Relevant bibliographies by topics / Hierarchical regression

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Hierarchical regression'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Hierarchical regression.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Hierarchical regression"

1

Weng, J., and W. S. Hwang. "Hierarchical discriminant regression." IEEE Transactions on Pattern Analysis and Machine Intelligence 22, no. 11 (2000): 1277–93. http://dx.doi.org/10.1109/34.888712.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Yucel, Recai M., Enxu Zhao, Nathaniel Schenker, and Trivellore E. Raghunathan. "Sequential Hierarchical Regression Imputation." Journal of Survey Statistics and Methodology 6, no. 1 (November 1, 2017): 1–22. http://dx.doi.org/10.1093/jssam/smx004.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Weng, Juyang, and Wey-Shiuan Hwang. "Incremental Hierarchical Discriminant Regression." IEEE Transactions on Neural Networks 18, no. 2 (March 2007): 397–415. http://dx.doi.org/10.1109/tnn.2006.889942.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Christiansen, Cindy L., and Carl N. Morris. "Hierarchical Poisson Regression Modeling." Journal of the American Statistical Association 92, no. 438 (June 1997): 618–32. http://dx.doi.org/10.1080/01621459.1997.10474013.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Chen, Yanliang, Man-Lai Tang, and Maozai Tian. "Semiparametric Hierarchical Composite Quantile Regression." Communications in Statistics - Theory and Methods 44, no. 5 (March 4, 2015): 996–1012. http://dx.doi.org/10.1080/03610926.2012.755199.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Xue, Fengchang. "Hierarchical Geographically Weighted Regression Model." Journal of Quantum Computing 1, no. 1 (2019): 9–20. http://dx.doi.org/10.32604/jqc.2019.05954.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Bentler, Peter M., and Albert Satorra. "Hierarchical Regression Without Phantom Factors." Structural Equation Modeling: A Multidisciplinary Journal 7, no. 2 (June 2000): 287–91. http://dx.doi.org/10.1207/s15328007sem0702_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Bertsimas, Dimitris, and Bart Van Parys. "Sparse hierarchical regression with polynomials." Machine Learning 109, no. 5 (January 24, 2020): 973–97. http://dx.doi.org/10.1007/s10994-020-05868-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

WITTE, JOHN S., and SANDER GREENLAND. "SIMULATION STUDY OF HIERARCHICAL REGRESSION." Statistics in Medicine 15, no. 11 (June 15, 1996): 1161–70. http://dx.doi.org/10.1002/(sici)1097-0258(19960615)15:11<1161::aid-sim221>3.0.co;2-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Woodard, Dawn B., Ciprian Crainiceanu, and David Ruppert. "Hierarchical Adaptive Regression Kernels for Regression With Functional Predictors." Journal of Computational and Graphical Statistics 22, no. 4 (October 2013): 777–800. http://dx.doi.org/10.1080/10618600.2012.694765.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Hierarchical regression"

1

Nilsson, Fredrik. "Payment Volume Forecasting using Hierarchical Regression with SARIMA Errors : Payment Volume Forecasting using Hierarchical Regression with SARIMA Errors." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425886.

Full text
Abstract:
When forecasting financial transaction volumes in different markets, different markets often exhibit similar seasonality patterns and public holiday behavior. In this thesis, an attempt is made at utilizing these similarities to improve forecasting accuracy as compared to forecasting each market individually. Bayesian hierarchical regression models with time series errors are used on daily transaction data. When fitting three years of historic data for all markets, no consistent significant improvements in forecasting accuracy was found over a non-hierarchical regression model. When the amount of historic data was limited to less than one year for a single market, with the other markets having three years of historic data, the hierarchical model significantly outperformed both non-hierarchical and naive reference models on the market with limited historic data.
APA, Harvard, Vancouver, ISO, and other styles
2

Prokopenko, Sergiy. "Hierarchical binary spatial regression models with cluster effects." [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=972223851.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Bao, Haikun. "Bayesian hierarchical regression model to detect quantitative trait loci /." Electronic version (PDF), 2006. http://dl.uncw.edu/etd/2006/baoh/haikunbao.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Wang, Chia-Fu. "A hierarchical Gamma/Weibull regression model for target detection times." Thesis, Monterey, California: Naval Postgraduate School, 1990. http://hdl.handle.net/10945/34954.

