Local Projections

Local Projections Impulse Response Analysis

Local Projections (LP) is a semi-parametric method for estimating impulse responses directly via multi-horizon regressions, bypassing VAR-model specification. Introduced by Jorda (2005), it projects outcomes h periods ahead onto current shocks and lags, producing impulse-response functions without assuming a particular lag structure or VAR order. This flexibility has made it the dominant approach in applied macroeconomics for measuring policy effects and shock transmission.

Jorda, O. (2005). Estimation and inference of impulse responses by local projections. American Economic Review, 95(1), 161-182.

Ramey, V. A., & Zubairy, S. (2018). Government spending multipliers in good times and in bad times. Journal of Political Economy, 126(2), 850-901.


Local Spatial Autocorrelation

local spatial association
local SA
LISA methods
local spatial clustering

Local Spatial Autocorrelation methods decompose global spatial clustering into location-specific statistics, revealing where in a study area significant clustering or dispersion occurs. Each observation receives its own association score and significance value, enabling the detection of spatial hot spots, cold spots, and spatial outliers rather than reporting a single summary statistic.

Georeferenced areal or point data with a spatial weights matrix

Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115.

Indicators of spatial association. Wikipedia.

local-morans-i local-gearys-c local-getis-ord-gi-star local-indicators-of-spatial-association spatial-autocorrelation hot-spot-analysis


Local Spatial Durbin Model

geographically weighted Spatial Durbin Model

LeSage & Pace (SDM foundation); local adaptation via Fotheringham et al. GWR framework

The Local Spatial Durbin Model (Local SDM) extends the global Spatial Durbin Model by allowing regression coefficients to vary across geographic space. It combines the SDM's ability to capture both spatial lag of the dependent variable and spatial lags of covariates with a geographically weighted estimation framework, producing location-specific direct and indirect spillover effects.

Georeferenced cross-sectional or panel data with spatial weights

LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis.

Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley.


Local Spatial Lag Model

geographically weighted spatial lag model

Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002)

The Local Spatial Lag Model extends the classical spatial lag model by allowing both the spatial autocorrelation parameter and the regression coefficients to vary across geographic locations. Instead of one global estimate of how neighboring outcomes influence each observation, the model fits location-specific parameters using kernel-weighted local estimation, revealing spatial heterogeneity in spatial dependence.

Georeferenced cross-sectional or panel data with a continuous outcome

Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.


Local Spatial Regression

locally weighted spatial regression
spatially varying coefficient model
local spatial model
place-based regression

Local Spatial Regression fits a separate regression model at each location in a study area, allowing regression coefficients to vary continuously across space. Rather than forcing one global slope on all observations, it reveals where and how the relationship between predictors and an outcome changes geographically — producing a map of coefficients rather than a single number.

Spatially varying coefficient regression

Georeferenced cross-sectional or panel data

Brunsdon, C., Fotheringham, A. S., & Charlton, M. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281-298.


Local Universal Kriging

Local Universal Kriging (Kriging with External Drift, Local Neighborhood)

local UK
local kriging with trend
local KED
local kriging with external drift

Matheron, G. (trend/drift kriging); local neighborhood approach standard in geostatistical practice

Local Universal Kriging is a geostatistical interpolation method that combines a spatially varying deterministic trend with a stochastic residual, estimated using only nearby observations within a defined search neighborhood. It generalizes local ordinary kriging by explicitly modeling and removing a polynomial or covariate-driven drift before interpolating the residual surface.

Continuous spatial data with a deterministic trend component

Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press.

Chiles, J.-P., & Delfiner, P. (1999). Geostatistics: Modeling Spatial Uncertainty. Wiley.

ordinary-kriging universal-kriging local-ordinary-kriging co-kriging local-co-kriging geographically-weighted-regression


Local Volatility (Dupire)

Dupire's local volatility model (1994) is a deterministic framework that extracts a term and strike-dependent volatility function from market option prices. Unlike constant volatility, local volatility perfectly fits the observed implied volatility smile and is implemented via finite difference methods for European and American option pricing.

Dupire, B. (1994). Pricing with a smile. Risk Magazine, 7(1), 18-20.

Gatheral, J. (2006). The Volatility Surface: A Practitioner's Guide. John Wiley & Sons.

sabr-model bates-model risk-neutral-valuation crank-nicolson-pricing


LOCAL-WLC

LOCAL-WLC (Local WLC — neighbourhood-adaptive weighted linear combination) is a ranking multi-criteria decision-making (MCDM) method introduced by Malczewski, J. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Range-sensitive neighbourhood-local additive utility — criterion weights scale with local criterion variance within each spatial neighbourhood

Malczewski, J. (2011). Local weighted linear combination. Transactions in GIS

ahp anp bwm critic entropy swara fucom merec


Locally Linear Embedding

LLE
manifold learning
nonlinear dimensionality reduction
yerel doğrusal gömme

Locally linear embedding, introduced by Sam Roweis and Lawrence Saul in 2000, is a manifold-learning method for nonlinear dimensionality reduction. It assumes that although data may curve through a high-dimensional space, each point and its neighbours lie approximately on a flat patch. LLE captures each point as a weighted combination of its neighbours and then finds a low-dimensional layout that preserves those same local relationships, unrolling curved structure into a faithful low-dimensional map.

Nonlinear manifold dimensionality reduction

Data lie on a locally linear low-dimensional manifold

Local neighborhood reconstruction weights

Output: Low-dimensional embedding

Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.


Location-Allocation

facility location
p-median problem
maximal covering location problem
yer-tahsis modelleri

Location-allocation models decide where to place a set of facilities and simultaneously assign demand points to them so as to optimize an objective such as total travel cost, worst-case distance, or population covered. Rooted in the operations-research work of Cooper (1963) and Hakimi (1964) and central to network GIS, they answer questions like where to site warehouses, hospitals, fire stations, or schools to best serve a spatially distributed population.

Site facilities + assign demand optimally

Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.

Hakimi, S. L. (1964). Optimum locations of switching centers and the absolute centers and medians of a graph. Operations Research, 12(3), 450–459.

least-cost-path linear-programming integer-programming gis-mcda


Lockdown Wellbeing Scale

The Lockdown Wellbeing Scale (LWS) measures the specific psychological and social impacts of mobility restrictions and lockdown policies on individual well-being. Developed by Giuntella and colleagues from economic and social data on pandemic restrictions, it captures dimensions of isolation, social disconnection, routinization disruption, and perceived loss of autonomy. The LWS is distinct from anxiety/depression measures, focusing instead on how restrictive policies themselves affect quality of life and social well-being.

Giuntella, O., Hyde, K., Saccardo, S., & Solomon, S. (2021). Lifestyle and mental health disruptions during COVID-19. Proceedings of the National Academy of Sciences, 118(9), e2016632118.

Twenge, J. M., & Joiner, T. E. (2020). Mental illness has increased in American adolescents: Meta-analysis of sample correlation data from 1987 to 2018. Journal of Child Psychology and Psychiatry, 61(5), 576–588.


LODECI

LODECI (LOgarithmic DEcomposition of Criteria Importance) is a weight objective multi-criteria decision-making (MCDM) method introduced by Pala, O. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Objective weighting via logarithmic decomposition

Pala, O. (2024). Assessment of the social progress on European Union by logarithmic decomposition of criteria importance. Expert Systems With Applications

ahpsort aploco aras aroman artasi cobra cocoso codas


LOESS

LOWESS
local regression
locally weighted scatterplot smoothing
yerel regresyon

LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots.

Span (bandwidth) and local polynomial degree

Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836.

Cleveland, W. S., & Devlin, S. J. (1988). Locally weighted regression: an approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403), 596–610.

regression-splines generalized-additive-model kernel-density-estimation polynomial-regression


Log-Loss (Cross-Entropy Loss)

Information theory and machine learning literature

Log-loss measures the difference between predicted probabilities and actual labels, penalizing confident wrong predictions more than uncertain ones. It is a standard loss function in machine learning optimization and evaluates probabilistic classifier calibration.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press.

brier-score accuracy f1-score calibration


Log Mean Temperature Difference

Log Mean Temperature Difference Method for Heat Exchangers

LMTD
logarithmic mean temperature difference

The Log Mean Temperature Difference (LMTD) method is a fundamental tool for calculating heat transfer rates in heat exchangers. It defines the effective temperature difference between two fluids as the logarithmic average of the temperature differences at the inlet and outlet. This method enables engineers to size and analyze heat exchangers systematically using the basic heat transfer equation Q = U A LMTD.

Kern, D. Q. (1950). Process Heat Transfer. McGraw-Hill.

Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.


Log-Rank Test

Log-Rank Test for Comparing Survival Curves

Mantel log-rank test
Mantel-Cox test
log-rank sağkalım testi
Log-Rank Testi

The log-rank test, developed by Nathan Mantel in 1966, is a non-parametric hypothesis test that compares the overall survival experience of two or more groups throughout the entire follow-up period. It is the standard companion to Kaplan-Meier curves and determines whether observed differences between curves are statistically meaningful.

Mantel, N. (1966). Evaluation of Survival Data and Two New Rank Order Statistics Arising in Its Consideration. Cancer Chemotherapy Reports, 50(3), 163–170.

