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 782–791.

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., … Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR).


Explainable Voting Ensemble

Explainable Voting Ensemble (XAI-Augmented Voting Classifier/Regressor)

XAI voting ensemble
interpretable voting classifier
transparent voting ensemble
explainable majority vote model

Composite: voting ensemble (Dietterich, 2000) + XAI frameworks (Ribeiro et al., 2016; Lundberg & Lee, 2017)

An Explainable Voting Ensemble combines predictions from multiple diverse base models through majority vote (hard voting) or averaged probabilities (soft voting), then applies post-hoc or ante-hoc XAI techniques — such as SHAP values, LIME, or permutation importance — to produce feature-level explanations for the combined model's decisions. The goal is to retain the accuracy gains of ensemble aggregation while meeting interpretability requirements in high-stakes or regulated applications.

Ensemble with post-hoc or ante-hoc interpretability

Tabular (continuous, categorical, binary, ordinal)

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.

Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1–2), 1–39.

voting-ensemble stacking-ensemble explainable-random-forest explainable-gradient-boosting shap-analysis bagging


Explainable XGBoost

Explainable XGBoost (XGBoost with SHAP-based Interpretability)

XGBoost + SHAP
interpretable XGBoost
XAI-XGBoost
transparent gradient boosting

Chen & Guestrin (XGBoost); Lundberg & Lee (SHAP for trees)

Explainable XGBoost pairs the high predictive accuracy of XGBoost gradient-boosted trees with SHAP (SHapley Additive exPlanations) values to make each prediction fully auditable. The result is a model that matches or surpasses neural networks on tabular data while offering theoretically grounded, per-prediction feature attributions that satisfy both scientific transparency and regulatory demands.

Interpretable ensemble (gradient-boosted trees + SHAP)

Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67.

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.

xgboost explainable-gradient-boosting explainable-random-forest explainable-lightgbm gradient-boosting random-forest


Explanatory Research

analytical research
causal research
explanatory study
explanatory quantitative research

1960s–1980s (codified in behavioral and social science methodology)

Formalized by Earl Babbie and Fred Kerlinger among others

Explanatory research is a non-experimental quantitative research design that goes beyond describing a phenomenon to identifying why it occurs — examining the relationships or mechanisms that account for observed patterns. Rooted in positivist social science methodology, it uses theory-driven hypotheses and statistical analysis to test whether specific variables explain variation in an outcome, without necessarily manipulating those variables.

Non-experimental quantitative research design

Quantitative survey, secondary data, archival records

Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston.

Babbie, E. (2010). The Practice of Social Research (12th ed.). Wadsworth/Cengage Learning.

correlational-research causal-comparative-research confirmatory-research hypothesis-testing-research descriptive-research exploratory-quantitative-research


Explanatory Sequential Mixed Methods Design

Explanatory Sequential Mixed Methods Research Design

explanatory sequential design
QUAN → qual design
two-phase explanatory design
sequential explanatory design

2007 (formalized in Creswell & Plano Clark's mixed methods typology)

The explanatory sequential mixed methods design is a two-phase research approach in which a quantitative study is conducted first, and qualitative data are then collected specifically to help explain or elaborate the initial quantitative results. The quantitative phase carries greater priority; the qualitative phase is purposefully built around the findings — such as surprising results, outliers, or statistically significant relationships — that need deeper interpretation.

Quantitative data (Phase 1) followed by qualitative data (Phase 2)

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

Plano Clark, V. L., & Creswell, J. W. (2007). The Mixed Methods Reader. Sage.


EFA

common factor analysis
açımlayıcı faktör analizi
factor analysis

Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.

Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.

confirmatory-factor-analysis pca sem cronbach-alpha


Exploratory Quantitative Research

Exploratory Quantitative Research Design

quantitative exploratory design
exploratory survey research
initial quantitative investigation
preliminary quantitative study

Mid-20th century (codified in social research methods texts c. 1950s–1970s)

Earl Babbie; John Creswell (systematic codification in social science methods)

Exploratory quantitative research is a non-experimental design used when a phenomenon is insufficiently understood to support formal hypothesis testing. The researcher collects numerical data — typically through surveys, structured observation, or existing records — to describe distributions, detect patterns, and generate hypotheses that more targeted confirmatory studies can subsequently test. It occupies the first stage of a cumulative quantitative research programme.

Numerical data from surveys, structured observation, archival records

Babbie, E. (2021). The Practice of Social Research (15th ed.). Cengage Learning.

Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage.

descriptive-research correlational-research confirmatory-research survey-research cross-sectional-research hypothesis-testing-research


Exploratory Sequential Mixed Methods Design

QUAL → QUAN design
exploratory sequential design
instrument-development design
theory-building mixed methods

The exploratory sequential mixed methods design is a two-phase research framework in which a qualitative phase is conducted first to explore a poorly understood phenomenon, and the findings then inform a subsequent quantitative phase — typically to develop and test a survey instrument, measure a theory, or generalize qualitative insights to a larger population. The qualitative strand guides what is measured; the quantitative strand tests or extends those findings at scale.

Qualitative data first (interviews, focus groups, observations), then quantitative data (surveys, instruments)


Exploratory Structural Equation Modeling (ESEM) is a hybrid approach that combines exploratory factor analysis (EFA) with confirmatory factor analysis (CFA) and path modeling, developed by Asparouhov and Muthén (2009). ESEM relaxes restrictive zero-loading assumptions of traditional CFA, allowing all indicators to load on all factors, which can reveal cross-factor complexity and improve model fit while retaining the ability to test substantive structural theories.

Hybrid exploratory-confirmatory factor modeling

Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16(3), 397-438.

Marsh, H. W., Lüdtke, O., Muthén, B., Asparouhov, T., Morin, A. J., Trautwein, U., & Nagengast, B. (2010). A new perspective on structural equation modeling: Guest editors' introduction. Structural Equation Modeling, 17(3), 357-370.

Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17(3), 313-335.

pls-sem wordscores wordfish latent-transition-analysis rule-space-methodology


Exponential Random Graph Model

Exponential Random Graph Model (ERGM / p*)

ERGM
p-star model
p* model
Üstel Rastgele Graf Modeli (ERGM / p*)

1986 (foundational); modern ERGM framework 1996–2007

Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)

The Exponential Random Graph Model (ERGM), also known as the p* model, is a statistical framework for network analysis that models the probability of an observed network as a function of its local structural features — such as reciprocity, triangles, and degree distribution. Developed from the foundational work of Frank and Strauss (1986) and extended into the modern framework by Wasserman and Pattison (1996) and Robins et al. (2007), ERGM is the inferential standard for social network analysis, capable of testing whether observed network structures arise by chance or reflect genuine social processes.

Markov chain Monte Carlo maximum likelihood (MCMC-MLE)

Output: Coefficient estimates for network structural configurations (edges, reciprocity, triangles, node covariates)

Directed, undirected, weighted variants all supported

Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173-191.

Lusher, D., Koskinen, J., & Robins, G. (Eds.) (2012). Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press.

text-network-analysis gnn graph-attention-network community-detection dbscan causal-discovery


Exposure and Response Prevention

Exposure and Response Prevention Therapy

Exposure and Response Prevention (ERP) is a behavioral intervention designed to reduce anxiety and compulsive behaviors by having clients repeatedly confront feared situations or intrusive thoughts without engaging in safety behaviors or compulsions. Developed by Edna B. Foa and colleagues in the 1980s, ERP is now considered the gold-standard treatment for obsessive-compulsive disorder (OCD) and is also highly effective for anxiety disorders, PTSD, and specific phobias.

Foa, E. B., & Kozak, M. J. (1986). Emotional processing of fear: Exposure to corrective information. Psychological Bulletin, 99(1), 20–35.

Foa, E. B., Yadin, E., & Lichner, T. K. (2012). Exposure and response (ritual) prevention for OCD: Therapist guide. Oxford University Press.


EXPROM-I

EXPROM-I (EXPROM I — Extended PROMETHEE (partial)) is a outranking multi-criteria decision-making (MCDM) method introduced by Diakoulaki, D., Koumoutsos, N. in 1991. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Diakoulaki, D., Koumoutsos, N. (1991). Cardinal ranking of alternative actions: Extension of the PROMETHEE method. European Journal of Operational Research

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


EXPROM-II

EXPROM-II (EXPROM II — Total ranking variant) is a outranking multi-criteria decision-making (MCDM) method introduced by Diakoulaki, D., Koumoutsos, N. in 1991. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


Extended Kalman Filter

The Extended Kalman Filter (EKF) is the nonlinear generalization of the Kalman Filter, extending the linear state estimation algorithm to nonlinear systems through local linearization. Developed by Bucy in the early 1960s, the EKF has become the workhorse for state estimation in nonlinear systems across robotics, aerospace, and navigation, enabling real-time processing of noisy measurements from nonlinear sensors and dynamics.

Bucy, R. S. (1961). A linear approximation to the solution of nonlinear filtering equations. Technical Report No. 32-486, Jet Propulsion Laboratory.

Bar-Shalom, Y., Li, X. R., & Kirubarajan, T. (2001). Estimation with Applications to Tracking and Navigation. Wiley-Interscience.

Welch, G., & Bishop, G. (2006). An Introduction to the Kalman Filter. UNC-CH Technical Report.