Full text
Abstract:
Approved for public release; distribution unlimited.
Combat models often involve target detection times which may vary with different observers due to characteristics of personnel, or detection systems. They may also be affected by different environmental factors such as visual levels, sea states, terrains, etc. There is often interest in quantifying the effects of different observer characteristics and environmental factors on detection times. A hierarchical gammaWeibull regression model is considered which can incorporate observer characteristics and environmental effects which may influence the time to detect targets. Numerical procedures for the estimation of parameters of the hierarchical gammaWeibull model based on maximum likelihood are described. Results of simulation experiments to study small sample behavior of the estimates are reported.
APA, Harvard, Vancouver, ISO, and other styles
5

Gschlössl, Susanne. "Hierarchical Bayesian spatial regression models with applications to non-life insurance." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=978924576.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Roualdes, Edward A. "New Results in ell_1 Penalized Regression." UKnowledge, 2015. http://uknowledge.uky.edu/statistics_etds/13.

Full text
Abstract:
Here we consider penalized regression methods, and extend on the results surrounding the l1 norm penalty. We address a more recent development that generalizes previous methods by penalizing a linear transformation of the coefficients of interest instead of penalizing just the coefficients themselves. We introduce an approximate algorithm to fit this generalization and a fully Bayesian hierarchical model that is a direct analogue of the frequentist version. A number of benefits are derived from the Bayesian persepective; most notably choice of the tuning parameter and natural means to estimate the variation of estimates – a notoriously difficult task for the frequentist formulation. We then introduce Bayesian trend filtering which exemplifies the benefits of our Bayesian version. Bayesian trend filtering is shown to be an empirically strong technique for fitting univariate, nonparametric regression. Through a simulation study, we show that Bayesian trend filtering reduces prediction error and attains more accurate coverage probabilities over the frequentist method. We then apply Bayesian trend filtering to real data sets, where our method is quite competitive against a number of other popular nonparametric methods.
APA, Harvard, Vancouver, ISO, and other styles
7

Ho, Yu-Yun. "Diagnostics for hierarchical Bayesian regression models with application to repeated measures data /." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487856906261615.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Acar, Emel. "Extensions of quantal problems." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322928.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Gordon, Shelley S. (Shelley Sampson). "Strategic Reorientation in the Computer Software and Furniture Industries: a Hierarchical Regression Analysis." Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc279149/.

Full text
Abstract:
Insufficient literature exists in the area of incremental and revolutionary change to explain and predict the convergence and reorientation phenomena happening in organizations. The process of strategic reorientation involves the internal organizational complexities of fast-paced (within two years) changes in competitive strategy as a necessary condition coupled with changes in at least two of organization structure, power distribution, and control systems. Antecedent forces believed to influence the discontinuous change process include industry sales turbulence, structural inertia/firm size, firm past financial performance, CEO turnover, top management team turnover, management team heterogeneity, management environmental awareness, and external attributions for negative financial performance. Punctuated equilibrium was the foundational theory for this study in which a strategic reorientation model published in Strategic Management Journal was reconstructed. The research question was: What seem to be the significant time-based antecedent forces or conditions that lead to strategic reorientation? The study used two hierarchical logit regression models to analyze data gathered from COMPUSTAT PC Industrial Data Base and Compact Disclosure (CD-ROM) over the years 1987-1993 from the turbulent computer software and stable furniture industries. Qualitative data were found in 10-K reports and President's Letters in Annual Reports filed with the SEC and available on Laserdisclosure. The sample, exclusive of 3 multivariate outliers, included 74 software firms and 43 furniture firms for a pooled total of 117 firms. When separate industries were analyzed using the first of the Systat logit hierarchical regressions, results showed no statistically significant effects. By contrast, when data were pooled, the second hierarchical logit regression model, which included industry turbulence and firm size, showed these one-tailed statistically significant results: strategic reorientation is positively affected by prior industry turbulence and CEO turnover, but is negatively affected by prior top management team turnover and the interaction between industry turbulence and external attributions for negative financial performance.
APA, Harvard, Vancouver, ISO, and other styles
10