Kleinbaum, D. G. & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer.

kaplan-meier cox-ph nelson-aalen fine-gray-model


LOGARITHMIC-NORMALIZATION

LOGARITHMIC-NORMALIZATION (Logarithmic Normalization — log-ratio column normalisation for multiplicative aggregation contexts) is a normalization multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z. in 2008. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Zavadskas, E. K., Turskis, Z. (2008). A new logarithmic normalization method in games theory. Informatica


Logic Synthesis

Logic Synthesis for Digital Circuit Design

RTL synthesis
Hardware synthesis
Logic optimization

Logic Synthesis is the automated conversion of high-level hardware descriptions (RTL in Verilog/VHDL) into optimized gate-level netlists. Pioneered by Brayton et al. at UC Berkeley in the 1980s-1990s, logic synthesis transforms behavioral specifications into physical implementations, optimizing for area, speed, and power. Synthesis is essential to modern digital design, enabling rapid iteration and automation of the most tedious manual tasks.

Automated conversion of HDL descriptions to gate-level netlists

Brayton, R. K., Hachtel, G. D., McMullin, C. T., Sangiovanni-Vincentelli, A. L., & Vincentelli, A. S. (1987). Logic Synthesis for VLSI Design. Kluwer Academic.

Mishchenko, A., Chatterjee, S., Brayton, R., & Sangiovanni-Vincentelli, A. L. (2006). DAG-aware AIG rewriting. In Proc. DAC (pp. 713-718). ACM.

Berkeley, S. (1995). SIS: A system for sequential circuit synthesis. Technical Report UCB/ERL M95/55, UC Berkeley.


Logistic regression (ML)

Logistic Regression (Machine Learning Classification Model)

logit model
logit regression
binomial logistic regression
maximum entropy classifier

Logistic regression is a foundational probabilistic classifier that models the log-odds of a binary (or multinomial) outcome as a linear function of the predictors. Introduced by D. R. Cox in 1958, it remains one of the most widely used and interpretable classification methods in both statistics and machine learning, valued for its calibrated probability outputs and clear coefficient interpretation.

Labeled tabular data; binary or multinomial outcome

Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.

James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 4). Springer.

linear-regression-ml support-vector-machine random-forest decision-tree naive-bayes regularized-logistic-regression


Logistic Regression

logit model
binomial logistic regression
LR

Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.

Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). John Wiley & Sons.


Long-Memory Models

Long-Memory Time Series Models (ARFIMA, FIGARCH)

ARFIMA
FIGARCH
fractionally integrated models
fractional integration
Uzun Hafıza Modelleri (ARFIMA, FIGARCH)

Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)

Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration.

Fractionally integrated time series model

Maximum likelihood with fractional differencing (d estimated via R/S or GPH)

Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29.

Baillie, R. T., Bollerslev, T. & Mikkelsen, H. O. (1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 74(1), 3-30.

arima garch-model ols-regression high-frequency-microstructure risk-factor-pca


Long Short-Term Memory

LSTM
LSTM network
LSTM-RNN
long short-term memory RNN

Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step.

Recurrent neural network with gated memory cells

Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

Graves, A., Mohamed, A.-R. & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. Proceedings of ICASSP 2013, pp. 6645–6649. IEEE.

recurrent-neural-network gated-recurrent-unit transformer bert-based-classification convolutional-neural-network sentence-embeddings


Longformer / BigBird

Long-Sequence Transformers with Sparse Attention (Longformer / BigBird)

Uzun Dizi Transformer (Longformer / BigBird)

Beltagy, Peters & Cohan (Longformer); Zaheer et al. (BigBird)

Long-sequence Transformers such as Longformer (Beltagy, Peters & Cohan, 2020) and BigBird (Zaheer et al., 2020) replace the standard Transformer's O(n²) attention with sparse attention patterns that scale linearly, O(n), with sequence length. This lets a single model attend over thousands of tokens — full documents, legal texts, or genomic sequences — that would not fit a conventional Transformer.

Sparse-attention Transformer for long sequences

Long-document classification & explanation

Beltagy, I., Peters, M. E. & Cohan, A. (2020). Longformer: The Long-Document Transformer. arXiv.

Zaheer, M. et al. (2020). Big Bird: Transformers for Longer Sequences. NeurIPS.

mixture-of-experts graph-attention-network random-forest xgboost


Longitudinal Autoethnography

longitudinal personal narrative research

Carolyn Ellis, Arthur Bochner (autoethnography foundations); longitudinal extension by various scholars from 2000s onward

Longitudinal autoethnography is a qualitative research design in which the researcher systematically documents, reflects on, and analyzes their own lived experience across an extended period — typically months to years. By combining the self-reflexive focus of autoethnography with a longitudinal temporal structure, this approach reveals how personal meanings, identities, and social understandings evolve over time. It bridges the personal and the cultural, producing richly layered narratives that connect individual transformation to broader social processes.

Qualitative longitudinal research design

Personal field notes, journals, reflective memos, interviews, diaries collected over time

Ellis, C. (2004). The Ethnographic I: A Methodological Novel about Autoethnography. AltaMira Press.

Holman Jones, S., Adams, T. E., & Ellis, C. (2013). Introduction: Coming to know autoethnography as more than a method. In S. Holman Jones, T. E. Adams, & C. Ellis (Eds.), Handbook of Autoethnography (pp. 17–47). Left Coast Press.

autoethnography longitudinal-ethnography narrative-inquiry longitudinal-narrative-research life-history-research reflexive-thematic-analysis


Longitudinal Biographical Research

LBR
longitudinal narrative research
biographical-longitudinal method
repeated biographical interviewing

1990s–2000s (consolidated as a named approach ca. 2000)

Tom Wengraf, Prue Chamberlayne, Joanna Bornat (BNIM tradition); also Robert Miller and Rita Charon in parallel strands

Longitudinal Biographical Research (LBR) is a qualitative approach that combines in-depth biographical or narrative interviewing with a repeated, time-extended data-collection design. Participants are interviewed at multiple time points — sometimes years apart — so that researchers can trace how individuals construct, revise, and re-narrate their life stories as circumstances change. The method captures both the content of life histories and the dynamic process through which meaning is made and remade over time.

Repeated in-depth biographical interviews, life-history narratives, documents

Wengraf, T. (2001). Qualitative Research Interviewing: Biographic Narrative and Semi-Structured Methods. Sage.

Thomson, R., & Holland, J. (2003). Hindsight, foresight and insight: The challenges of longitudinal qualitative research. International Journal of Social Research Methodology, 6(3), 233–244.

narrative-inquiry biographical-method life-history-research longitudinal-case-study oral-history grounded-theory


Longitudinal Case Study

longitudinal case research
panel case study
repeated case study
temporal case study

1984–1990 (foundational methodological codification)

Robert K. Yin (case study methodology); Andrew M. Pettigrew (longitudinal field research)

A longitudinal case study is a qualitative research design that combines the in-depth, contextually rich focus of case study methodology with repeated data collection across multiple time points. Rather than capturing a single snapshot, it follows one or a small number of cases — an individual, group, organisation, or programme — over months or years to trace how processes, relationships, and meanings evolve. This design is well suited to questions about how and why things change, not merely what the state of affairs is at one moment.

Interviews, documents, observations, archival records collected at multiple time points

Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.

Pettigrew, A. M. (1990). Longitudinal field research on change: Theory and practice. Organization Science, 1(3), 267–292.

case-study ethnography narrative-analysis action-research grounded-theory phenomenology


Longitudinal Causal-Comparative Research Design

1970s–1980s (as an established combined design in educational and social research)

Synthesized from causal-comparative tradition (Kerlinger, 1973) and longitudinal design frameworks (Goldstein, 1979)

Longitudinal causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on one or more dependent variables across multiple measurement points over time. Unlike true experiments, the researcher does not manipulate the independent variable; instead, naturally occurring group differences (e.g., gender, socioeconomic status, diagnostic category) are examined to explore their relationship to outcomes as they evolve longitudinally.

Non-experimental quantitative research design

Quantitative group-comparison data collected at multiple time points (surveys, tests, archival records)

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (7th ed.). McGraw-Hill.

Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational Research: An Introduction (8th ed.). Pearson. [Chapter on causal-comparative and longitudinal designs]

causal-comparative-research longitudinal-research ex-post-facto-design panel-research cohort-research correlational-research


Longitudinal Classic grounded theory

longitudinal substantive theory building

1967 (classic GT); longitudinal application developed from 1980s onward

Barney G. Glaser and Anselm L. Strauss (classic GT); longitudinal extension by later methodologists

Longitudinal Classic Grounded Theory applies Glaser and Strauss's original discovery-oriented grounded theory method across two or more data collection waves separated by time. The approach tracks how social processes, behaviors, and conceptual categories evolve, allowing the researcher to build a substantive theory that captures change and continuity rather than a single static snapshot of a phenomenon.

Interview transcripts, field notes, documents collected at multiple time points

Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.

Glaser, B. G. (2001). The Grounded Theory Perspective: Conceptualization Contrasted with Description. Sociology Press.


Longitudinal Cohort Research

longitudinal cohort study
prospective cohort study
cohort follow-up study
panel cohort design

1950s–1960s (formalized in epidemiological methodology)

Richard Doll & Austin Bradford Hill (landmark Doctors' Cohort Study, 1951); cohort logic formalized in mid-20th century epidemiology

Longitudinal cohort research is an observational quantitative design that recruits a defined group of individuals sharing a common characteristic (the cohort) and follows them prospectively over time, collecting data at multiple points to examine how outcomes develop, risks accumulate, or relationships change. It is the cornerstone design for studying causation, developmental trajectories, and the natural history of phenomena in epidemiology, social science, and education.

