Multilevel modelling and time series analysis in ... - ERSO - Swov

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Executive SummaryThe SafetyNet project is set up to build a European Road Safety Observatory.The data assembled or gathered for the observatory consist of the Communitydatabase on Accidents on the Roads in Europe (CARE); data on road safetyrisk indicators; data on road safety performance indicators and in-depthaccident data. Potential users will link data from different data-sets, considerdifferent levels of aggregation jointly, and analyse the development over time.Work package 7 (WP7) is set up to deal with statistical and conceptual issuesthat come into play when analysing such complex data structures.One of WP7’s main objectives is to develop a best practice advice for theanalysis of data structures that require more than the standard statistical tools.This best practice consists of D7.4 “Multilevel modelling and time seriesanalysis in traffic research – A methodology” and D7.5 “Multilevel modelling andtime series analysis in traffic research – The manual”.The main goal is to enable the reader to deal with complex data structures thatshow dependencies in space (nested data) or in time (time series data). At firstit is demonstrated how such dependencies can compromise the applicability ofstandard methods of statistical inferences, because they can lead to anunderestimation of the standard error and consequently of the error in statisticaltests.As a solution to this problem, two families of statistical techniques are presentedto deal with these dependencies. Multilevel Modelling is dedicated to theanalysis of data that are structured hierarchically. It offers the possibility toinclude hierarchical structures into the model of analysis. In road safetyresearch, multilevel analyses allow for the introduction of exposure data and ofsafety performance indicators, even if those are not specified at the same levelof disaggregation as the accident data themselves. In this way, multilevelanalyses allow a global and detailed approach simultaneously. Time seriesanalyses are employed to overcome dependency issues in time-related data.They allow describing the development over time, relating the accidentoccurrencesto explanatory factors such as exposure measures or safetyperformanceindicators (e.g., speeding, seatbelt-use, alcohol, etc), andforecasting the development into the near future.Deliverable 7.5 contains the manual to support the methodology D7.4, wherethe theoretical background for these two families of analyses is given. For eachtechnique described in the methodology, this manual presents the instructionsto fit the models on the basis of user friendly software, as well as guidelines forinterpreting the results. The aim of the manual is to enable the reader toconduct all analyses described in the methodology and this way to get hands onexperience in the analysis of road safety data. To enable the reader to trackevery step presented, the data sets discussed in the various sections areavailable.
Chapter 1 - IntroductionHeike Martensen and Emmanuelle Dupont (IBSR)This deliverable has been produced in Workpackage 7 (WP7) of the SafetyNetproject. WP7 is set up to deal with statistical and conceptual issues that comeinto play when analysing complex data structures as they arise in road safetyresearch when combining data from different sources or when considering datathat have been collected over a long timespan. One of its main objectives is thedevelopment of a best practice for the analysis of data structures that requiremore than the standard statistical tools.This best practice consists of D7.4 “Multilevel modelling and time seriesanalysis in traffic research – A methodology” (subsequently, simply “themethodology-report”) and the present deliverable. This document contains thepractical instructions to support the methodology, where it has been describedhow to deal with data that are dependent in space (nested data) or in time (timeseries data). It has been demonstrated how such dependencies cancompromise the applicability of standard methods of statistical inferences,because they lead to an underestimation of the standard error andconsequently of the probability to classify a result as significant that is in factdue to chance.Two families of statistical techniques have been presented to deal with thesedependencies. Multilevel Modelling is dedicated to the analysis of data that arestructured hierarchically and Time Series analyses are employed to overcomedependency issues in time-related data. The methodology is organized in twomain chapters, focussing on multilevel modelling (Chapter 2) and time seriesanalysis (Chapter 3) respectively.For those sections in the methodology where models dedicated to multilevelanalysis or to time series analysis are presented, this manual presents theinstructions to fit each model on the basis of user friendly software, as well asguidelines for interpreting the results. The aim of this document is to enable thereader to conduct all analyses described in the methodology and this way to gethands-on experience in the analysis of road safety data. To enable the readerto track every step presented, the data sets discussed in the various sectionsare available. The data are included as a CD and will be available at theSafetyNet website (www.erso.en/safetynet.htm).This manual is not a stand-alone document. It is intimately related to themethodology and its sections were written under the assumption that therespective part of the methodology report is known. To allow an easy matchingof methodology report and manual sections, the numbering in the manual is thesame as that in the methodology report. Some sections in the methodologyreport, however, do not contain data examples or the models presented employtraditional techniques rather than multilevel or dedicated time series models.For these latter sections there is no counterpart in this manual. As aconsequence, some sections in the manual are rather short, their main purpose
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2.4 Longitudinal dataHeike Martense
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Chapter 2Than open the “Names”
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Chapter 2• Click “Calc” to ca
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Chapter 2Results and Interpretation
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Chapter 2Results and Interpretation
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Chapter 2Now a variable that contai
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Chapter 2 Click “Done”• Estim
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Chapter 2As described in the multiv
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Chapter 2▪ Click on resp1 or resp
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Chapter 2Before we proceed in fitti
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Chapter 2A significant regional var
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Chapter 2The fixed effect of enforc
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Chapter 3 - Time series analysis3.1
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Chapter 3
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Chapter 3The residual analysis in t
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Chapter 3The same result is obtaine
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Chapter 3
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Chapter 3time series on “Squared
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Chapter 33.2.2 Generalized linear m
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Chapter 3Model Description ARIMA(0,
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Chapter 3In addition to the precedi
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Chapter 3Note that it is very diffi
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Chapter 3In the case the Residuals
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Chapter 3The normal Q-Q plot compar
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Chapter 33.4.4 ARIMA models for sea
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Chapter 3Kolmogorov-Smirnov TestIn
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Chapter 3Histogram
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Chapter 3The auxiliary residuals ar
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Chapter 33.6.2.2 Stochastic level m
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Chapter 36.25Log_NO_fatTrend_Log_NO
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Chapter 3Parameter estimation sampl
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Chapter 38.007.757.507.257.000.2Log
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Chapter 3Goodness-of-fit results fo
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Chapter 3interpretation of the test
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Chapter 3The text "Lvl 1983.2" appe
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Chapter 3Normality N 2.40 5.99 +Tab
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Chapter 32Residual Log_UKdriversKSI
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Chapter 32IrrRes Log_UKdriversKSI0.
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Chapter 33.6.6 Explanatory variable
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Chapter 3function has increased fro
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Chapter 3Figure 3.6.18: Observed lo
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Chapter 3 Use the menu or to save
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Chapter 32IrrRes Log_UKdriversKSI0.
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Chapter 3for this period. The actua
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Chapter 3the top figure and the ori
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Chapter 4 - ConclusionThe present d
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Chapter 4researchers conduct valid
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ReferencesLISREL for Windows. Scien
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