Olsen, Andrew Nolan. "Hierarchical Bayesian Methods for Evaluation of Traffic Project Efficacy." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2922.

Full text
Abstract:
A main objective of Departments of Transportation is to improve the safety of the roadways over which they have jurisdiction. Safety projects, such as cable barriers and raised medians, are utilized to reduce both crash frequency and crash severity. The efficacy of these projects must be evaluated in order to use resources in the best way possible. Five models are proposed for the evaluation of traffic projects: (1) a Bayesian Poisson regression model; (2) a hierarchical Poisson regression model building on model (1) by adding hyperpriors; (3) a similar model correcting for overdispersion; (4) a dynamic linear model; and (5) a traditional before-after study model. Evaluation of these models is discussed using various metrics including DIC. Using the models selected for analysis, it was determined that cable barriers are quite effective at reducing severe crashes and cross-median crashes on Utah highways. Raised medians are also largely effective at reducing severe crashes. The results of before and after analyses are highly valuable to Departments of Transportation in identifying effective projects and in determining which roadway segments will benefit most from their implementation.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Hierarchical regression"

1

Gelman, Andrew. Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press, 2007.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Ward, Joe H. General applications of hierarchical grouping using the HIER-GRP computer program. Brooks Air Force Base, Tex: Air Force Human Resources Laboratory, Air Force Systems Command, 1985.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Hesselager, Ole. Estimation of Variance Components in Hierarchical Regression Models with Nested Classification. Copenhagen: Laboratory of Actuarial Mathematics, University of Copenhagen, 1988.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

To, Yen, and Jon Mandracchia. Learn About Hierarchical Linear Regression in SPSS With Data From Prison Inmates. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526494290.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Gaver, Donald Paul. Regression analysis of hierarchical Poisson-like event rate data: Superpopulation model effect on predictions. Monterey, Calif: Naval Postgraduate School, 1990.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

Full text
Abstract:
The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluation, analysis, and practical implementation of Box — Jenkins stationary time series models, as well as multidimensional time series models: vector autoregressive models and vector error correction models. It includes basic econometric models for panel data that have been widely used in recent decades, as well as formal tests for selecting models based on their hierarchical structure. Each section provides examples of evaluating, analyzing, and testing models in the R software environment. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is addressed to master's students studying in the Field of Economics, the curriculum of which includes the disciplines Econometrics (advanced course)", "Econometric modeling", "Econometric research", and graduate students."
APA, Harvard, Vancouver, ISO, and other styles
7

Gelman, Andrew, and Jennifer Hill. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, 2006.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Gelman, Andrew, and Jennifer Hill. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, 2006.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Gelman, Andrew. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, 2006.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Gelman, Andrew, and Jennifer Hill. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, 2012.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Hierarchical regression"