Quantitative observational longitudinal design

Repeated-measures quantitative data; structured questionnaires, records, biomarkers collected at multiple time points

Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press.

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

cohort-research longitudinal-research panel-research prospective-study cross-sectional-research repeated-measures-design


Longitudinal comparative legal analysis

LCLA
diachronic comparative law
longitudinal legal comparison
dynamic comparative legal research

Late 20th century (comparative law foundational texts 1960s–1998; longitudinal integration from 1990s onward)

Konrad Zweigert and Hein Kotz (comparative law foundation); longitudinal dimension integrated in socio-legal and legal history scholarship

Longitudinal comparative legal analysis examines how legal rules, doctrines, or institutions develop and diverge across two or more legal systems over an extended period. By combining the spatial dimension of comparative law with the temporal dimension of longitudinal research, it captures not just differences between jurisdictions at a single point but the trajectories of legal change — convergence, divergence, transplantation, and resistance — over years or decades.

Qualitative-interpretive legal research design

Legal texts (statutes, case law, regulations, travaux preparatoires), secondary legal literature, court records across multiple time points and jurisdictions

Zweigert, K., & Kotz, H. (1998). An Introduction to Comparative Law (3rd ed.). Oxford University Press.

Siems, M. M. (2014). Comparative Law. Cambridge University Press.

comparative-legal-analysis doctrinal-legal-research longitudinal-case-law-analysis legal-content-analysis historical-archival-research hermeneutic-analysis


Longitudinal CFA

Longitudinal Confirmatory Factor Analysis

longitudinal CFA
repeated-measures CFA
longitudinal measurement model
panel CFA

Karl Jöreskog (CFA framework); longitudinal extension by Wheaton, Muthén, and Alwin in the 1970s–1990s

Longitudinal confirmatory factor analysis (longitudinal CFA) applies a theoretically specified measurement model to data collected at two or more time points. Its primary purpose is to verify that a scale measures the same latent construct in the same way over time — a prerequisite for drawing valid conclusions about change from repeated-measures data.

Longitudinal latent variable / measurement model

Repeated-measures continuous or ordinal item responses across two or more time points

Widaman, K. F. & Reise, S. P. (1997). Exploring the measurement invariance of psychological instruments: Applications in the substance use domain. In K. J. Bryant, M. Windle & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 281–324). American Psychological Association.

Millsap, R. E. (2011). Statistical Approaches to Measurement Invariance. Routledge.


Longitudinal Confirmatory Research

Longitudinal Confirmatory Research Design

longitudinal confirmatory study
confirmatory longitudinal design
longitudinal hypothesis-testing design
longitudinal CFA design

1970s onward; consolidated in SEM literature from 1990s

Synthesized from longitudinal design traditions (e.g., Baltes & Nesselroade, 1979) and confirmatory analytic frameworks (Joreskog, 1969)

Longitudinal confirmatory research combines the temporal depth of longitudinal design with the hypothesis-driven logic of confirmatory analysis. The researcher specifies a priori hypotheses or structural models about how variables change or remain stable over time, then tests those predictions against data collected at two or more time points. It is the design of choice when theory is mature enough to make specific predictions about developmental, causal, or stability processes.

Repeated-measures quantitative data (survey, psychometric, biomedical, educational)

Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press.

Little, T. D. (2013). Longitudinal Structural Equation Modeling. Guilford Press.

longitudinal-research confirmatory-research panel-research cohort-research longitudinal-correlational-research longitudinal-model-testing-research


Longitudinal Construct Validity

longitudinal measurement validity
construct validity over time
longitudinal measurement invariance
LCV

Meredith, Vandenberg, and the measurement invariance tradition

Longitudinal construct validity evaluates whether a psychological scale measures the same latent construct in the same way across multiple time points. It is tested by progressively constraining a confirmatory factor model across waves and comparing model fit, ensuring that observed change scores reflect genuine change in the underlying trait rather than measurement drift.

Repeated-measures questionnaire / test data

Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.

Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525–543.

confirmatory-factor-analysis measurement-invariance longitudinal-sem test-retest-reliability convergent-validity exploratory-factor-analysis


longitudinal CGT
constructivist GT longitudinal
longitudinal Charmaz grounded theory
temporal constructivist grounded theory

2006 (Charmaz's constructivist GT); longitudinal application from ~2000s onward

Kathy Charmaz (constructivist GT); extended to longitudinal designs by qualitative longitudinal researchers

Longitudinal Constructivist Grounded Theory combines Kathy Charmaz's constructivist variant of grounded theory — which foregrounds the co-construction of meaning between researcher and participants — with a multi-wave, time-extended data collection design. Rather than capturing a single snapshot, the researcher returns to the same participants across two or more time points, allowing the emergent theory to track how processes, identities, and social meanings develop, shift, or stabilise over time.

Qualitative research design and analysis approach

Multiple-wave interview transcripts, field notes, participant documents collected over time

Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.

Thomson, R., & Holland, J. (2003). Hindsight, foresight and insight: the challenges of longitudinal qualitative research. International Journal of Social Research Methodology, 6(3), 233–244.

constructivist-grounded-theory longitudinal-grounded-theory longitudinal-qualitative-research grounded-theory longitudinal-case-study longitudinal-thematic-analysis


Longitudinal Content Analysis

LCA
repeated content analysis
diachronic content analysis
trend content analysis

Mid-20th century onward; systematized alongside content analysis (Berelson, 1952; Krippendorff, 1980)

Developed within the content analysis tradition; longitudinal extensions widely applied since the mid-20th century in communication and political science research

Longitudinal Content Analysis (LCA) applies systematic content analysis to documents, media, or texts sampled at two or more time points in order to detect how themes, frames, language, or discourse patterns change or persist over time. Drawing on the established logic of content analysis, it adds a temporal dimension that allows researchers to chart trends, trace the evolution of representations, and test hypotheses about historical or social change. It is widely used in communication research, political science, media studies, and the health sciences.

Qualitative and mixed-methods research design

Textual, visual, or audio-visual documents collected at multiple time points

Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage.

Neuendorf, K. A. (2017). The Content Analysis Guidebook (2nd ed.). Sage.

content-analysis thematic-analysis discourse-analysis narrative-analysis frame-analysis document-analysis


Longitudinal content validity

longitudinal content validation
temporal content validity
repeated-measure content validity
content validity over time

Haynes, Richard & Kubany (1995); extended to longitudinal contexts by measurement invariance researchers

Longitudinal content validity evaluates whether the items of a measure adequately and consistently represent the intended content domain not only at a single point in time but across repeated administrations. It ensures that the conceptual coverage of a scale remains appropriate and stable as measurement occasions accumulate over a study period.

Expert ratings, item-level survey data across time points

Haynes, S. N., Richard, D. C. S., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 7(3), 238–247.

Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.


Longitudinal convergent validity

longitudinal construct validity
repeated-measure convergent validity
cross-time convergent validity
temporal convergent validity

Longitudinal convergent validity evaluates whether a scale's indicators correlate with theoretically related constructs not just at a single time point but consistently across repeated measurement occasions. It extends standard convergent validity testing into longitudinal designs to ensure that the scale measures the intended construct in the same meaningful way over time.

Repeated-measures / longitudinal scale data

Cole, D. A. & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112(4), 558–577.


Longitudinal Correlational Research

Longitudinal Correlational Research Design

longitudinal correlational study
prospective correlational design
longitudinal associational research
repeated-measures correlational design

Mid-20th century (formalized 1940s–1960s)

Rooted in early correlational methodology (Galton, Pearson late 19th c.); longitudinal extension formalized through panel studies in social sciences (mid-20th c.)

Longitudinal correlational research is a non-experimental quantitative design that examines the strength and direction of relationships among variables by collecting data from the same participants at two or more points in time. Unlike a cross-sectional correlational study, the longitudinal approach captures how associations evolve, persist, or dissolve across time, providing a stronger empirical basis for causal inference without experimental manipulation.

Repeated measures of quantitative variables across two or more time points

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill.

Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). SAGE Publications.


Longitudinal CDA
diachronic critical discourse analysis
longitudinal discourse study
temporal CDA

1990s–2000s (CDA foundations ~1989–1992; longitudinal applications consolidated through 2000s)

Longitudinal Critical Discourse Analysis (LCDA) combines the critical discourse analysis tradition — which examines how language constructs and reproduces power, ideology, and social inequality — with a longitudinal design that collects and compares texts at multiple time points. By tracking discursive change over time, LCDA reveals how ideological representations, social identities, and power relations shift, stabilise, or are contested across different historical or political periods.

Qualitative longitudinal discourse design

Text corpora collected at multiple time points (media texts, policy documents, speeches, interviews, social media archives)

Fairclough, N. (1992). Discourse and Social Change. Polity Press.

Wodak, R., & Meyer, M. (Eds.). (2001). Methods of Critical Discourse Analysis. Sage.


Longitudinal Cronbach's Alpha

Longitudinal Cronbach's Alpha Reliability Analysis

repeated-measures alpha
longitudinal internal consistency
wave-specific Cronbach's alpha
time-point reliability estimation

1951 (alpha); longitudinal application systematised ca. 1990s–2000s

Lee J. Cronbach (alpha); longitudinal extension formalised in scale validation literature from 1980s onward

Longitudinal Cronbach's alpha assesses the internal consistency reliability of a scale at each wave of a repeated-measures study and examines whether that reliability remains stable across time. It is an essential step in longitudinal scale validation, ensuring that a scale measures its construct with consistent precision at every measurement occasion.