Extra Trees

Extremely Randomized Trees (Extra-Trees)

Extremely Randomized Trees
ExtraTreesClassifier
ExtraTreesRegressor
ET

Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time.

Ensemble (extremely randomized decision trees)

Tabular (continuous, categorical, mixed)

Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.

random-forest decision-tree xgboost gradient-boosting bagging support-vector-machine


Extreme Value Theory

Extreme Value Theory (GEV, GPD, Peaks-Over-Threshold)

Aşırı Değer Teorisi (EVT — GEV, GPD, POT)

Coles (textbook treatment); McNeil, Frey & Embrechts

Extreme Value Theory is a statistical framework for modelling the rare events that live in the tail of a probability distribution. As developed in Coles (2001) and applied to risk by McNeil, Frey & Embrechts (2005), it offers two standard routes: the Generalized Extreme Value (GEV) distribution for block maxima and the Generalized Pareto Distribution (GPD), used in the peaks-over-threshold approach, for exceedances above a high threshold.

Maximum likelihood fit of GEV (block maxima) or GPD (peaks over threshold)

Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer.

McNeil, A. J., Frey, R., & Embrechts, P. (2005). Quantitative Risk Management: Concepts, Techniques and Tools. Princeton University Press.

value-at-risk conditional-value-at-risk realized-volatility egarch arima


Eye-Tracking Analysis

Gaze Analysis
Eye Movement Tracking
Oculomotor Measurement

Keith Rayner and colleagues (modern cognitive applications)

Eye-tracking analysis is a method for recording and quantifying eye movements and gaze patterns during visual tasks, providing direct measures of visual attention, comprehension, and cognitive processing. Advancing from mechanical devices to high-speed infrared cameras, eye tracking enables researchers to identify where people look, for how long, and in what sequence—revealing cognitive processes underlying reading, scene perception, decision-making, and attention.

Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Weijer, J. (2011). Eye tracking: A comprehensive guide to methods and measures. Oxford University Press.

Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372-422.

Duchowski, A. T. (2007). Eye tracking methodology: Theory and practice. Springer.

pupillometry response-time-analysis visual-search attention-measurement


F-beta Score

The F-beta score is a weighted harmonic mean of precision and recall that allows customizing the relative importance of recall versus precision through a parameter beta. It generalizes the F1-score, which is the special case where beta = 1.

van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.

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.


F-LMAW

F-LMAW (Fuzzy Logarithm Methodology of Additive Weights (TFN)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Božanić, D., Pamučar, D., Milić, A., Marinković, D., Komazec, N. in 2021 crisp; 2022 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Triangular-fuzzy linguistic expert weighting with Bonferroni aggregation; logarithmic transform around an absolute anti-ideal point

Božanić, D., Pamučar, D., Milić, A., Marinković, D., Komazec, N. (2022). Modification of the Logarithm Methodology of Additive Weights (LMAW) by a Triangular Fuzzy Number and Its Application in Multi-Criteria Decision Making. Axioms

lmaw topsis vikor waspas marcos mabac edas copras


F-statistics (FST)

F-statistics for Population Differentiation and Genetic Structure

FST
Wright's F-statistics
Population differentiation index

F-statistics are a family of measures developed by Sewall Wright to quantify population genetic structure and the degree of genetic differentiation between populations. FST, the most widely used F-statistic, measures the proportion of total genetic variation attributable to differences between populations versus within populations. FST ranges from zero (no differentiation) to one (complete differentiation). These statistics have become fundamental tools for understanding population structure, detecting population admixture, and analyzing the evolutionary forces shaping genetic variation.

Wright, S. (1951). The genetical structure of populations. Annals of Eugenics, 15(4), 323–354.

Weir, B. S., & Cockerham, C. C. (1984). Estimating F-statistics for the analysis of population structure. Evolution, 38(6), 1358–1370.

Hudson, R. R., Boos, D. D., & Kaplan, N. L. (1992). A statistical test for detecting geographic subdivision in nucleotide sequences. Molecular Biology and Evolution, 9(3), 405–418.

admixture-analysis ld-block-analysis coalescent-theory selection-sweep


F1-Score (Harmonic Mean of Precision and Recall)

The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.


Face-to-face Delphi Technique

in-person Delphi
face-to-face Delphi
conventional Delphi
FtF Delphi

Norman Dalkey and Olaf Helmer (RAND Corporation)

The face-to-face Delphi Technique is a structured, iterative consensus-building method conducted through in-person sessions with a purposively selected panel of experts. Across multiple rounds, panelists independently respond to structured questionnaires, receive aggregated group feedback, and revise their judgments until acceptable consensus is reached. The face-to-face format adds direct interpersonal interaction while preserving the anonymity of individual ratings within each round.

Expert judgments, ratings, rankings (structured questionnaires across iterative rounds)

Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458–467.

Linstone, H. A., & Turoff, M. (Eds.). (1975). The Delphi Method: Techniques and Applications. Addison-Wesley.

delphi-technique online-delphi-technique focus-group face-to-face-focus-group nominal-group-technique expert-elicitation


Face-to-face Diary Method

in-person diary study
face-to-face diary study
personal diary method
paper diary method

1980s–1990s (formalized face-to-face protocol era)

Rooted in sociological and psychological diary research traditions; face-to-face protocols formalized in the late 20th century

The face-to-face diary method is a data collection technique in which participants are recruited, briefed, and supported through in-person researcher contact while keeping structured or open-ended diaries over a defined period. By combining the temporal depth of diary records with the rapport and clarity of direct researcher interaction, it reduces ambiguity in diary instructions, improves compliance, and allows the researcher to probe or clarify entries at handover or follow-up meetings.

Qualitative / mixed-methods data collection technique

Participant-generated text, narrative, and behavioral self-reports

Alaszewski, A. (2006). Using Diaries for Social Research. Sage Publications.

Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54(1), 579–616.

diary-method structured-interview face-to-face-structured-interview experience-sampling-method participant-observation field-notes


Face-to-face Field Notes

in-person field notes
observational field notes
ethnographic field notes
fieldwork notes

Early 20th century (Malinowski ~1915–1922); codified by Emerson et al. 1995

Bronislaw Malinowski (systematic ethnographic fieldwork); Robert Emerson, Rachel Fretz & Linda Shaw (contemporary methodology)

Face-to-face field notes are a foundational qualitative data collection technique in which the researcher is physically present in the setting and records observations, interactions, events, and contextual details in written form. As the canonical mode of ethnographic and observational research, in-person field notes capture the social texture, nonverbal cues, spatial arrangements, and moment-to-moment dynamics of real-world settings that remote or mediated data collection cannot fully replicate.

Written observational records, descriptive text, direct observational data

Emerson, R. M., Fretz, R. I., & Shaw, L. L. (1995). Writing Ethnographic Fieldnotes. University of Chicago Press.

Bernard, H. R. (2011). Research Methods in Anthropology: Qualitative and Quantitative Approaches (5th ed.). AltaMira Press.

participant-observation non-participant-observation ethnography face-to-face-participant-observation diary-method field-notes


Face-to-face Focus Group

in-person focus group
FGD
co-located focus group
face-to-face FGD

1940s (Merton & Lazarsfeld); systematised 1980s–1990s

Robert K. Merton and Paul Lazarsfeld (focused interview); Richard Krueger and David Morgan (applied focus group methodology)

A face-to-face focus group is a structured, moderated group discussion conducted in a shared physical space, typically with 6–10 participants who are selected because they share a relevant characteristic. The moderator follows a semi-structured topic guide to elicit opinions, perceptions, and experiences. Unlike surveys, focus groups capture social interaction — agreement, disagreement, and the group dynamics through which attitudes are formed and expressed.

Qualitative group data-collection technique

Verbal discussion data from small groups (audio/video recorded transcripts)

Krueger, R. A., & Casey, M. A. (2015). Focus Groups: A Practical Guide for Applied Research (5th ed.). Sage Publications.

Morgan, D. L. (1997). Focus Groups as Qualitative Research (2nd ed.). Sage Publications.

online-focus-group structured-interview face-to-face-semi-structured-interview face-to-face-in-depth-interview participant-observation survey


Face-to-face Participant Observation

in-person participant observation
direct participant observation
fieldwork participant observation
co-present observation

Early 20th century (Chicago School ~1920s; Spradley formalisation 1980)

Chicago School sociologists (Robert Park, Ernest Burgess); systematised by Raymond Gold (1958) and James Spradley (1980)

Face-to-face participant observation is a qualitative data collection technique in which the researcher physically enters a setting and engages with participants in real time to document social behaviour, interactions, and meaning-making as they naturally occur. Unlike online or remote variants, the researcher is bodily present, enabling direct sensory access to context, non-verbal cues, and the full texture of everyday life in the setting under study.

Field notes, observational records, informal conversations, artefacts

Spradley, J. P. (1980). Participant Observation. Holt, Rinehart and Winston.

Gold, R. L. (1958). Roles in sociological field observations. Social Forces, 36(3), 217–223.

ethnography non-participant-observation face-to-face-in-depth-interview field-notes case-study action-research


Face-to-face Research Diary

in-person research journal
fieldwork reflexive diary
face-to-face researcher journal
in-person reflexive log

1981–1989 (systematic articulation in qualitative fieldwork literature)

Robert G. Burgess (systematic research diary in fieldwork); Mary Louise Holly (professional journal writing)

A face-to-face research diary is a systematic reflexive log maintained by the researcher during in-person fieldwork. Unlike participant diaries, this is the researcher's own running record of observations, analytic thoughts, methodological decisions, and emotional responses captured during or immediately after direct, embodied encounters with participants or field settings. It serves simultaneously as a data source, an audit trail, and a reflexivity instrument within qualitative research.