1

Cowles, Mary Kathryn. "Regression and Hierarchical Regression Models." In Springer Texts in Statistics, 179–205. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5696-4_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Bellocchio, Francesco, N. Alberto Borghese, Stefano Ferrari, and Vincenzo Piuri. "Hierarchical Support Vector Regression." In 3D Surface Reconstruction, 111–42. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5632-2_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Paquet, Ulrich, Sean Holden, and Andrew Naish-Guzman. "Bayesian Hierarchical Ordinal Regression." In Lecture Notes in Computer Science, 267–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550907_42.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Wilson, Jeffrey R., and Kent A. Lorenz. "Hierarchical Logistic Regression Models." In ICSA Book Series in Statistics, 201–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23805-0_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Bolin, Jocelyn H. "Model Comparisons and Hierarchical Regression." In Regression Analysis in R, 79–92. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9780429295843-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Galárraga, Luis, Olivier Pelgrin, and Alexandre Termier. "HiPaR: Hierarchical Pattern-Aided Regression." In Advances in Knowledge Discovery and Data Mining, 320–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75762-5_26.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Huang, Xiao, Juyang Weng, and Roger Calantone. "Locally Balanced Incremental Hierarchical Discriminant Regression." In Intelligent Data Engineering and Automated Learning, 185–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45080-1_26.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Johnson, Alicia A., Miles Q. Ott, and Mine Dogucu. "Non-Normal Hierarchical Regression & Classification." In Bayes Rules!, 463–84. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9780429288340-18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Mileski, Vanja, Sašo Džeroski, and Dragi Kocev. "Predictive Clustering Trees for Hierarchical Multi-Target Regression." In Advances in Intelligent Data Analysis XVI, 223–34. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68765-0_19.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Guo, Ruocheng, Hamidreza Alvari, and Paulo Shakaria. "Strongly Hierarchical Factorization Machines and ANOVA Kernel Regression." In Proceedings of the 2018 SIAM International Conference on Data Mining, 729–37. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2018. http://dx.doi.org/10.1137/1.9781611975321.82.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Hierarchical regression"

1

Yamani, Asma Z., and Rabah A. Al-Zaidy. "Recursive Hierarchical Regression Clustering." In 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE). IEEE, 2021. http://dx.doi.org/10.1109/csde53843.2021.9718490.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zhang, Yanxin, David J. Miller, and George Kesidis. "Hierarchical maximum entropy modeling for regression." In 2009 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2009. http://dx.doi.org/10.1109/mlsp.2009.5306225.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Inghelbrecht, Gilles, and Kurt Barbe. "Large Measurement Regression: Hierarchical Least Squares Multisplitting." In 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2019. http://dx.doi.org/10.1109/i2mtc.2019.8826840.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ke, Shih-Wen, and Chi-Wei Yeh. "Hierarchical Classification and Regression with Feature Selection." In 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2019. http://dx.doi.org/10.1109/ieem44572.2019.8978843.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Chung-Han Lee, Chi-Chun Hsia, Chung-Hsien Wu, and Mai-Chun Lin. "Regression-based clustering for hierarchical pitch conversion." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960403.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Zheng, Hao, Fugui Wang, Jiangfeng Xu, Nengfeng Zhou, Minping Qian, Ji Zhu, and Minghua Deng. "Pathway Detection Based on Hierarchical LASSO Regression Model." In 2009 2nd International Conference on Biomedical Engineering and Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bmei.2009.5305086.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ghosh, Sayan, Ryan Jacobs, and Dimitri N. Mavris. "Multi-Source Surrogate Modeling with Bayesian Hierarchical Regression." In 17th AIAA Non-Deterministic Approaches Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-1817.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ben Taieb, Souhaib, and Bonsoo Koo. "Regularized Regression for Hierarchical Forecasting Without Unbiasedness Conditions." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3330976.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Liu, Xinyu, Zongliang Gan, and Feng Liu. "Hierarchical Subspace Regression for Compressed Face Image Restoration." In 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2018. http://dx.doi.org/10.1109/wcsp.2018.8555682.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Li, Xiaotian, Shuzhe Wang, Yi Zhao, Jakob Verbeek, and Juho Kannala. "Hierarchical Scene Coordinate Classification and Regression for Visual Localization." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.01200.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Hierarchical regression"

1

Wallstrom, Timothy Clarke, and David Mitchell Higdon. Hierarchical Linear Regression. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1489929.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Gaver, Donald P., Patricia A. Jacobs, and I. G. O'Muircheartaigh. Regression Analysis of Hierarchical Poisson-Like Event Rate Data: Super- Population Model Effect on Predictions. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada230297.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

McPhedran, R., K. Patel, B. Toombs, P. Menon, M. Patel, J. Disson, K. Porter, A. John, and A. Rayner. Food allergen communication in businesses feasibility trial. Food Standards Agency, March 2021. http://dx.doi.org/10.46756/sci.fsa.tpf160.