Ordinal or interval Likert-type items measured at two or more time points

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.


Longitudinal Cross-Sectional Research

Longitudinal Cross-Sectional Research Design (Cohort-Sequential Design)

cohort-sequential design
accelerated longitudinal design
mixed longitudinal design
cross-sequential design

Longitudinal cross-sectional research — also called cohort-sequential or accelerated longitudinal design — simultaneously follows multiple age cohorts over time, combining the depth of longitudinal tracking with the age-range efficiency of cross-sectional sampling. By overlapping cohorts at successive waves, researchers can disentangle age effects, cohort effects, and period effects far more rigorously than either pure design allows, and can compress the calendar time needed to study development across a wide age span.

Quantitative observational research design

Repeated quantitative measurements across multiple age cohorts

Schaie, K. W. (1965). A general model for the study of developmental problems. Psychological Bulletin, 64(2), 92–107.

Baltes, P. B. (1968). Longitudinal and cross-sectional sequences in the study of age and generation effects. Human Development, 11(3), 145–171.

longitudinal-study cross-sectional-survey panel-study cohort-study growth-curve-modeling repeated-measures-design


Longitudinal Diary Method

diary study (longitudinal)
daily diary method
repeated-measures diary
longitudinal self-report diary

1942 (diary method); longitudinal variant formalised 1980s–2000s

Allport (1942); systematic longitudinal extension developed by Bolger, Davis & Rafaeli (2003)

The Longitudinal Diary Method is a data collection technique in which participants record experiences, thoughts, feelings, or behaviors in structured diary entries repeatedly over an extended period — from days to months or even years. Unlike a one-shot survey, it tracks within-person change, daily fluctuation, and temporal processes in natural settings, making it especially powerful for studying how phenomena evolve over time.

Longitudinal qualitative/quantitative data collection

Repeated self-report entries (text, ratings, checklists) over days, weeks, or months

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

Scollon, C. N., Kim-Prieto, C., & Diener, E. (2009). Experience sampling: Promises and pitfalls, strengths and weaknesses. Journal of Happiness Studies, 4(1), 5–34.

diary-method experience-sampling-method longitudinal-survey longitudinal-participant-observation mobile-diary-method longitudinal-field-notes


Longitudinal DIF

Longitudinal Differential Item Functioning

longitudinal DIF
DIF across time
temporal DIF
longitudinal item bias

Multiple contributors; foundational DIF methods by Lord (1980) extended to longitudinal designs

Longitudinal differential item functioning detects whether individual test or scale items behave differently across measurement occasions for the same respondents. It extends standard DIF methodology to repeated-measures designs, ensuring that observed change scores genuinely reflect construct change rather than shifts in item characteristics over time.

Repeated-measures ordinal or binary item responses

Millsap, R. E., & Kwok, O. M. (2004). Evaluating the impact of partial factorial measurement invariance on selection in two groups. Psychological Methods, 9(1), 93–115.


Longitudinal Discourse Analysis

LDA
diachronic discourse analysis
longitudinal CDA
discourse change analysis

1990s–2000s (systematised as a distinct approach)

Norman Fairclough; Jan Blommaert; applied linguists in sociolinguistics and CDA traditions

Longitudinal Discourse Analysis (LDA) is a qualitative research approach that examines how discourse — language in use, texts, talk, and representational practices — changes across time. Rather than analysing a single snapshot of language, LDA collects and compares discourse data at multiple points to uncover how meanings, identities, ideologies, or social practices evolve, stabilise, or shift under the influence of historical, institutional, or societal forces.

Texts, documents, transcripts, media archives collected at multiple time points

Fairclough, N. (2003). Analysing Discourse: Textual Analysis for Social Research. Routledge.

Blommaert, J. (2005). Discourse: A Critical Introduction. Cambridge University Press.

discourse-analysis critical-discourse-analysis narrative-analysis content-analysis thematic-analysis ethnography


Longitudinal Discriminant Validity

LDV
longitudinal construct distinctiveness
cross-time discriminant validity
temporal discriminant validity

Formalized through SEM-based validity traditions (Campbell & Fiske, 1959; Cole & Maxwell, 1993)

Longitudinal discriminant validity tests whether a psychological construct measured at two or more time points is empirically distinct across occasions — ensuring that the same construct does not collapse into a single undifferentiated mass over time. It is a prerequisite for meaningful change modeling in panel and longitudinal research.

Validity assessment / measurement quality

Repeated-measures ordinal or continuous survey/test data

Cole, D. A. & Maxwell, S. E. (1993). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112(4), 558–577.

Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.

confirmatory-factor-analysis measurement-invariance convergent-validity structural-equation-modeling average-variance-extracted longitudinal-cfa


Longitudinal document analysis

Longitudinal Qualitative Document Analysis

longitudinal documentary research
longitudinal archival analysis
repeated document analysis
LDA

2003–2009 (formalized in qualitative research methodology)

Glenn A. Bowen (document analysis framework); Johnny Saldaña (longitudinal qualitative methods)

Longitudinal document analysis is a qualitative research approach that systematically collects and analyzes documents at multiple time points to trace how phenomena, discourses, policies, or organizational practices change over time. By treating documents as primary data sources rather than supplementary evidence, researchers can reconstruct temporal trajectories, identify turning points, and understand how meaning evolves across extended periods without requiring direct participant contact.

Documents (reports, policies, minutes, records, texts) collected at multiple time points

Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.

Saldaña, J. (2003). Longitudinal Qualitative Research: Analyzing Change Through Time. AltaMira Press.

document-analysis longitudinal-qualitative-research longitudinal-content-analysis longitudinal-thematic-analysis archival-research historical-comparative-analysis


Longitudinal Ethnography

extended ethnography
long-term fieldwork
sustained ethnographic study
longitudinal field research

1920s (classical origins); refined 1990s–2000s

Rooted in classical anthropological fieldwork (Malinowski, 1922); systematised for sociological revisits by Michael Burawoy (2003)

Longitudinal ethnography is a qualitative research design in which a researcher conducts sustained, repeated fieldwork with the same community, organisation, or group across an extended period — months to decades. By returning to the field at multiple time points, the researcher captures how social processes, meanings, and structures evolve, making it the only qualitative method capable of directly observing change and continuity in lived experience.

Field notes, interviews, documents, observations collected across multiple time points

Burawoy, M. (2003). Revisits: An outline of a theory of reflexive ethnography. American Sociological Review, 68(5), 645–679.

Thomson, R., & Holland, J. (2003). Hindsight, foresight and insight: The challenges of longitudinal qualitative research. International Journal of Social Research Methodology, 6(3), 233–244.

ethnography participant-observation case-study narrative-analysis grounded-theory life-history-interview


Longitudinal Ex Post Facto Design

Longitudinal Ex Post Facto Research Design

longitudinal causal-comparative design
longitudinal after-the-fact design
longitudinal retrospective design
LEPF design

1964–1986 (Kerlinger 1964 first edition; Campbell & Stanley 1966)

Fred N. Kerlinger (systematized); Donald T. Campbell & Julian C. Stanley (quasi-experimental framework)

A longitudinal ex post facto design combines the time-depth of longitudinal research with the retrospective logic of ex post facto inquiry. Participants are grouped by a naturally occurring characteristic or past event — not randomly assigned — and then observed or measured at multiple points over time. The goal is to trace how pre-existing differences between groups unfold or predict outcomes across an extended period, without the researcher ever manipulating the independent variable.

Archival records, repeated-measures surveys, administrative data, prospective or retrospective panel data

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

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

ex-post-facto-design longitudinal-research causal-comparative-research cohort-research panel-research longitudinal-correlational-research


Longitudinal Explanatory Research

Longitudinal Explanatory Research Design

explanatory longitudinal design
longitudinal causal research
explanatory panel study
longitudinal explanatory study

1970s–1990s (formal methodological codification)

Rooted in panel and longitudinal survey traditions; systematised by Scott Menard and others in the late 20th century

Longitudinal explanatory research combines repeated measurement over time with an explicit aim of explaining why and how variables change or influence one another. Unlike purely descriptive longitudinal designs, the explanatory orientation tests causal or predictive hypotheses by examining temporal precedence — a key criterion for causal inference in non-experimental settings. It is widely used in social, behavioral, educational, and health sciences to disentangle cause from correlation.

Repeated-measures numeric data; survey panels; archival records collected at two or more time points

Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications.

longitudinal-research panel-research cohort-research explanatory-research causal-comparative-research cross-lagged-panel-model


Longitudinal EFA

Longitudinal Exploratory Factor Analysis

LEFA
longitudinal factor analysis
repeated-measures EFA
panel EFA

John R. Nesselroade and colleagues (lifespan developmental tradition)

Longitudinal EFA applies exploratory factor analysis separately at each measurement occasion — or jointly across occasions — to discover whether the same latent factor structure emerges over time and whether factor loadings remain stable across waves. It is the foundational data-driven approach for examining structural change and continuity in panel and developmental research.

Latent variable / dimension reduction across time

Repeated-measures continuous or ordinal item responses collected at two or more time points

Nesselroade, J. R. (1983). Temporal selection and factor invariance in the study of development and change. In P. B. Baltes & O. G. Brim (Eds.), Life-Span Development and Behavior (Vol. 5, pp. 59–87). Academic Press.


Longitudinal Focus Group

repeated focus group
panel focus group
longitudinal FG
follow-up focus group

1940s (focus groups); longitudinal variant refined 1980s–1990s

Adapted from Robert K. Merton's focused interview tradition; longitudinal design developed in social and health sciences

A longitudinal focus group convenes the same group of participants in multiple sessions over an extended period — weeks, months, or years — to trace how their attitudes, experiences, or interpretations evolve in response to changing circumstances. Unlike a single focus group snapshot, the repeated-contact design captures the dynamics of opinion and meaning-making across time, making it particularly valuable in health, policy, and social research where change is the phenomenon of interest.

Qualitative longitudinal data collection

Verbal group discussion transcripts collected at multiple time points

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

Farquhar, C., & Das, R. (1999). Are focus groups suitable for 'sensitive' topics? In R. S. Barbour & J. Kitzinger (Eds.), Developing Focus Group Research (pp. 47–63). Sage.

focus-group longitudinal-survey longitudinal-in-depth-interview panel-study diary-method participant-observation


Longitudinal Generalizability Theory

Webb, Shavelson, and colleagues, building on Cronbach et al. (1963) G-theory foundations

Longitudinal generalizability theory extends classical G-theory to repeated-measures and longitudinal designs, decomposing score variance across persons, measurement occasions, raters, and items simultaneously. It quantifies how reliably scores can be generalized across time points, evaluators, and conditions — information that is invisible to cross-sectional reliability indices.

Variance components / reliability estimation

Repeated-measures / longitudinal ratings or scores

Webb, N. M., Shavelson, R. J., & Harrigan, E. H. (2007). Generalizability theory: Overview. In C. R. Rao & S. Sinharay (Eds.), Handbook of Statistics, Vol. 26: Psychometrics (pp. 1–43). Elsevier.

Brennan, R. L. (2001). Generalizability Theory. Springer.

generalizability-theory confirmatory-factor-analysis multilevel-modeling intraclass-correlation latent-growth-curve-modeling exploratory-factor-analysis


Longitudinal Grounded Theory

LGT
longitudinal GT
temporal grounded theory
grounded theory longitudinal design

1990s–2000s (as a recognized variant of grounded theory)

Kathy Charmaz and longitudinal qualitative researchers (building on Glaser & Strauss)

Longitudinal grounded theory is a qualitative research design that applies grounded theory's inductive, iterative logic to data collected from the same participants or settings across multiple time points. It is used to build substantive theory that accounts not only for social processes but also for how those processes unfold, shift, and are renegotiated over time. The approach is particularly suited to studying change, trajectory, and temporal experience in social and health research.

Repeated in-depth interviews, field notes, documents collected across multiple time points

Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. Sage.

Hallberg, L. R.-M. (2006). The 'core category' of grounded theory: Making constant comparisons. International Journal of Qualitative Studies on Health and Well-being, 1(3), 141–148.

grounded-theory constructivist-grounded-theory longitudinal-qualitative-research longitudinal-case-study longitudinal-thematic-analysis classic-grounded-theory


Longitudinal Hermeneutic Analysis

longitudinal interpretive analysis
repeated hermeneutic inquiry
diachronic hermeneutics
temporal hermeneutic study

1960s–1980s (hermeneutic tradition); longitudinal application consolidated late 20th century

Hans-Georg Gadamer (hermeneutic foundation); extended by Paul Ricoeur and longitudinal qualitative researchers

Longitudinal hermeneutic analysis combines the interpretive depth of hermeneutics with repeated data collection across time, tracing how meanings, understandings, and interpretations evolve within individuals, texts, or communities. Rooted in Gadamerian and Ricoeurian hermeneutics, this approach treats meaning as temporally situated and subject to revision, making it particularly valuable in humanities and social science research that seeks to understand change in lived interpretation over months or years.

Qualitative interpretive research design

Texts, documents, interviews, field notes collected at multiple time points

Gadamer, H.-G. (1975). Truth and Method (G. Barden & J. Cumming, Trans.). Seabury Press. (Original work published 1960)

Ricoeur, P. (1988). Time and Narrative, Vol. 3 (K. Blamey & D. Pellauer, Trans.). University of Chicago Press.

hermeneutic-analysis longitudinal-qualitative-research narrative-analysis interpretive-phenomenological-analysis longitudinal-case-study discourse-analysis


Longitudinal Historical Archival Research

longitudinal archival study
diachronic archival research
historical longitudinal analysis
archival panel research

20th century (formalized in social science methodology by the 1970s–1990s)

Established practice in historical and social science research traditions

Longitudinal historical archival research is a qualitative and documentary method that systematically examines primary archival sources — records, manuscripts, correspondence, institutional files — across multiple points in time to trace change, continuity, or development within a phenomenon over an extended historical period. By imposing a longitudinal dimension on standard archival inquiry, researchers can reconstruct how events, structures, policies, or social conditions evolved rather than capturing only a single historical moment.

Qualitative/mixed archival research design

Primary archival documents, records, manuscripts, institutional files spanning multiple time points

Scott, J. (1990). A Matter of Record: Documentary Sources in Social Research. Polity Press.

Hill, M. R. (1993). Archival Strategies and Techniques. Sage Publications.

historical-archival-research longitudinal-research document-analysis comparative-historical-analysis oral-history-method content-analysis


Longitudinal Hypothesis Testing Research Design

longitudinal confirmatory study
repeated-measures hypothesis testing
prospective hypothesis testing
longitudinal inferential research

Consolidated as a formal design framework in the 1960s–1980s

Synthesized from longitudinal design traditions (Lazarsfeld, 1940s) and classical hypothesis testing (Fisher, Neyman-Pearson, 1920s–1930s)

Longitudinal hypothesis testing research combines a longitudinal design — measuring the same units repeatedly over time — with formal null-hypothesis significance testing to determine whether observed changes exceed what chance alone can explain. It is widely used in education, medicine, psychology, and social science to test directional predictions about change, stability, or group differences that emerge over a defined time span.

Quantitative longitudinal research design

Repeated measurements on the same subjects across two or more time points

Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press.

Fitzmaurice, G. M., Laird, N. M., & Ware, J. H. (2011). Applied Longitudinal Analysis (2nd ed.). Wiley.

longitudinal-research panel-research hypothesis-testing-research repeated-measures-anova growth-curve-modeling confirmatory-research


Longitudinal In-depth Interview

Longitudinal Qualitative In-depth Interview

repeated in-depth interview
longitudinal qualitative interview
panel qualitative interview
longitudinal IDI

1990s–2000s (as a formalised qualitative method)

Rooted in qualitative longitudinal research traditions; systematised by Johnny Saldana

Longitudinal in-depth interviewing is a qualitative data collection technique in which the same participants are interviewed in depth on multiple occasions across a defined time span. By revisiting the same people over weeks, months, or years, researchers can trace how experiences, identities, attitudes, and meanings change — something a single interview cannot reveal. It is widely used in life-course research, health studies, education, and social policy.

Qualitative longitudinal data collection technique

Repeated verbal/textual data from the same participants over multiple time points

Saldana, J. (2003). Longitudinal Qualitative Research: Analyzing Change Through Time. AltaMira Press.

Farrall, S., & Calverley, A. (2006). Understanding desistance from crime: Theoretical directions in resettlement and rehabilitation. Open University Press.

in-depth-interview semi-structured-interview longitudinal-survey diary-method narrative-analysis panel-study


Longitudinal Institutional Ethnography

longitudinal IE
time-extended institutional ethnography
longitudinal IE study
IE longitudinal design

1987 (IE foundation); longitudinal applications from 1990s onward

Dorothy E. Smith (institutional ethnography); longitudinal extension by subsequent IE practitioners

Longitudinal Institutional Ethnography (longitudinal IE) combines Dorothy Smith's sociology of standpoint — institutional ethnography — with repeated data collection over time to trace how institutional texts, relations, and ruling practices shape people's everyday lives across a temporal span. By revisiting the same participants, settings, or documents at multiple time points, it reveals how institutional coordination evolves, accumulates, or intensifies over weeks, months, or years.

Qualitative longitudinal research design

Interviews, texts, documents, observations collected at multiple time points

Smith, D. E. (2005). Institutional Ethnography: A Sociology for People. AltaMira Press.

DeVault, M. L., & McCoy, L. (2006). Institutional ethnography: Using interviews to investigate ruling relations. In D. E. Smith (Ed.), Institutional Ethnography as Practice (pp. 15–44). Rowman & Littlefield.

institutional-ethnography longitudinal-ethnography longitudinal-case-study participatory-institutional-ethnography critical-institutional-ethnography longitudinal-grounded-theory


L-IPA
longitudinal IPA
repeated-interview IPA
temporal IPA

2000s–2010s (IPA from mid-1990s; longitudinal variant formalised ~2009–2014)

Jonathan A. Smith and colleagues; longitudinal extension developed by Smith, Flowers, and Larkin

Longitudinal Interpretive Phenomenological Analysis (L-IPA) extends the IPA tradition by interviewing the same participants at multiple time points, allowing researchers to trace how the meaning of a lived experience evolves over time. Grounded in phenomenology and hermeneutics, L-IPA preserves idiographic depth at each wave while adding a temporal dimension that cross-sectional IPA cannot provide. It is used widely in health psychology, illness adjustment studies, and any domain where experience unfolds across a significant time span.

Repeated in-depth interview transcripts collected from the same participants at multiple time points

Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretive Phenomenological Analysis: Theory, Method and Research. Sage.

Larkin, M., Watts, S., & Clifton, E. (2006). Giving voice and making sense in interpretive phenomenological analysis. Qualitative Research in Psychology, 3(2), 102–120.

interpretive-phenomenological-analysis phenomenology narrative-analysis thematic-analysis grounded-theory diary-study


Longitudinal Item Analysis

LIA
repeated-measures item analysis
longitudinal item calibration
item parameter stability analysis

Vandenberg, Lance, Meade and colleagues in organizational/educational measurement

Longitudinal item analysis examines how the statistical properties of individual scale items — difficulty, discrimination, factor loadings, and fit — remain stable or change systematically across repeated measurement occasions. It is the item-level foundation of longitudinal measurement validity.

Repeated-measures item response data (ordinal or binary) collected at two or more time points

Meade, A. W., Johnson, E. C. & Braddy, P. W. (2008). Power and sensitivity of alternative fit indices in tests of measurement invariance. Journal of Applied Psychology, 93(3), 568–592.

Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.


Longitudinal IRT

LIRT
longitudinal IRT
repeated-measures IRT
dynamic item response modeling

Longitudinal IRT extends classical item response theory to data collected at multiple time points, allowing researchers to model both the initial latent trait level and its change over time. It is used in educational assessment, clinical trials, and panel studies where the same items or item banks are administered repeatedly to the same individuals.

Latent trait / longitudinal psychometric model

Repeated dichotomous or polytomous item responses across time points

Embretson, S. E. (1991). A multidimensional latent trait model for measuring learning and change. Psychometrika, 56(3), 495–515.

von Davier, M. & Carstensen, C. H. (Eds.) (2007). Multivariate and Mixture Distribution Rasch Models: Extensions and Applications. Springer.


Longitudinal Life history research

longitudinal biographical research
life history longitudinal design
repeated life history study
longitudinal oral biography

1918 (origins); longitudinal application developed from 1980s onward

Thomas & Znaniecki (Polish Peasant, 1918–1920); elaborated by Ken Plummer, Daniel Bertaux

Longitudinal life history research follows the same participants across multiple points in time, collecting repeated in-depth accounts of how their life stories evolve, how they narrate past events differently over time, and how biography intersects with social change. It combines the interpretive depth of life history methodology with the temporal sensitivity of longitudinal design, capturing both the content of lived experience and its unfolding across the life course.

Repeated in-depth interviews, personal documents, diaries, correspondence

Plummer, K. (2001). Documents of Life 2: An Invitation to a Critical Humanism. Sage.

Thomson, R. (2009). Unfolding Lives: Youth, Gender and Change. Policy Press.

life-history-research biographical-research longitudinal-narrative-research longitudinal-case-study narrative-inquiry oral-history


Longitudinal McDonald's omega

Longitudinal McDonald's Omega Reliability Coefficient

longitudinal omega
omega longitudinal reliability
time-varying omega
repeated-measures omega

1999 (original omega); 2014 (longitudinal extension)

McDonald (1999); extended to longitudinal contexts by Geldhof, Preacher, and Zyphur (2014) and subsequent authors

Longitudinal McDonald's omega estimates scale reliability separately at each measurement occasion in a panel or repeated-measures study. By fitting a confirmatory factor model at each wave, it tracks how consistently a set of items measures its target construct over time, detecting erosion or improvement in measurement quality that a single omnibus reliability coefficient would obscure.

Reliability / internal consistency coefficient

Repeated measures / panel data with multiple indicators per time point

McDonald, R. P. (1999). Test Theory: A Unified Treatment. Lawrence Erlbaum Associates.

Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19(1), 72–91.

mcdonalds-omega cronbach-alpha confirmatory-factor-analysis longitudinal-sem multilevel-reliability test-retest-reliability


Longitudinal Measurement Invariance

Longitudinal Measurement Invariance Testing

LMI
longitudinal invariance
measurement equivalence across time
temporal measurement invariance

Longitudinal measurement invariance testing determines whether a psychological scale measures the same construct in the same way across two or more time points. It is a prerequisite for interpreting mean-level change scores in panel and repeated-measures studies, ensuring that observed change reflects true change in the construct rather than drift in the measurement instrument.

Ordinal or continuous repeated-measure indicators

Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525–543.

confirmatory-factor-analysis structural-equation-modeling multigroup-confirmatory-factor-analysis measurement-invariance latent-growth-curve-modeling repeated-measures-analysis


Longitudinal Metaphor Analysis

LMA
diachronic metaphor analysis
longitudinal conceptual metaphor study
repeated-measures metaphor analysis

2000s–2010s (systematic longitudinal application)

Lynne Cameron and colleagues (discourse dynamics framework); broader tradition rooted in Lakoff & Johnson's conceptual metaphor theory (1980)

Longitudinal Metaphor Analysis (LMA) is a qualitative method that tracks how individuals or groups use metaphors across multiple time points to reveal conceptual, attitudinal, or identity shifts. Grounded in conceptual metaphor theory and discourse dynamics, it treats metaphor not as mere rhetorical decoration but as a window into evolving thought, belief, and meaning-making over time.

Transcripts, interviews, written texts, diaries, or discourse data collected at multiple time points

Cameron, L., & Maslen, R. (Eds.). (2010). Metaphor Analysis: Research Practice in Applied Linguistics, Social Sciences and the Humanities. Equinox.

Cameron, L., Maslen, R., Todd, Z., Maule, J., Stratton, P., & Stanley, N. (2009). The discourse dynamics approach to metaphor and metaphor-led discourse analysis. Metaphor and Symbol, 24(2), 63–89.

metaphor-analysis discourse-analysis narrative-analysis content-analysis thematic-analysis longitudinal-qualitative-research


Longitudinal Mobile Experience Sampling

Longitudinal Mobile Experience Sampling Method

Longitudinal Ecological Momentary Assessment

1983 (ESM origins); 2000s onward (mobile longitudinal variants)

Csikszentmihalyi & Larson (ESM, 1983); Shiffman, Stone & Hufford (EMA, 2008); extended to longitudinal mobile designs by Hamaker and colleagues

Longitudinal Mobile Experience Sampling combines the real-time, in-context signal capture of Experience Sampling Method (ESM) with a longitudinal design spanning weeks, months, or longer. Participants respond to repeated prompts delivered to their smartphones across multiple time waves, enabling researchers to observe within-person change, stability, and dynamic processes as they unfold in daily life rather than in retrospective recall.

Longitudinal intensive data collection technique

Real-time self-reports, behavioral signals, physiological sensor data

Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.

Hamaker, E. L., & Wichers, M. (2017). No time like the present: Discovering the hidden dynamics in intensive longitudinal data. Current Directions in Psychological Science, 26(1), 10–15.

mobile-experience-sampling experience-sampling-method longitudinal-survey ecological-momentary-assessment diary-method longitudinal-diary-method


Longitudinal Model Testing Research

longitudinal confirmatory modeling
longitudinal SEM
panel model testing
longitudinal structural modeling

1970s–1990s (SEM foundations by Joreskog 1970; longitudinal SEM elaborated through 1990s–2000s)

Synthesized from longitudinal panel design and SEM tradition (Joreskog, Bollen, Singer & Willett)

Longitudinal model testing research combines repeated measurement across time with formal, a priori structural modeling to confirm or disconfirm hypothesized relationships among constructs. Rather than simply describing change, it tests whether a pre-specified theoretical model — typically a structural equation model or growth model — fits observed data collected at two or more time points. This design supports causal inference more convincingly than cross-sectional approaches by capturing temporal ordering of variables.

Quantitative, confirmatory, longitudinal design

Repeated-measures quantitative data (panel data, survey waves, archival records collected at two or more time points)

Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press.

Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press.

longitudinal-research model-testing-research confirmatory-research panel-research longitudinal-confirmatory-research structural-equation-modeling


Longitudinal Multiple case study

Longitudinal Multiple Case Study Research

longitudinal multi-case study
repeated multiple case study
panel case study
multi-site longitudinal case study

1980s–2000s (Yin's multiple-case framework c. 1984; longitudinal qualitative elaboration c. 2003)

Robert K. Yin (multiple case design); Johnny Saldana (longitudinal qualitative methods)

Longitudinal multiple case study is a qualitative research design that examines two or more bounded cases through repeated data-collection waves over an extended period. By tracking each case across time and comparing patterns across cases, researchers can document how phenomena change, stabilise, or diverge — generating both depth within each site and breadth across sites that neither a single case nor a one-shot survey can provide.

Interviews, observations, documents collected at multiple time points across multiple cases

Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.

Saldana, J. (2003). Longitudinal Qualitative Research: Analyzing Change Through Time. AltaMira Press.

multiple-case-study longitudinal-case-study comparative-case-study longitudinal-ethnography longitudinal-narrative-research case-study


Longitudinal Narrative Research

longitudinal narrative inquiry
narrative longitudinal design
LNI
temporal narrative research

1990s–2000s (narrative inquiry established 1990; longitudinal application elaborated 2000s–2010s)

D. Jean Clandinin & F. Michael Connelly (narrative inquiry foundations); extended into longitudinal designs by Clandinin and colleagues

Longitudinal narrative research is a qualitative design that follows participants across multiple time points, gathering and analyzing their stories to understand how experiences, identities, and meanings evolve over time. Rooted in Clandinin and Connelly's narrative inquiry tradition, it treats human experience as fundamentally storied and temporal — what matters is not just what happened but how people narrate, revise, and make sense of their lives as circumstances change.

Repeated narrative interviews, field texts, journals, documents collected over multiple time points

Clandinin, D. J., Huber, J., Huber, M., Murphy, M. S., Murray Orr, A., Pearce, M., & Steeves, P. (2006). Composing diverse identities: Narrative inquiries into the interwoven lives of children and teachers. Routledge.

Clandinin, D. J. (2016). Engaging in Narrative Inquiry. Left Coast Press / Routledge.


Longitudinal Netnography

longitudinal online ethnography
temporal netnography
long-term netnography
diachronic netnography

1997 (netnography); longitudinal application developed 2000s–2010s

Robert V. Kozinets (netnography); longitudinal extension by subsequent researchers

Longitudinal netnography applies the systematic, immersive online ethnographic method developed by Kozinets across multiple time points to reveal how digital communities, cultural practices, and shared meanings evolve. Rather than offering a snapshot of online life, it tracks the same community or platform over weeks, months, or years, capturing change, continuity, and the temporal rhythms of internet culture.

Longitudinal qualitative online research design

Online community data: posts, threads, comments, profiles collected across multiple time points

Kozinets, R. V. (2020). Netnography: The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage.

Mkono, M., & Markwell, K. (2014). The application of netnography in tourism research: Revolution or evolution? Journal of Travel and Tourism Marketing, 31(4), 453–465.


Longitudinal Nomological Validity

Longitudinal Nomological Validity Assessment

nomological network validation across time

1955 (concept); longitudinal extension 1990s–2000s

Cronbach & Meehl (nomological network concept, 1955); longitudinal extension developed in organizational and personality research from the 1990s onward

Longitudinal nomological validity evaluates whether a construct's theoretically predicted relationships with other constructs hold consistently across multiple measurement occasions. It extends the nomological network framework of Cronbach and Meehl (1955) to longitudinal designs, testing whether a scale behaves as theory demands not only at a single time point but over time.

Repeated-measures / longitudinal scale scores, latent variable scores

Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.

Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.


extended non-participant field observation

Early–mid 20th century (systematized 1920s–1970s)

Rooted in sociological field research traditions (e.g., Chicago School); longitudinal extension developed through 20th-century social science methodology

Longitudinal non-participant observation is a data collection method in which a researcher systematically watches and records naturally occurring behaviors, interactions, or events at a setting over multiple, repeated observation sessions spanning weeks, months, or years — without joining or influencing the activities being observed. The researcher remains an external observer, producing a time-ordered record of change or continuity in the phenomenon under study.

Longitudinal observational data collection

Field notes, observational records, structured observation logs collected at multiple time points

Angrosino, M. (2007). Doing Ethnographic and Observational Research. Sage Publications.

Menard, S. (Ed.). (2002). Longitudinal Research (2nd ed.). Sage Publications.

non-participant-observation longitudinal-participant-observation longitudinal-field-notes participant-observation longitudinal-survey ethnography


Longitudinal oral history method

repeated oral history interviewing
longitudinal life history
serial oral history
longitudinal biographical interviewing

1970s–1980s (systematic formulation); longstanding practice in oral history

Paul Thompson; developed further by Ken Plummer and oral history practitioners

Longitudinal oral history method is a qualitative research design in which the same participants are interviewed repeatedly over an extended period — months or years — using oral history interviewing techniques. By returning to narrators across time, researchers can trace how personal accounts, identities, and interpretations of experience shift and evolve, capturing the processual and biographical dimensions of social life that a single interview cannot reveal.

Repeated in-depth oral history interviews with the same participants over time

Thompson, P. (2000). The Voice of the Past: Oral History (3rd ed.). Oxford University Press.

Plummer, K. (2001). Documents of Life 2: An Invitation to a Critical Humanism. Sage.

oral-history-method life-history-research longitudinal-qualitative-research narrative-analysis biographical-method phenomenology


Longitudinal Oral History

repeated oral history
serial oral history
life-course oral history
longitudinal life narrative

1970s–1990s (formalized as distinct variant)

Allan Nevins (oral history); longitudinal variant developed across life-course sociology and oral history practice from 1970s–1990s

Longitudinal oral history is a qualitative research design in which the same participants are interviewed repeatedly over an extended period — months or years — using open-ended, narrative-focused conversations. By revisiting participants at multiple points in time, the researcher traces how individuals construct, revise, and reinterpret their personal stories as their lives unfold, capturing not just retrospective accounts but the dynamic, evolving nature of memory and meaning-making.

Repeated in-depth interviews, personal narratives, biographical accounts

Thomson, A. (2007). Four paradigm transformations in oral history. The Oral History Review, 34(1), 49–70.

Bornat, J. (2008). Biographical methods. In L. Given (Ed.), The SAGE Encyclopedia of Qualitative Research Methods. SAGE.

oral-history narrative-inquiry life-history-research biographical-method longitudinal-qualitative-research phenomenology


Longitudinal Phenomenology

longitudinal phenomenological inquiry
temporal phenomenology
repeated-interview phenomenology
longitudinal lived-experience research

Draws on Husserl and Heidegger's phenomenological tradition; longitudinal application developed in qualitative research (Saldana, Thomson et al., early 2000s)

Longitudinal phenomenology applies phenomenological inquiry across two or more time points to capture how participants' lived experience of a phenomenon changes, deepens, or transforms over time. Rooted in the phenomenological tradition of Husserl and Heidegger, it adds an explicit temporal dimension — asking not only what an experience is like, but how it evolves. It is used where a single-point interview would miss the processual, shifting nature of lived meaning.

Repeated in-depth interviews, field notes, participant diaries (text data across multiple time points)

Saldana, J. (2003). Longitudinal Qualitative Research: Analyzing Change through Time. AltaMira Press.

Thomson, R., Bell, R., Holland, J., Henderson, S., McGrellis, S., & Sharpe, S. (2002). Critical moments: Choice, chance and opportunity in young people's narratives of transition. Sociology, 36(2), 335–354.


Longitudinal Program Evaluation

LPE
longitudinal evaluation
long-term program evaluation
prospective program evaluation

1960s–1970s (program evaluation); longitudinal designs formalized 1970s–1980s

Peter Rossi, Michael Scriven, Donald Campbell (program evaluation tradition)

Longitudinal program evaluation is an applied research design that tracks the outcomes and processes of a program or intervention across multiple time points — from pre-implementation baseline through medium- and long-term follow-up. Unlike single-point evaluations, it captures how program effects emerge, fade, or evolve over time, enabling evaluators and funders to judge sustained impact, cost-effectiveness, and unintended consequences that would be invisible in a snapshot assessment.

Quantitative outcomes, administrative records, survey panels, qualitative follow-up interviews

Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2004). Evaluation: A Systematic Approach (7th ed.). Sage Publications.

Shadish, W. R., Cook, T. D., & Leviton, L. C. (1991). Foundations of Program Evaluation: Theories of Practice. Sage Publications.

program-evaluation longitudinal-research outcome-evaluation formative-evaluation summative-evaluation interrupted-time-series


Longitudinal Qualitative Content Analysis

LQCA
longitudinal QCA
repeated qualitative content analysis
panel qualitative content analysis

Philipp Mayring (foundational QCA); longitudinal extension developed across qualitative health and social research traditions

Longitudinal qualitative content analysis (LQCA) applies systematic content analysis to text data gathered from the same participants, settings, or documents at two or more points in time. The method preserves the interpretive rigour of qualitative content analysis while adding an explicit temporal dimension, enabling researchers to track how meanings, experiences, categories, or discourse shift, deepen, or stabilise across time rather than producing a single-point-in-time description.

Text data collected at multiple time points (interviews, documents, field notes, open-ended surveys)

Mayring, P. (2000). Qualitative content analysis. Forum: Qualitative Social Research, 1(2), Art. 20.

Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288.

qualitative-content-analysis thematic-analysis narrative-analysis longitudinal-qualitative-research framework-analysis interpretive-phenomenological-analysis


Longitudinal Quantitative Content Analysis

longitudinal content analysis
repeated-measure content analysis
time-series content analysis
longitudinal QCA

1950s onward; longitudinal application widely adopted in media research by the 1970s–1980s

Developed within communication and media studies; codified by Berelson (1952) and extended by Riffe, Lacy, Fico

Longitudinal quantitative content analysis systematically codes and counts features of texts, images, or media messages gathered at two or more points in time, enabling researchers to track how content changes, how themes rise or fall in prevalence, and how media or institutional messaging responds to external events. The design merges the structured measurement logic of quantitative content analysis with the temporal tracking power of longitudinal observation.

Quantitative observational research design

Coded textual, visual, or media content collected across multiple time points

Riffe, D., Lacy, S., Watson, B., & Fico, F. (2019). Analyzing Media Messages: Using Quantitative Content Analysis in Research (4th ed.). Routledge.

Neuendorf, K. A. (2017). The Content Analysis Guidebook (2nd ed.). Sage.

quantitative-content-analysis longitudinal-research trend-research panel-research time-series-analysis descriptive-research


Longitudinal Reflexive thematic analysis

longitudinal qualitative thematic analysis

2006 (RTA seminal); longitudinal application developed through 2010s

Virginia Braun & Victoria Clarke (reflexive thematic analysis); longitudinal design adapted from qualitative longitudinal research traditions (Saldaña, 2003)

Longitudinal Reflexive Thematic Analysis (L-RTA) applies Braun and Clarke's reflexive thematic analysis framework to qualitative data collected from the same participants (or context) at two or more time points. Rather than producing a single static account, it tracks how meanings, experiences, and themes evolve, persist, or transform over time, foregrounding the researcher's active reflexive engagement at every stage of the iterative process.

Qualitative analytic method applied longitudinally

Repeated qualitative data collections (interviews, focus groups, diaries) across two or more time points

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.


Longitudinal relational survey

longitudinal correlational survey
prospective relational survey
repeated-measures relational survey
panel relational survey

1960s–1980s (formalized in panel and longitudinal survey literature)

Classical survey methodology (Campbell & Stanley, 1963; Kessler & Greenberg, 1981)

A longitudinal relational survey follows the same sample at two or more time points, collecting structured questionnaire data each wave and examining how the relationships among variables change, strengthen, weaken, or emerge across time. Unlike a cross-sectional relational survey that offers a single snapshot, this design captures temporal dynamics and allows researchers to test whether earlier measurements predict later outcomes, making it valuable for studying development, attitude change, and causal ordering.

Structured questionnaire or scale data collected at two or more time points from the same sample

Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press.

Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed.). Guilford Press.

longitudinal-research relational-survey panel-research cohort-research correlational-research cross-lagged-panel-model


Longitudinal Reliability Analysis

repeated-measures reliability
longitudinal consistency assessment
temporal reliability analysis
reliability over time

Paul B. Baltes, John R. Nesselroade, Lee J. Cronbach (foundational contributors)

Longitudinal reliability analysis evaluates the consistency and stability of measurement instruments across two or more time points. It extends classical reliability concepts — internal consistency, test-retest stability, and measurement precision — to repeated-measures designs, ensuring that observed score changes reflect true change rather than measurement error.

Repeated-measures ordinal or continuous scale scores

Baltes, P. B., & Nesselroade, J. R. (1979). History and rationale of longitudinal research. In J. R. Nesselroade & P. B. Baltes (Eds.), Longitudinal research in the study of behavior and development (pp. 1–39). Academic Press.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.


Longitudinal Research Diary

longitudinal reflexive journal
longitudinal researcher diary
longitudinal field diary
longitudinal research log

Rooted in Zimmerman & Wieder's diary-interview method (1977); developed further in qualitative longitudinal research through the 1980s–1990s

A longitudinal research diary is a structured, ongoing record kept by the researcher throughout an extended study, capturing observations, decisions, emerging insights, and methodological reflections at repeated intervals over weeks, months, or years. It functions simultaneously as a reflexivity tool and a secondary data source, documenting how the inquiry evolves, how researcher positionality shifts, and how contextual changes influence the data collection process across time.

Researcher-authored textual records: observations, reflections, methodological notes, analytic memos

Zimmerman, D. H., & Wieder, D. L. (1977). The diary: Diary-interview method. Urban Life, 5(4), 479–498.

research-diary longitudinal-field-notes longitudinal-participant-observation diary-method field-notes longitudinal-survey


Longitudinal Research

longitudinal study
longitudinal design
prospective longitudinal study
repeated-measures observational study

Late 19th–early 20th century; methodologically codified through the 20th century

No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett

Longitudinal research is an observational design in which the same participants, groups, or units are measured repeatedly over an extended period. Rather than capturing a single snapshot, it tracks change, stability, and temporal sequencing of variables — making it the primary non-experimental strategy for studying development, growth, decline, and the unfolding of causal processes across time.

Quantitative (or mixed) observational research design

Repeated quantitative measurements from the same participants or units over time

Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications.

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Longitudinal scale development

LSD
longitudinal measurement development
repeated-measures scale construction
scale development with panel data

Longitudinal scale development is the systematic process of constructing and validating a measurement instrument using data collected at multiple time points. It extends classical scale development by additionally testing whether the scale measures the same construct in the same metric across occasions, enabling valid tracking of change over time.

Millsap, R. E. (2011). Statistical Approaches to Measurement Invariance. Routledge.

Little, T. D. (2013). Longitudinal Structural Equation Modeling. Guilford Press.


Longitudinal Semi-structured Interview

LSI
repeated semi-structured interview
panel qualitative interview
longitudinal qualitative interview

Rooted in longitudinal qualitative research traditions; systematised by Johnny Saldana and Rachel Thomson & Janet Holland

A longitudinal semi-structured interview study collects open-ended, guided interview data from the same participants across multiple time points. By returning to the same individuals — weeks, months, or years apart — researchers can trace how experiences, perceptions, and meanings change over time. The approach blends the flexibility of qualitative inquiry with the temporal depth that is impossible in a one-shot design, making it a cornerstone method in qualitative longitudinal research.

Audio-recorded or transcribed interview text collected at multiple time points

Saldana, J. (2003). Longitudinal Qualitative Research: Analyzing Change Through Time. AltaMira Press.

Thomson, R., & Holland, J. (2003). Hindsight, foresight and insight: The challenges of longitudinal qualitative research. International Journal of Social Research Methodology, 6(3), 233–244.

semi-structured-interview longitudinal-survey longitudinal-in-depth-interview longitudinal-focus-group diary-method narrative-analysis


Longitudinal Sensor Data Collection

Longitudinal Sensor-Based Data Collection

long-term sensor monitoring
longitudinal sensing
continuous sensor logging
repeated-measures sensor collection

1990s–2000s (accelerated with IoT and wearable devices from ~2010)

Emerging from ambulatory assessment and wearable technology research communities

Longitudinal sensor data collection deploys physical or digital sensors to record phenomena continuously or at regular intervals across an extended study period — days, months, or years. Unlike one-shot measurement, the repeated temporal structure captures change, trajectory, and variability in outcomes such as physical activity, environmental exposure, sleep, or physiological state. The approach combines the ecological validity of real-world sensing with the analytical power of longitudinal design.

Longitudinal quantitative/mixed data collection technique

Continuous or episodic sensor readings (physiological, environmental, accelerometric, GPS, etc.) collected repeatedly over time

Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2005). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14(4), 671–694. [For longitudinal intensive repeated-measures designs context, see also: Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.]

Stone, A. A., & Shiffman, S. (2002). Capturing momentary, self-report data: A proposal for reporting guidelines. Annals of Behavioral Medicine, 24(3), 236–243.

sensor-data-collection longitudinal-survey mobile-experience-sampling-method experience-sampling-method ecological-momentary-assessment time-series-analysis


Longitudinal Single Case Study

single-case longitudinal design
in-depth longitudinal case study
idiographic longitudinal study
LSCS

1984 (Yin's foundational codification); longitudinal case methods in use since early 20th century

Robert K. Yin (systematic codification); roots in clinical and anthropological case tradition

A longitudinal single case study is a qualitative research design that follows one bounded unit — a person, organization, program, or community — through multiple points in time. Unlike a cross-sectional snapshot, it captures how phenomena develop, shift, or respond to events across months or years, combining the contextual richness of case study methodology with the temporal depth needed to understand process and change.

Interviews, observations, documents, archival records collected at multiple time points from a single bounded unit

Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.

Gerring, J. (2007). Case Study Research: Principles and Practices. Cambridge University Press.

case-study multiple-case-study ethnography narrative-analysis action-research process-tracing


Longitudinal Straussian Grounded Theory

Strauss-Corbin longitudinal grounded theory

longitudinal constructivist grounded theory

1990s–2000s (systematic longitudinal application emerged ~2000–2010)

Anselm Strauss & Juliet Corbin (grounded theory basis); extended by qualitative longitudinal researchers (e.g., Bren Neale, Julia Brannen)

Longitudinal Straussian Grounded Theory applies the systematic coding procedures of Strauss and Corbin's grounded theory — open, axial, and selective coding — to data gathered across multiple time points. Rather than producing a static snapshot of a social phenomenon, it tracks how processes, identities, or conditions evolve, generating a substantive theory grounded in change over time. It is particularly powerful for studying social processes that unfold across months or years.

Qualitative research design and analytic approach

Repeated-wave qualitative interviews, field notes, documents collected over time

Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). Sage.

Bartlett, D., & Milligan, C. (2015). What is Diary Method? Bloomsbury Academic. [See also: Neale, B. (2021). Qualitative Longitudinal Research: Research Methods. Bloomsbury Academic.]

grounded-theory constructivist-grounded-theory longitudinal-qualitative-research narrative-analysis thematic-analysis case-study


Longitudinal Structured Interview

panel structured interview
repeated structured interview
longitudinal survey interview
wave-based structured interview

Established practice in panel and cohort research; codified in survey methodology literature from the 1970s onward

A longitudinal structured interview applies a fixed, standardised interview schedule to the same participants at two or more points in time. By holding the instrument constant across waves, the method enables genuine within-person change to be measured, trends to be tracked, and causal sequences to be examined with far greater confidence than a single cross-sectional interview can provide. It is widely used in panel studies, cohort research, and programme evaluations.

Longitudinal quantitative data collection method

Structured verbal responses collected across multiple time points

Lynn, P. (Ed.). (2009). Methodology of Longitudinal Surveys. Wiley.

structured-interview longitudinal-survey panel-study semi-structured-interview cohort-study repeated-measures-design


Longitudinal Survey Research

longitudinal survey study
repeated-measures survey
prospective survey design
panel survey

Mid-20th century (formalized ~1950s–1970s)

Survey methodology tradition; codified in social sciences by scholars including W.S. Robinson (1950) and later Scott Menard

Longitudinal survey research collects structured questionnaire data from the same individuals (or units) at two or more points in time. Unlike a one-shot cross-sectional survey, this design captures change, stability, and temporal ordering of variables — enabling researchers to track trajectories, test causal sequences, and distinguish cohort effects from aging effects within a quantitative framework.

Repeated structured survey responses from the same sample over time

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