Qualitative data collection and reflexivity tool

Researcher-generated narrative text, field reflections, analytic memos

Holly, M. L. (1989). Writing to Grow: Keeping a Personal-Professional Journal. Heinemann.

Burgess, R. G. (1981). Keeping a research diary. Cambridge Journal of Education, 11(1), 75–83.

research-diary field-notes face-to-face-diary-method participant-observation reflexive-thematic-analysis ethnography


Face-to-face Semi-structured Interview

in-person semi-structured interview
semi-structured personal interview
guided face-to-face interview
FFSSI

1940s onward; widely codified in the 1980s–1990s

Rooted in sociological interview traditions; systematised by researchers including Robert Merton and Paul Lazarsfeld (focused interview, 1940s) and later elaborated by Steinar Kvale

A face-to-face semi-structured interview is a qualitative data collection technique in which a researcher meets a participant in person and follows a prepared topic guide of open-ended questions while retaining the flexibility to probe, reorder, and explore emerging themes. It combines the consistency of a predetermined agenda with the depth and responsiveness of an open dialogue, making it one of the most widely used methods in qualitative and mixed-methods research across the social, health, and educational sciences.

Bryman, A. (2016). Social Research Methods (5th ed.). Oxford University Press.

Kvale, S., & Brinkmann, S. (2009). InterViews: Learning the Craft of Qualitative Research Interviewing (2nd ed.). Sage.


Face-to-face Sensor Data Collection

in-person sensor data collection
proximate biosensor data collection
face-to-face ambulatory assessment
on-site sensor recording

1990s–2000s (growth with wearable/biosensor technology)

Emerging from ambulatory assessment and wearable computing research communities

Face-to-face sensor data collection involves attaching or deploying sensors — physiological, motion, environmental, or proximity-based — on or around participants during in-person research sessions. The co-present setting allows direct researcher oversight of equipment, real-time signal monitoring, and immediate troubleshooting, yielding high-fidelity continuous or event-triggered data streams that capture objective behavioral and physiological indicators as they unfold.

Quantitative / mixed-methods data collection technique

Physiological, behavioral, and environmental sensor signals collected during in-person interaction

Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151–176.

sensor-data-collection mobile-sensor-data-collection mobile-experience-sampling face-to-face-participant-observation ecological-momentary-assessment physiological-measurement


Face-to-face structured interview

FTFSI
personal interview
in-person structured interview
face-to-face survey interview

Mid-20th century (formalized 1950s–1960s)

Cannell, Kahn, and survey methodology tradition

A face-to-face structured interview is a data collection method in which a trained interviewer meets each respondent in person and asks a fixed set of questions in a predetermined order, recording responses verbatim or using a closed-response format. It combines the response-rate advantages of personal contact with the standardization of a fixed instrument, making it a cornerstone of large-scale social, health, and policy surveys.

Quantitative / mixed-methods data collection

Verbal responses to fixed questions (categorical, ordinal, scalar)

Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey Methodology (2nd ed.). Wiley.

Fowler, F. J. (2009). Survey Research Methods (4th ed.). Sage.

structured-interview semi-structured-interview online-structured-interview telephone-assisted-structured-interview survey face-to-face-survey


Face-to-face Survey

Face-to-face Survey (Interviewer-Administered Questionnaire)

personal interview survey
in-person survey
PAPI survey
door-to-door survey

Established practice formalised in survey methodology (Gallup, Likert, and others from the 1930s–1940s)

A face-to-face survey is a structured data collection method in which a trained interviewer meets respondents in person and administers a standardised questionnaire. The interviewer reads questions aloud, clarifies wording when permitted by protocol, and records answers — either on paper (PAPI) or a laptop/tablet (CAPI). This mode consistently achieves higher response rates and better data quality for complex or sensitive questionnaires than self-administered alternatives, and is the reference standard in large-scale population surveys.

Quantitative / mixed-mode data collection

Structured questionnaire responses (numerical ratings, categorical choices, open-ended text)

Fowler, F. J. (2014). Survey Research Methods (5th ed.). Sage Publications.

survey structured-interview online-survey telephone-assisted-survey mobile-survey computer-assisted-personal-interviewing


Facility Layout (SLP)

Systematic Layout Planning (SLP) is a structured methodology developed by Richard Muther in the 1960s–1970s for designing optimal plant and facility layouts. The approach systematizes the consideration of material flow, personnel movement, equipment relationships, and space constraints to minimize material handling costs, improve safety, and enhance flexibility. SLP has become the foundational framework for facility design in manufacturing, warehousing, and service environments.

Muther, R. (1973). Systematic layout planning (2nd ed.). Boston: Cahners Books.

Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. (2010). Facilities planning (4th ed.). Hoboken, NJ: John Wiley & Sons.

assembly-line-balancing job-shop-scheduling scor-model kanban cross-docking


FACIT-Palliative Subscale

Functional Assessment of Chronic Illness Therapy–Palliative subscale

FACIT-Pal
FACIT-Palliative
FACIT-Spiritual subscale

Peterman, Fitchett, Brady, and colleagues (funded by National Cancer Institute)

The FACIT-Palliative (FACIT-Pal) is a 12-item self-report subscale of the Functional Assessment of Chronic Illness Therapy (FACIT) family, specifically designed to measure spiritual well-being and existential meaning in patients with advanced cancer and life-limiting illness. Developed by Peterman and colleagues in 2002 and funded by the National Cancer Institute, the FACIT-Pal is embedded within larger FACIT instruments and has become a standard spiritual quality-of-life measure in oncology trials, hospice research, and palliative care programs internationally.

Peterman, A. H., Fitchett, G., Brady, M. J., Hernandez, L., & Cella, D. (2002). Measuring spiritual well-being in people with cancer: The Functional Assessment of Chronic Illness Therapy–Spiritual Well-Being scale. Annals of Behavioral Medicine, 24(1), 49–58.

Cella, D., Peterman, A., Passik, S., Jacobsen, P., & Breitbart, W. (2009). Progress toward guidelines for the management of fatigue. Oncology, 12(11A), 369–377.

spiritual-wellbeing-scale mcgill-quality-of-life patient-dignity-inventory palliative-performance-scale comfort-care-checklist


FACT-Anemia

Functional Assessment of Cancer Therapy—Anemia

cancer-specific quality of life, treatment side effects

Yellen, S. B., Cella, D. F., et al. (built on Cella, D. F. FACT-G framework)

The FACT-Anemia (FACT-An) is a quality-of-life measure combining the 27-item FACT-G core with a disease-specific subscale focusing on fatigue and anemia-related symptoms common in cancer patients receiving chemotherapy or dealing with cancer-induced anemia. Developed by Yellen et al. in 1997, it quantifies the impact of chemotherapy-induced anemia on functional and emotional well-being, supporting clinical trials and anemia treatment research.

Yellen, S. B., Cella, D. F., Webster, K., Blendowski, C., & Kaplan, E. (1997). Measuring fatigue and other anemia-related symptoms with the Functional Assessment of Cancer Therapy (FACT) measurement system. J Pain Symptom Manage, 13(2), 63–74.

Cella, D. F., Tulsky, D. S., Gray, G., Sarafian, B., Linn, E., Bonomi, A., et al. (1993). The Functional Assessment of Cancer Therapy scale: development and validation of the general measure. J Clin Oncol, 11(3), 570–579.

fact-lung fact-colorectal fact-prostate cancer-worry-scale eortc-qlq-c15-pal


FACT-B

The FACT-B is a comprehensive 36-item disease-specific quality-of-life instrument that integrates the generic FACT-G (27 items covering physical, social, emotional, and functional well-being) with a 9-item breast-cancer-specific subscale addressing body image, sexual function, arm symptoms, and treatment side effects. Developed by Brady et al. in 1997, the FACT-B is the gold-standard QoL measure for breast cancer research and clinical practice, used in hundreds of clinical trials and enabling comparison across breast cancer populations and treatment modalities.

Patient self-report disease-specific QoL instrument

Brady, M. J., Cella, D. F., Mo, F., et al. (1997). Reliability and validity of the Functional Assessment of Cancer Therapy-Breast quality-of-life instrument. J Clin Oncol, 15(3), 974–986.

Cella, D. F., Tulsky, D. S., Gray, G., et al. (1993). The Functional Assessment of Cancer Therapy scale: development and validation of a general measure. J Clin Oncol, 11(3), 570–579.

fact-g distress-thermometer edmonton-symptom-assessment memorial-symptom-assessment-scale functional-living-index-cancer


FACT-Colorectal

Functional Assessment of Cancer Therapy—Colorectal

Ward, W. L., et al. (built on Cella, D. F. FACT-G framework)

The FACT-Colorectal (FACT-C) is a disease-specific quality-of-life instrument designed for patients with colorectal cancer. It combines the 27-item FACT-G core (general cancer) with a 9-item colorectal-specific subscale addressing bowel function, sexual function, and cancer-related digestive concerns. Validated by Ward et al. in 1999, it is a standard endpoint in colorectal cancer clinical trials and clinical practice monitoring.

Ward, W. L., Hahn, E. A., Mo, F., Hernandez, L., Tulsky, D. S., & Cella, D. (1999). Reliability and validity of the Functional Assessment of Cancer Therapy-Colorectal (FACT-C) quality of life instrument. Qual Life Res, 8(3), 181–195.

fact-lung fact-prostate fact-ovarian eortc-qlq-c15-pal cancer-worry-scale


FACT-G

Functional Assessment of Cancer Therapy-General

The FACT-G is a 27-item self-report questionnaire measuring health-related quality of life in cancer patients across four key domains: physical, social/family, emotional, and functional well-being. Developed by Cella et al. in 1993, it has become one of the most widely used generic QoL instruments in oncology research and clinical practice, translated into 40+ languages and validated across diverse cancer populations.

fact-b-breast-cancer edmonton-symptom-assessment distress-thermometer piper-fatigue-scale brief-fatigue-inventory


FACT-Lung

Functional Assessment of Cancer Therapy—Lung

The FACT-Lung (FACT-L) is a lung-cancer-specific quality-of-life measure that combines a 27-item general cancer assessment with a 7-item lung cancer subscale. Developed by Cella et al. in 1995, it quantifies physical, emotional, social, and functional well-being specifically relevant to lung cancer patients. It is widely used in clinical trials and practice to assess treatment impact and symptom burden.

Cella, D. F., Bonomi, A. E., Lloyd, S. R., Tulsky, D. S., Kaplan, E., & Bonomi, P. (1995). Validation of the Functional Assessment of Cancer Therapy-Lung (FACT-L) quality of life instrument for patients with lung cancer. J Clin Oncol, 13(1), 142–153.

fact-colorectal fact-prostate fact-ovarian eortc-qlq-lc13 cancer-worry-scale


FACT-Ovarian

Functional Assessment of Cancer Therapy—Ovarian

Basen-Engquist, K., et al. (built on Cella, D. F. FACT-G framework)

The FACT-Ovarian (FACT-O) is a disease-specific quality-of-life measure for women with ovarian cancer, integrating the 27-item FACT-G core with a 12-item ovarian-specific subscale addressing cancer-related symptoms, sexual function, abdominal distension, and treatment side effects. Validated by Basen-Engquist et al. in 2001, it is a standard endpoint in ovarian cancer clinical trials and supportive care research.

Basen-Engquist, K., Bodurka, D. C., Lu, H., Sill, M. W., Thaker, P. H., Deavers, M. T., et al. (2001). Reliability and validity of the Functional Assessment of Cancer Therapy-Ovarian (FACT-O) quality of life instrument. Gynecol Oncol, 89(3), 478–488.

fact-lung fact-colorectal fact-prostate eortc-qlq-br23 cancer-worry-scale


FACT-Prostate

Functional Assessment of Cancer Therapy—Prostate

Esper, P., et al. (built on Cella, D. F. FACT-G framework)

The FACT-Prostate (FACT-P) is a disease-specific quality-of-life instrument for men with prostate cancer, combining the 27-item FACT-G core with a 12-item prostate-specific subscale addressing urinary, sexual, and bowel function concerns. Developed and validated by Esper et al. in 1997, it is a standard outcome measure in prostate cancer clinical trials and clinical practice.

Esper, P., Mo, F., Chodak, G., Sinner, M., Cella, D., & Pienta, K. J. (1997). Measuring quality of life in men with prostate cancer using the Functional Assessment of Cancer Therapy-Prostate instrument. Urology, 50(6), 920–928.

fact-lung fact-colorectal fact-ovarian ucla-prostate-cancer-index cancer-worry-scale


Factor Analysis for Scale Development

Exploratory Factor Analysis Method for Psychometric Scale Construction and Validation

Exploratory factor analysis
EFA for scale development
Factorial structure analysis

Exploratory factor analysis (EFA) is a statistical method for discovering the underlying dimensional structure of a set of items or variables. Pioneered by Louis Thurstone in the mid-20th century, EFA is widely used to develop and validate psychometric scales by identifying groups of items that correlate together, thereby revealing latent dimensions of the construct being measured. The method reduces item sets to a smaller number of interpretable factors.

Thurstone, L. L. (1947). Multiple-Factor Analysis: A Development and Expansion of the Vectors of Mind (2nd ed.). Chicago: University of Chicago Press.

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299.

DeVellis, R. F. (2016). Scale Development: Theory and Applications (4th ed.). Thousand Oaks, CA: Sage Publications.

likert-scale-construction guttman-scale content-validity-ratio confirmatory-factor-analysis-scale floor-ceiling-effect


Factor Analysis

Exploratory and Confirmatory Factor Analysis

Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.

Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.

Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.

Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151.


Factor Risk Model

Multi-Factor Risk Model (Fama-French, Arbitrage Pricing Theory)

Fama-French model
Fama-French three-factor model
Fama-French five-factor model
arbitrage pricing theory
APT
multi-factor model
Faktör Risk Modeli (Fama-French, APT)

Fama & French (factor model); Ross (Arbitrage Pricing Theory)

A factor risk model is a multi-factor framework that links asset returns to systematic risk factors such as the market, value, size, and momentum. The Fama-French three- and five-factor models (1993) and Ross's Arbitrage Pricing Theory (1976) decompose portfolio risk and detect alpha.

Time-series OLS regression of excess returns on risk factors

Fama, E. F., & French, K. R. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 33(1), 3-56.

Ross, S. A. (1976). The Arbitrage Theory of Capital Asset Pricing. Journal of Economic Theory, 13(3), 341-360.

principal-component-risk portfolio-optimization-mean-variance ols-regression credit-risk-models stochastic-volatility-model


Factorial A/B Test

Factorial A/B Test (Multi-Factor Online Experiment)

factorial split test
multi-factor A/B test
factorial online experiment
factorial controlled experiment

Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s

Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s

A factorial A/B test is a controlled online experiment that simultaneously manipulates two or more independent factors, each at two or more levels, exposing different user groups to every combination of factor levels. Rooted in Fisher's factorial design and operationalised at scale by tech companies, it enables researchers to estimate both the independent main effect of each factor and the interaction effects between factors — all from a single experimental run.

Binary or continuous outcome metrics (clicks, conversions, revenue, engagement); user-level or session-level observations

Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press.

Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.

factorial-experiment ab-design full-factorial-experiment fractional-factorial-experiment multi-arm-experiment adaptive-ab-test


Factorial ABA Design

Factorial reversal design
Multi-factor ABA design
Factorial withdrawal design
SCED factorial ABA

1968 (ABA base); factorial extensions developed through 1980s–2000s

Derived from ABA reversal design (Baer, Wolf & Risley, 1968) extended with factorial manipulation principles

The Factorial ABA design embeds a factorial treatment structure within the ABA reversal framework. Rather than testing a single treatment against baseline, the researcher systematically varies two or more independent variables (factors) across treatment phases, using the ABA withdrawal logic to establish experimental control. This makes it possible to examine main effects and interactions among treatment components within a single-case or small-N experimental context.

Single-case experimental design with factorial treatment structure

Repeated-measures behavioral or continuous outcome data collected on individual participants

Kratochwill, T. R., & Levin, J. R. (Eds.). (2010). Single-Case Intervention Research: Methodological and Statistical Advances. American Psychological Association.

Kennedy, C. H. (2005). Single-Case Designs for Educational Research. Allyn & Bacon.


Factorial ABAB Design

factorial reversal design
factorial withdrawal design
multi-factor ABAB design
factorial single-subject reversal

1960s–1970s (integration of factorial and single-subject reversal traditions)

Derived from Sidman (1960) reversal logic and Fisher & Yates factorial principles; systematized in applied behavior analysis

The factorial ABAB design embeds a factorial structure within the classical ABAB reversal framework, enabling a single participant or a small set of participants to experience multiple factor combinations across alternating baseline (A) and treatment (B) phases. By systematically withdrawing and reinstating treatment conditions that vary across two or more factors, the design allows examination of both main effects and interactions at the individual level, providing strong experimental control through within-subject replication.

Repeated behavioral measures over time (continuous observation data)

Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.

Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied Behavior Analysis (3rd ed.). Pearson.

abab-design aba-design factorial-experiment multiple-baseline-design single-subject-experimental-design alternating-treatments-design


Factorial Control Group Experimental Design

Factorial Experiment with Control Group Design

factorial controlled experiment
factorial design with control
factorial RCT with control arm
multi-factor controlled experiment

A factorial control group experimental design crosses two or more independent variables (factors) in a fully factorial structure while including at least one condition that serves as a no-treatment or standard-treatment control. This allows researchers to simultaneously estimate the main effect of each factor, their interactions, and the size of those effects relative to a meaningful baseline, maximising both causal precision and experimental efficiency.

Continuous, ordinal, or categorical outcome measures

Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.

factorial-experiment full-factorial-experiment randomized-controlled-trial control-group-experimental-design fractional-factorial-experiment factorial-randomized-controlled-trial


Full Factorial Design

Full Factorial Experimental Design (2^k)

Faktöriyel Deneme Deseni (Full Factorial, 2^k)

A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured.

Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.

fractional-factorial response-surface-methodology one-way-anova two-way-anova randomized-complete-block-design taguchi-methods


Factorial Experiment

factorial design
factorial ANOVA design
multi-factor experiment
crossed-factor design

A factorial experiment is an experimental design in which two or more independent variables (factors) are manipulated simultaneously, and every combination of their levels is tested. Introduced by Ronald Fisher in the 1920s–1930s, it is the standard approach whenever a researcher needs to detect not only the main effect of each factor but also whether the effect of one factor depends on the level of another — the interaction effect.

Continuous or categorical outcome measures; manipulated categorical or continuous factors

full-factorial-experiment fractional-factorial-experiment randomized-controlled-trial analysis-of-variance response-surface-methodology latin-square-design


Factorial Field Experiment

factorial design in the field
field factorial design
multi-factor field trial
factorial field trial

1920s–1935 (Fisher's foundational work); widely applied through 20th century

Ronald A. Fisher (factorial principle); extended to field settings in agricultural and social sciences

A factorial field experiment applies factorial experimental design — simultaneously manipulating two or more independent factors across all combinations of their levels — in a real-world field setting rather than a controlled laboratory. It allows researchers to estimate both main effects and interaction effects of multiple factors on an outcome under ecologically valid conditions, making findings directly relevant to practice.

Continuous and categorical outcome measures collected in real-world field settings

Cochran, W. G., & Cox, G. M. (1957). Experimental Designs (2nd ed.). Wiley.

factorial-experiment full-factorial-experiment fractional-factorial-experiment field-experiment randomized-controlled-trial blocked-factorial-experiment


Factorial Laboratory Experiment

factorial lab experiment
laboratory factorial design
factorial controlled experiment
multi-factor lab study

1926 (Fisher's factorial principle); laboratory application systematized mid-20th century

A factorial laboratory experiment is a controlled experimental design in which two or more independent variables (factors) are simultaneously manipulated, each at two or more levels, within a laboratory setting. This design allows researchers to estimate both the individual main effect of each factor and the interaction effects between factors — making it one of the most efficient and informative designs in behavioral, psychological, and natural science research.

Quantitative (continuous or categorical outcome measures collected under controlled conditions)

Kirk, R. E. (2013). Experimental Design: Procedures for the Behavioral Sciences (4th ed.). Sage Publications.

randomized-controlled-trial between-subjects-design within-subjects-design mixed-factorial-design analysis-of-variance full-factorial-design


Factorial Multi-Arm Experiment

multi-arm factorial trial
factorial multi-arm trial
multi-arm factorial experiment
MAFT

1926 (factorial basis); multi-arm factorial trials formalized 1980s–1990s

R. A. Fisher (factorial foundations); multi-arm extension established in clinical trial methodology

A factorial multi-arm experiment simultaneously tests multiple factors (each at two or more levels) by assigning participants to distinct arms that represent unique combinations of those factors. This design efficiently estimates the independent main effects of each factor and their interactions, all within a single study — making it far more informative than running separate one-factor experiments.

Continuous, binary, or ordinal outcome measures across experimental units

Juszczak, E., Altman, D. G., Hopewell, S., & Schulz, K. (2019). Reporting of multi-arm parallel-group randomized trials: Extension of the CONSORT 2010 Statement. JAMA, 321(16), 1610–1620.

factorial-experiment multi-arm-experiment factorial-randomized-controlled-trial full-factorial-experiment fractional-factorial-experiment adaptive-multi-arm-experiment


Factorial Natural Experiment

factorial quasi-experiment
multi-factor natural experiment
factorial exogenous variation design

1920s (factorial origins, Fisher); natural experiment formalization 1990s–2000s; factorial natural experiment usage widespread 2000s–present

Extension of natural experiment tradition (Dunning, Angrist & Pischke) combined with factorial design logic (Fisher)

A factorial natural experiment exploits naturally occurring exogenous variation across two or more factors simultaneously, allowing researchers to estimate main effects and interactions without random assignment. Natural events, policy changes, or institutional rules create treatment conditions that approximate a factorial structure, enabling causal inference in observational settings where controlled experimentation is infeasible or unethical.

Observational data with multiple exogenously varying treatment dimensions (continuous or categorical)

Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press.

Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.

natural-experiment factorial-experiment factorial-randomized-controlled-trial difference-in-differences regression-discontinuity-design instrumental-variable


Factorial Pretest-Posttest Experimental Design

factorial repeated-measures pretest-posttest design

Codified by Donald T. Campbell and Julian C. Stanley

A factorial pretest-posttest experimental design combines the simultaneous manipulation of two or more independent variables (factors) with measurement of the dependent variable both before and after treatment. This structure allows researchers to assess the main effect of each factor, all possible interaction effects between factors, and the magnitude of change from pretest to posttest — all within a single, fully randomised experiment.

Continuous or ordinal outcome measures (pretest and posttest scores); categorical independent variables (factors)

Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.

Kirk, R. E. (2013). Experimental Design: Procedures for the Behavioral Sciences (4th ed.). SAGE Publications.

factorial-experiment pretest-posttest-experimental-design full-factorial-experiment split-plot-design repeated-measures-anova solomon-four-group-design


Factorial Randomized Controlled Trial

Factorial RCT
factorial trial
multi-factor RCT
factorial experiment with randomization

1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)

R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)

A factorial randomized controlled trial (factorial RCT) is an experimental design in which participants are randomly assigned to every possible combination of two or more independent factors (treatments or intervention components) simultaneously. This allows researchers to estimate the main effect of each factor and their interactions within a single, efficient trial, rather than running separate experiments for each factor.

Continuous, binary, or ordinal outcome data from randomized participants

Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014). Factorial experiments: Efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47(4), 498–504.

factorial-experiment randomized-controlled-trial fractional-factorial-experiment full-factorial-experiment adaptive-randomized-controlled-trial crossover-randomized-controlled-trial


Factorial Single-Subject Experimental Design

factorial within-subject experimental design

Applied behavior analysis tradition; systematized in Barlow & Hersen (1984) and Kazdin (1982)

A factorial single-subject experimental design applies the logic of factorial experiments — manipulating two or more independent variables simultaneously to study main effects and interactions — within a single-subject (N=1 or small N) repeated-measures framework. Instead of comparing groups, the same individual serves as their own control across systematically varied conditions, enabling fine-grained analysis of how multiple treatment components combine to influence behavior or clinical outcomes.

Experimental single-subject design with multiple independent variables

Repeated behavioral or clinical measurements on one or few individuals

Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change (3rd ed.). Pearson.


Fagan Inspection

Fagan Inspection Process for Software Quality

Fagan method
code inspection
formal review

Fagan Inspection is a formal, structured code review process developed by Michael Fagan at IBM in 1976 that systematically identifies defects before testing. Using defined roles and checklists, Fagan inspections are far more effective at catching bugs than ad-hoc reviews; studies show 70–90% defect detection rate.

Fagan, M. E. (1976). Design and code inspections to reduce errors in program development. IBM Systems Journal, 15(3), 182–211.

Fagan, M. E. (1986). Advances in software inspections. IEEE Transactions on Software Engineering, SE-12(7), 744–751.

Gilb, T., & Graham, D. (1993). Software Inspection. Addison-Wesley.


Fagerström Test for Nicotine Dependence

Fagerström Test for Nicotine Dependence - Smoking Severity Assessment

Nicotine dependence and tobacco use severity

The Fagerström Test for Nicotine Dependence (FTND) is a brief, validated self-report instrument originally developed by Fagerstrom in 1978 and revised by Heatherton and colleagues in 1991 to quantify the severity of nicotine dependence in cigarette smokers. The FTND comprises six items assessing morning cigarette use, daily cigarette consumption, and the subjective difficulty of abstaining from smoking. It is the most widely used measure of nicotine dependence in smoking cessation research and clinical practice.

Heatherton, T. F., Kozlowski, L. T., Frecker, R. C., & Fagerstrom, K. O. (1991). The Fagerström Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. British Journal of Addiction, 86(9), 1119-1127.

Fagerstrom, K. O. (1978). Measuring degree of physical dependence to tobacco smoking with reference to individualization of treatment. Addictive Behaviors, 3(3-4), 235-241.

Kozlowski, L. T., & Pill, C. K. (2007). Epidemiology, public health, and supplies of nicotine medications to treat tobacco dependence. Preventive Medicine, 45(2-3), 239-245.


Failure Mode and Effects Analysis

Failure Mode Effects and Criticality Analysis

1949 (military); widespread industrial adoption 1970s–1980s

U.S. Military / NASA (formalized by MIL-P-1629, 1949)

Failure Mode and Effects Analysis (FMEA) is a structured, proactive risk management technique used to identify potential failure modes in a system, process, or product design, evaluate their consequences, and prioritize corrective actions before failures occur. Originally developed for the U.S. military in 1949 and later adopted by NASA, automotive, and manufacturing industries, FMEA is now a cornerstone quality-engineering tool embedded in standards such as AIAG-VDA and ISO 9001-aligned processes.

Process/design knowledge, expert judgment, historical failure data

Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press.

Failure mode and effects analysis. Wikipedia.

fault-tree-analysis root-cause-analysis statistical-process-control control-chart six-sigma-dmaic reliability-analysis


Fairness-Aware ML

Algorithmic Fairness
Fair Classification
Bias-Mitigating ML
Adil Makine Öğrenmesi

Fairness-Aware Machine Learning is a family of techniques that train, constrain, or post-process predictive models so that their error rates or outcomes are equitable across protected demographic groups such as race, gender, or age. The foundational framework of equalized odds and equality of opportunity was formalized by Moritz Hardt, Eric Price, and Nati Srebro in their landmark 2016 NeurIPS paper, establishing rigorous statistical criteria for non-discriminatory classifiers.

Constrained supervised learning framework

Equalize error rates across protected groups

Equalized Odds / Equality of Opportunity

Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 29.


Faith's Phylogenetic Diversity

phylogenetic diversity
PD
evolutionary distinctiveness
branch length

Faith's Phylogenetic Diversity (PD), introduced by David Faith (1992), measures the evolutionary diversity within a community by summing the branch lengths of a phylogenetic tree connecting all species. Unlike species richness, which counts species equally regardless of evolutionary relationships, PD weights species by their evolutionary distinctiveness: a community with evolutionarily distant species has higher PD than one dominated by recently diverged species. PD is widely used in conservation to prioritize protection of species and habitats that preserve evolutionary history.

Faith, D. P. (1992). Conservation evaluation and phylogenetic diversity. Biological Conservation, 61(1), 1-10.

Steel, M., & Mooers, A. O. (2010). The expected value of shed phylogenetic diversity. PLoS Biology, 8(9), e1000475.

Redding, D. W., & Mooers, A. O. (2006). Incorporating evolutionary measures into conservation prioritization. Conservation Biology, 20(6), 1670-1678.

species-accumulation functional-diversity beta-diversity-partitioning population-viability-analysis


Fake News Detection

Fake News Detection (Misinformation Classification)

misinformation detection
false news classification
automated fact checking
Yanlış/Sahte Haber Tespiti

Fake news detection is a natural-language-processing classification task that assesses the credibility of news text and labels content as fake or genuine. Building on the social-media framing of Shu et al. (2017) and the automated-fact-checking framing of Thorne and Vlachos (2018), it turns unstructured news articles into a supervised credibility decision learned from labelled examples.

TF-IDF feature classifier / BERT-based model

Output: Credibility label (fake / real)

Shu, K. et al. (2017). Fake News Detection on Social Media. ACM SIGKDD.

Thorne, J. & Vlachos, A. (2018). Automated Fact Checking. COLING.

sentiment-analysis tf-idf bert-embeddings text-classification


Falls Efficacy Scale International

Falls Efficacy Scale International (FES-I)

FES-I
International Falls Efficacy Scale
Falls Self-Efficacy

The Falls Efficacy Scale-International (FES-I), developed by Lucy Yardley and colleagues in 2005, is a validated tool measuring fear of falling and confidence in balance in older adults and others at risk of falls. The 16-item scale assesses how confident a person feels performing daily activities without falling (self-efficacy for fall avoidance). Fear of falling is not anxiety disorder but a rational concern that, if excessive, can lead to activity restriction, deconditioning, and further fall risk. The FES-I is used internationally in clinical practice and research to identify patients at risk for this vicious cycle and guide fall prevention interventions.

Yardley, L., Beyer, N., Eklund, K., et al. (2005). Development and initial validation of the Falls Efficacy Scale-International (FES-I). Age Ageing, 34(6), 614-619.

Tinetti, M. E., Mendes de Leon, C. F., Doucette, J. T., & Baker, D. I. (1994). Fear of falling and fall-related efficacy in relationship to functioning among community-living elders. J Gerontol, 49(3), M140-M147.


Fama-MacBeth Regression

The Fama-MacBeth procedure is a two-step regression methodology for analyzing cross-sectional relationships while controlling for time-series structure. Introduced by Fama and MacBeth (1973), it first estimates time-series parameters for each cross-sectional unit, then regresses outcomes on those parameters across the cross-section, averaging results over time. This approach elegantly separates within-unit dynamics from cross-sectional heterogeneity and provides standard errors robust to panel structure.

Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Political Economy, 81(3), 607-636.

Shanken, J. (1992). On the estimation of beta-pricing models. Review of Financial Studies, 5(1), 1-33.


Family Assessment Device

Norman Epstein, Deborah Baldwin, and David Bishop

The Family Assessment Device is a widely used self-report instrument designed to measure family functioning across six key domains derived from the McMaster Model of Family Functioning. Developed by Epstein, Baldwin, and Bishop in 1983, the FAD assesses problem-solving, communication, roles, affective responsiveness, affective involvement, and behavioral control in families. It is used extensively in family therapy research, clinical assessment of family dynamics, and as an outcome measure in family-based interventions for mental health, medical, and developmental conditions.

Epstein, N. B., Baldwin, L. M., & Bishop, D. S. (1983). The McMaster Family Assessment Device. Journal of Marital and Family Therapy, 9(2), 171-180.

Miller, I. W., Ryan, C. E., Keitner, G. I., Bishop, D. S., & Epstein, N. B. (2000). The McMaster approach to families: Theory, assessment, treatment and research. Journal of Family Therapy, 22(2), 168-189.


Foot and Ankle Outcome Score

Ewa M. Roos, Mats Brandsson, H. Hugelhotz, M. Klassbo, L. Stefan Lohmander

The Foot and Ankle Outcome Score (FAOS) is a 42-item patient self-report instrument designed to assess symptoms, function, and activity limitations in individuals with foot and ankle pathology. Developed by Roos and colleagues in 2001 and published in the Journal of Orthopedic & Sports Physical Therapy, the FAOS has become the standard outcome measure in foot and ankle surgery and rehabilitation research, providing comprehensive evaluation across pain, stiffness, physical function, sport/recreation, and foot-ankle-related quality of life.

Roos EM, Brandsson M, Hugelhotz H, Klassbo M, Lohmander LS. Development and validation of the Foot and Ankle Outcome Score. J Orthop Sports Phys Ther. 2001;31(9):504-514.


FARE

FARE (Factor Relationship weighting method) is a weight objective multi-criteria decision-making (MCDM) method introduced by Žižović, M., Pamucar, D., Marinkovic, D., Žižović, M. M. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Weight_Subjective (pairwise factor relationship matrix, closed-form weights)

Žižović, M., Pamucar, D., Marinkovic, D., Žižović, M. M. (2020). Novel integrated multi-criteria ranking methodology: Case of public administration efficiency. European Journal of Operational Research

ahpsort aploco aras aroman artasi cobra cocoso codas


Fast Decoupled Power Flow

The Fast Decoupled Load Flow (FDLF) method, introduced by Stott and Alsac in 1972, exploits the weak coupling between active and reactive power in power systems to accelerate convergence beyond standard Newton-Raphson. By decoupling the equations and using constant, approximate Jacobians, it reduces computation per iteration while maintaining acceptable accuracy for most practical systems. This method remains widely used in operational software for its speed and numerical stability.

Decoupled iterative solution method for power system analysis

Stott, B., & Alsac, O. (1972). Fast decoupled load flow. IEEE Transactions on Power Apparatus and Systems, 91(3), 859-869.

Tinney, W. F., Brandwajn, V., & Chan, S. M. (1983). Sparse vector methods for small-signal and transient stability studies. IEEE Transactions on Power Apparatus and Systems, 102(7), 2137-2141.

Wood, A. J., Wollenberg, B. F., & Sheblé, G. B. (2013). Power Generation, Operation, and Control (3rd ed.). Wiley-Interscience.


Fast Multipole Method

FMM
multipole acceleration
hierarchical method

The Fast Multipole Method (FMM) is a hierarchical algorithm that reduces the computational complexity of particle interactions from O(n²) to O(n log n) or O(n), developed by Greengard and Rokhlin in 1987. By grouping distant particles and approximating their cumulative effects via multipole expansions, FMM enables efficient simulation of N-body problems, boundary integral equations, and Coulomb interactions.

Greengard, L., & Rokhlin, V. (1987). A fast algorithm for particle simulations. Journal of Computational Physics, 73(2), 325–348.

Greengard, L. (1988). The Rapid Evaluation of Potential Fields in Particle Systems. MIT Press.

Ying, L., Biros, G., & Zorin, D. (2004). A kernel-independent adaptive fast multipole method. Journal of Computational Physics, 196(2), 591–626.

barnes-hut-tree treecode boundary-element-method particle-methods


Faster R-CNN

Faster Region-based Convolutional Neural Network

Faster RCNN
Faster-RCNN
RPN-based detector
two-stage object detector
region proposal network detector

Ren, S.; He, K.; Girshick, R.; Sun, J. (Microsoft Research)

Faster R-CNN is a two-stage deep convolutional object detection framework introduced by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun (Microsoft Research) at NeurIPS 2015. It replaces the slow selective-search region proposal step used in its predecessors R-CNN and Fast R-CNN with a learned Region Proposal Network (RPN) that shares convolutional features with the detection head, enabling the first end-to-end trainable, near-real-time accurate object detector and establishing a long-standing accuracy benchmark on PASCAL VOC and MS COCO.

Object detection (localization + classification)

Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems (NeurIPS), 28, 91–99.

Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9: Convolutional Networks). MIT Press.

fast-r-cnn r-cnn yolo ssd mask-r-cnn feature-pyramid-network deformable-convolutional-networks resnet


FastText

FastText: Subword-Level Word Embeddings and Efficient Text Classification

fastText
fast text
subword embedding
character n-gram embedding
bag of tricks text classification

Joulin, A.; Bojanowski, P.; Grave, E.; Mikolov, T. (Facebook AI Research)

FastText is a word embedding and text classification framework developed by Facebook AI Research (Joulin, Bojanowski, Grave, and Mikolov, 2016–2017) that represents each word as the sum of its character n-gram vectors, allowing it to construct meaningful representations for unseen and morphologically rich words and to perform near state-of-the-art text classification orders of magnitude faster than deep neural network alternatives.

Subword embedding model and linear text classifier

Word representation & text classification

Joulin, A., Grave, E., Bojanowski, P. & Mikolov, T. (2017). Bag of Tricks for Efficient Text Classification. In Proceedings of EACL 2017, Short Papers, pp. 427–431. ACL.

Bojanowski, P., Grave, E., Joulin, A. & Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5, 135–146.

Goldberg, Y. (2017). Neural Network Methods for Natural Language Processing. Morgan & Claypool Publishers.

word2vec glove bert transformer text-cnn naive-bayes


Fault Analysis in Power Systems

Symmetrical and Unsymmetrical Fault Analysis in Power Systems

short-circuit analysis
fault current calculation
symmetrical components method

Fault analysis determines the magnitude and distribution of currents and voltages during abnormal conditions in power systems, such as short circuits. Using Fortescue's symmetrical components method (1918), engineers calculate fault currents to design protection relays and equipment ratings. It is essential for ensuring safe and reliable power system operation.

Fortescue, C. L. (1918). Method of symmetrical coordinates applied to the solution of polyphase networks. Transactions of the AIEE, 37(2), 1027-1044.

Bergen, A. R. (1986). Power System Analysis (2nd ed.). Prentice-Hall.

Saadat, H. (2010). Power System Analysis (3rd ed.). PSA Publishing.

power-flow-analysis protection-relay-coordination smart-grid-state-estimation harmonic-distortion-analysis


Fault Tree Analysis

FTA
Fault Tree Method
Top-Down Reliability Analysis
Hata Ağacı Analizi

Vesely et al. (US NRC Fault Tree Handbook)

Fault Tree Analysis (FTA) is a top-down, deductive reliability method that begins with an undesired top-level failure event and systematically traces backward through chains of contributing causes using Boolean logic gates (AND, OR). First formalized by Watson at Bell Telephone Laboratories in 1961 and later standardized by Vesely, Goldberg, Roberts, and Haasl in the landmark 1981 NRC Fault Tree Handbook, FTA has become a cornerstone of quantitative risk assessment in nuclear, aerospace, and industrial safety engineering.

Minimal cut sets and failure probability

Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook (NUREG-0492). U.S. Nuclear Regulatory Commission.


FAVAR

factor-augmented VAR
FAVAR model
Faktör Artırımlı VAR (FAVAR)

Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes

FAVAR is a multivariate time-series model that first compresses information from a very large set of variables into a few common factors, then includes those factors alongside the observed variables in a vector autoregression. It was introduced by Bernanke, Boivin and Eliasz in 2005 to study monetary policy using hundreds of macroeconomic indicators at once.

Principal-component factor extraction followed by VAR estimation

Bernanke, B. S., Boivin, J. & Eliasz, P. (2005). Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach. The Quarterly Journal of Economics, 120(1), 387-422.

Stock, J. H. & Watson, M. W. (2002). Macroeconomic Forecasting Using Diffusion Indexes. Journal of Business & Economic Statistics, 20(2), 147-162.

var-model stvar markov-switching ols-regression principal-component-analysis


FCI Algorithm

FCI
Fast Causal Inference
FCI Causal Discovery
FCI Algoritması

The Fast Causal Inference (FCI) algorithm is a constraint-based causal discovery method introduced by Spirtes, Glymour, and Scheines in their landmark 2000 book Causation, Prediction, and Search. Unlike its predecessor the PC algorithm, FCI is specifically designed to handle the presence of latent (unmeasured) common causes and sample selection bias. It outputs a Partial Ancestral Graph (PAG), which faithfully represents the set of all causal structures consistent with the observed conditional independencies.

Constraint-based causal discovery algorithm

Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press.


FDOSM

FDOSM (Fuzzy Decision by Opinion Score Method) is a ranking multi-criteria decision-making (MCDM) method introduced by Mohammed, K. I. Zaidan, A. A. Zaidan, B. B. Albahri, O. S. Albahri, A. S. Alsalem, M. A. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Linguistic multi-DM opinion aggregation using fuzzy set scoring

Mohammed, K. I., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Albahri, A. S., Alsalem, M. A. (2019). Real-time remote health-monitoring systems in a medical centre: a review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects. Journal of Medical Systems


FEA Bone Remodeling

Finite Element Analysis for Bone Remodeling

Bone remodeling simulation
Trabecular architecture adaptation
Mechano-regulation

Finite element analysis (FEA) for bone remodeling predicts how bone tissue density and architecture adapt to changes in mechanical loading over time. Pioneered by Rik Huiskes and Donald Carter in the 1980s, this computational approach integrates stress analysis with biophysical remodeling rules to simulate the long-term response of bone to disease, aging, or surgical intervention.

Huiskes, R., Weinans, H., Grootenboer, H. J., Dalstra, M., Fudala, B., & Slooff, T. J. (1987). Adaptive bone-remodeling theory applied to prosthetic-design analysis. Journal of Biomechanics, 20(11-12), 1135-1150.

Carter, D. R., Fyhrie, D. P., & Whalen, R. T. (1987). Trabecular bone density and loading history: regulation of stress-induced bone loss. Journal of Biomechanics, 20(9), 785-794.


Fear Conditioning

Classical Conditioning
Pavlovian Fear Learning

Fear conditioning is a classical (Pavlovian) learning paradigm in which a neutral stimulus (conditioned stimulus, CS—e.g., a tone or image) is repeatedly paired with an aversive outcome (unconditioned stimulus, US—e.g., mild electric shock or loud noise). After conditioning, the CS alone elicits a fear response. Fear conditioning is fundamental to understanding associative learning, anxiety disorders, and the neural bases of threat detection. Behavioral and physiological measures reveal learning acquisition, extinction, and individual differences in fear sensitivity.

Pavlov, I. P. (1927). Conditioned reflexes: An investigation of the physiological activity of the cerebral cortex. Oxford University Press.

Davis, M. (1998). Are different parts of the extended amygdala involved in fear versus anxiety? Biological Psychiatry, 44(12), 1239-1247.

Foa, E. B., & Kozak, M. J. (2006). Emotional processing of fear: Exposure to corrective information. Psychological Bulletin, 99(1), 20-35.

extinction-learning amygdala-function conditioned-stimulus unconditioned-stimulus


Fear of COVID-19 Scale

Fear of COVID-19 Infection Scale (FCV-19S)

The Fear of COVID-19 Scale (FCV-19S) is a 7-item, self-report instrument assessing fear of COVID-19 infection across cognitive, emotional, and physiological domains. Developed by Ahorsu and colleagues in 2020, it measures threat perception, anxiety symptoms triggered by disease-related triggers, and avoidance behaviors. The FCV-19S has been translated into over 40 languages and validated across diverse populations, making it the most widely used pandemic-specific fear measure globally.

Ahorsu, D. K., Lin, C. Y., Imani, V., Saffari, M., Griffiths, M. D., & Pakpour, A. H. (2020). The Fear of COVID-19 Scale: Development, initial validation, and reliability testing. International Journal of Mental Health and Addiction, 20(3), 1146–1159.


FoMO Scale

The FoMO Scale is a 10-item self-report instrument that measures the extent to which individuals experience anxiety or apprehension about missing out on social events, experiences, or information shared by others, particularly in social media contexts. Developed by Przybylski and colleagues in 2013, it quantifies this contemporary psychological phenomenon that has become increasingly relevant with the proliferation of digital communication platforms.

Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841–1848.

social-media-disorder-scale social-comparison-scale-online smartphone-addiction-scale-short technoference-scale


Federated Learning

Collaborative Learning
Decentralized Learning
FedAvg
Federe Öğrenme

Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model.

Distributed privacy-preserving machine learning

Weighted averaging of local model updates

McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282.


FEDformer

FEDformer (Frequency Enhanced Decomposed Transformer)

Frekans Tabanlı Ayrıştırılmış Dönüştürücü

FEDformer is a Transformer-based architecture for long-term multivariate time-series forecasting, introduced by Zhou et al. at ICML 2022. Its core innovation is the combination of seasonal-trend decomposition with frequency-domain attention: instead of computing full token-to-token attention in the time domain, FEDformer projects queries, keys, and values into the frequency domain via Fourier or wavelet transforms and operates on a randomly selected subset of frequency components, achieving linear complexity while preserving global temporal structure.

Frequency-domain decomposed Transformer for time-series forecasting

O(L) linear complexity via random frequency sampling

Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., & Jin, R. (2022). FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting. ICML.


Feed Conversion Ratio

Feed Conversion Ratio Assessment and Optimization

feed efficiency ratio
gain-to-feed ratio
FCR analysis

Feed conversion ratio (FCR) is a key metric of nutritional efficiency in livestock, measuring the amount of feed consumed relative to animal growth or product output. Developed by animal nutrition scientists in the mid-20th century, FCR quantifies how efficiently livestock convert dietary nutrients into meat, milk, eggs, or other products. It is a primary driver of profitability in commercial animal agriculture and a focus of genetic selection in breeding programs.

Blaxter, K. L. (1989). Energy metabolism in animals and man. Cambridge University Press.

Ferrell, C. L., Crouse, J. D., & Dickson, W. M. (1982). Feed efficiency of beef cattle: a review. Journal of Animal Science, 55(3), 527-544.

Ten Pas, A. F. (2000). Nutrient partitioning in farm animals: concepts and applications. Journal of Animal Science, 78(11), 2804-2811.


Feed Ration Formulation

Nutritionally Balanced Feed Ration Formulation and Optimization

diet formulation
ration design
nutrient balancing

Feed ration formulation is the process of selecting and blending feed ingredients to meet animal nutrient requirements while optimizing cost and feed acceptance. Formalized by veterinary nutritionists in the 1960s-1970s, the method integrates knowledge of nutrient requirements (energy, protein, minerals, vitamins) with ingredient composition and cost data. Modern ration formulation uses linear programming and decision support software to identify cost-minimizing combinations that satisfy nutritional constraints.

National Research Council. (2001). Nutrient requirements of dairy cattle (7th rev. ed.). National Academies Press.

Eckert, M. A., & Schroeder, J. W. (2008). Formulation of rations for dairy cattle. Veterinary Clinics of North America: Food Animal Practice, 24(1), 1-22.

Van Soest, P. J. (1994). Nutritional ecology of the ruminant (2nd ed.). Cornell University Press.


Feedback Linearization

Exact Linearization
Nonlinear Feedback Control
Input-Output Linearization

Feedback Linearization is a nonlinear control technique that uses a nonlinear state-feedback transformation to convert a nonlinear system into a linear one, enabling the use of standard linear control methods. Developed by Isidori, Sontag, and others in the 1980s, feedback linearization is conceptually elegant and powerful: if the system satisfies certain structural conditions (relative degree, decoupling matrix rank), the nonlinearities can be exactly cancelled through feedback, reducing the problem to linear design.

Isidori, A. (1995). Nonlinear Control Systems (3rd ed.). Springer-Verlag.

Sontag, E. D. (1983). A concept of input-output linearization. Proceedings of the 22nd IEEE Conference on Decision and Control.

Nijmeijer, H., & Van der Schaft, A. J. (1990). Nonlinear Dynamical Control Systems. Springer-Verlag.

sliding-mode-control backstepping-control model-predictive-control h-infinity-control


Female Pelvic Pain Scale

The Female Pelvic Pain Scale (FPPS) is a standardized self-report instrument designed to assess the severity and functional impact of pelvic pain conditions in women, including dysmenorrhea, pelvic inflammatory disease, interstitial cystitis, and pelvic pain of unclear etiology. By measuring pain intensity, location, timing, and associated functional limitations, the FPPS enables clinicians to quantify baseline pain burden and track treatment response.

Jones, G. L., Kennedy, S. H., Barnard, A., Wong, J., & Jenkinson, C. (2001). Development of an endometriosis quality-of-life instrument: The Endometriosis Health Profile-30. Obstetrics & Gynecology, 98(2), 258-264.

McGowan, L. (2016). Improving sexual function in women with endometriosis: a review. Sexual Medicine Reviews, 4(4), 326-337.


Female Sexual Distress Scale

The FSDS is a brief self-report measure designed to assess psychological distress specifically related to female sexual dysfunction. Originally developed by Derogatis and colleagues and published in 2002, the revised version (FSDS-R) comprises 13 items measuring distress about sexual concerns, with particular utility in identifying women who seek treatment for sexual problems versus those with asymptomatic sexual dysfunction. It is widely used in clinical practice and research to distinguish distress-inducing sexual dysfunction from normative sexual variation.

Derogatis, L. R., Clayton, A. H., Lewis-D'Agostino, D. J., Wunderlich, G., & Fu, Y. (2008). Validation of the Female Sexual Distress Scale-Revised for assessing distress in women with hypoactive sexual desire disorder and female sexual arousal disorder. Journal of Sexual & Marital Therapy, 34(2), 94–118.

Derogatis, L. R. (1994). SCL-90-R: Administration, Scoring and Procedures Manual. National Computer Systems.


Female Sexual Function Index

The FSFI is a 19-item multidimensional self-report instrument designed to assess sexual function in women across the lifespan. Developed by Rosen and colleagues in 2000, it measures six core domains of sexual response and has become a gold standard in both clinical and research settings for evaluating female sexual dysfunction.

Rosen, R., Brown, C., Heiman, J., Leiblum, S., Meston, C., Shabsigh, R., & D'Agostino, R. (2000). The Female Sexual Function Index (FSFI): a multidimensional self-report instrument for the assessment of female sexual function. Journal of Sex & Marital Therapy, 26(2), 191–208.

Wiegel, M., Meston, C., & Rosen, R. (2005). The female sexual function index (FSFI): cross-validation and development of clinical cutoff scores. Journal of Sex & Marital Therapy, 31(1), 1–20.

international-index-erectile-function female-sexual-distress-scale sexual-satisfaction-scale menopause-rating-scale


Feminist Research Methodology

feminist inquiry
feminist qualitative research
feminist standpoint research
gender-critical research

1970s–1980s (formalized as a methodology)

Sandra Harding, Dorothy Smith, Patricia Hill Collins, and the broader feminist social science movement

Feminist research methodology is a qualitative approach grounded in feminist theory that centres gender, power, and social justice as core analytical lenses. It challenges claims of value-free objectivity, foregrounds the voices and experiences of marginalized groups — particularly women — and explicitly positions the researcher as a political and social actor. Developed across disciplines including sociology, education, and health sciences, it draws on standpoint theory, intersectionality, and participatory ethics to produce knowledge that can inform emancipatory practice.

Interviews, personal narratives, ethnographic observations, documents, autobiographical accounts

Variable; commonly 10–30 participants in interview-based studies

Harding, S. (Ed.). (1987). Feminism and Methodology: Social Science Issues. Indiana University Press.

Creswell, J. W. (2013). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (3rd ed.). Sage. [Chapter on feminist inquiry and advocacy approaches]

ethnography grounded-theory narrative-analysis discourse-analysis action-research phenomenology


Fertigation Scheduling

Precision Timing and Delivery of Nutrients via Irrigation Systems

fertigation management
nutrient timing optimization
drip fertilization

Nutrient delivery and timing optimization

Irrigation engineering and crop nutrition integration

Fertigation scheduling integrates irrigation and nutrient delivery to optimize plant nutrition while minimizing waste and environmental impact. By applying fertilizers through drip or sprinkler systems at precise times and rates matched to plant development stage and soil water availability, growers can improve nutrient use efficiency, reduce leaching, and boost yields. This method is now standard in commercial vegetable, orchard, and nursery production worldwide.

Hochmuth, G. J. (1994). Efficiency of nutrient uptake—A review. HortTechnology, 4(1), 14–23.

Bar-Yosef, B. (2001). Fertigation management and crops response. Advances in Agronomy, 65, 1–75.

hydroponic-nutrient-solution greenhouse-climate-control phenological-stage-monitoring crop-load-management


FWQ

FWQ
Fetal Wellbeing
Pregnancy Wellbeing Questionnaire

DiPietro, Ghera, Costigan; fetal development research

The FWQ is a self-report questionnaire assessing pregnant women's subjective perception of fetal wellbeing, maternal physical and emotional health, and prenatal bonding. Developed by DiPietro and colleagues studying fetal development and maternal-fetal attachment, the FWQ captures non-clinical dimensions of pregnancy experience that predict maternal and infant outcomes. The questionnaire is used in research and clinical screening to identify pregnant women at risk for anxiety, depression, or poor bonding, enabling early intervention.

Pregnant woman self-report; subjective pregnancy experiences

DiPietro, J. A., Ghera, M. M., & Costigan, K. A. (2008). Prenatal origins of postnatal temperament. Early Human Development, 84(10), 643–650.

Valman, H. B. (ed.). (2009). Neonatal jaundice. Current Problems in Pediatrics and Adolescent Health Care, 39(10), 298–318.


Few-shot Learning

Few-shot Learning (Meta-learning with Limited Labeled Examples)

FSL
low-shot learning
k-shot learning
meta-learning for few examples

Lake, B. M.; Vinyals, O.; Finn, C. et al.

Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited.

Meta-learning / low-data learning paradigm

Labeled images, text, or structured data with very few examples per class

Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29.

Finn, C., Abbeel, P., & Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR 70:1126–1135.

transfer-learning meta-learning self-supervised-learning semi-supervised-learning metric-learning


Few-Shot Object Detection

Few-Shot Object Detection with Contrastive Learning

Deep Learning, Object Detection, Meta-Learning

Few-Shot Object Detection (FSOD) is a meta-learning approach that enables detecting novel object classes from only a few annotated examples. Unlike standard object detection requiring hundreds of labeled instances per class, FSOD learns to quickly adapt detection models to new object categories by leveraging knowledge from base categories.

Wang, X., Huang, T. E., Darrell, T., Gonzalez, J. E., & Yu, F. (2020). Few-shot object detection with attention-RPN and multi-relation detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9050-9059).

detr swin-transformer simclr meta-learning