Full text
Abstract:
Background: Clear allergen communication in food business operators (FBOs) has been shown to have a positive impact on customers’ perceptions of businesses (Barnett et al., 2013). However, the precise size and nature of this effect is not known: there is a paucity of quantitative evidence in this area, particularly in the form of randomised controlled trials (RCTs). The Food Standards Agency (FSA), in collaboration with Kantar’s Behavioural Practice, conducted a feasibility trial to investigate whether a randomised cluster trial – involving the proactive communication of allergen information at the point of sale in FBOs – is feasible in the United Kingdom (UK). Objectives: The trial sought to establish: ease of recruitments of businesses into trials; customer response rates for in-store outcome surveys; fidelity of intervention delivery by FBO staff; sensitivity of outcome survey measures to change; and appropriateness of the chosen analytical approach. Method: Following a recruitment phase – in which one of fourteen multinational FBOs was successfully recruited – the execution of the feasibility trial involved a quasi-randomised matched-pairs clustered experiment. Each of the FBO’s ten participating branches underwent pair-wise matching, with similarity of branches judged according to four criteria: Food Hygiene Rating Scheme (FHRS) score, average weekly footfall, number of staff and customer satisfaction rating. The allocation ratio for this trial was 1:1: one branch in each pair was assigned to the treatment group by a representative from the FBO, while the other continued to operate in accordance with their standard operating procedure. As a business-based feasibility trial, customers at participating branches throughout the fieldwork period were automatically enrolled in the trial. The trial was single-blind: customers at treatment branches were not aware that they were receiving an intervention. All customers who visited participating branches throughout the fieldwork period were asked to complete a short in-store survey on a tablet affixed in branches. This survey contained four outcome measures which operationalised customers’: perceptions of food safety in the FBO; trust in the FBO; self-reported confidence to ask for allergen information in future visits; and overall satisfaction with their visit. Results: Fieldwork was conducted from the 3 – 20 March 2020, with cessation occurring prematurely due to the closure of outlets following the proliferation of COVID-19. n=177 participants took part in the trial across the ten branches; however, response rates (which ranged between 0.1 - 0.8%) were likely also adversely affected by COVID-19. Intervention fidelity was an issue in this study: while compliance with delivery of the intervention was relatively high in treatment branches (78.9%), erroneous delivery in control branches was also common (46.2%). Survey data were analysed using random-intercept multilevel linear regression models (due to the nesting of customers within branches). Despite the trial’s modest sample size, there was some evidence to suggest that the intervention had a positive effect for those suffering from allergies/intolerances for the ‘trust’ (β = 1.288, p<0.01) and ‘satisfaction’ (β = 0.945, p<0.01) outcome variables. Due to singularity within the fitted linear models, hierarchical Bayes models were used to corroborate the size of these interactions. Conclusions: The results of this trial suggest that a fully powered clustered RCT would likely be feasible in the UK. In this case, the primary challenge in the execution of the trial was the recruitment of FBOs: despite high levels of initial interest from four chains, only one took part. However, it is likely that the proliferation of COVID-19 adversely impacted chain participation – two other FBOs withdrew during branch eligibility assessment and selection, citing COVID-19 as a barrier. COVID-19 also likely lowered the on-site survey response rate: a significant negative Pearson correlation was observed between daily survey completions and COVID-19 cases in the UK, highlighting a likely relationship between the two. Limitations: The trial was quasi-random: selection of branches, pair matching and allocation to treatment/control groups were not systematically conducted. These processes were undertaken by a representative from the FBO’s Safety and Quality Assurance team (with oversight from Kantar representatives on pair matching), as a result of the chain’s internal operational restrictions.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Hierarchical regression' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography