# Analysing Near-Miss Incidents in Construction: A Systematic Literature Review
## 4 Results
### Methods Used to Analyse the Information and Figures That Have Been Obtained
#### 4.3.1 Quantitative and qualitative statistical methods  
The analysis of accumulated near-miss data typically entails a combination of quantitative and qualitative statistical techniques. The primary objective of quantitative analysis is to utilise statistical inference techniques in order to obtain results that can be generalised to the entire study population. A qualitative analysis [18] and structural analysis [68] are often conducted in parallel. An example of the use of qualitative and quantitative analysis is the study conducted by [PERSON] et al. [69]. The study employed incident reporting systems (IRSs) based on semi-structured interviews, participant observation, direct observation at construction sites, and document analysis. Additionally, a quantitative analysis of 946 reported near misses and occupational accidents at 16 construction sites was conducted, classifying them by type, severity, duration, and number of reports. The results of the study have the potential to yield benefits in three key areas regarding the enhancement of construction safety: (1) The analysis of incident reporting systems (IRSs) revealed insights that can inform further investigation of existing data; (2) A set of IRS design guidelines was developed based on these insights; and (3) The quantitative analysis of incident reports constituted a significant contribution in its own right, given the dearth of empirical research on near misses. It is anticipated that these findings will prove beneficial to those engaged in the design and management of incident reporting systems within the construction industry. Additionally, [PERSON] et al. [70] conducted a qualitative analysis of a substantial data set (3519 reports). The objective of the study was to enhance the near-miss reporting system and identify deficiencies within the system. Temporal and spatial data are also analysed [34], making it possible to identify locations characterized by the risk of a hazardous event [47]. The data are subjected to automated analysis in order to ascertain the degree of danger, which is subsequently presented in the form of a heat map. [PERSON] et al. [14] used accident precursor analysis in their study. They used historical accident records compiled from reported incidents to identify precursors and immediate factors (PaFs) at construction sites and obtain information on mitigation measures. The findings of the research led to the development of a systematic mechanism for the disruption and prevention of precursors and immediate factors at construction sites. Figure 7: Methods used to analyse the information and figures that have been obtained. An analysis of system failures was conducted by [PERSON] et al. [71]. The study employed a data-driven approach to analyse texts describing the failure events. The data were processed using natural language processing (NLP) and machine learning. Based on historical data and expert knowledge, [PERSON] et al. [72] conducted an event probability analysis, including an evaluation of potential consequences. The study employed stochastic methodologies to evaluate the probability of accidents and their potential consequences. This enabled the modelling and prediction of hazardous events in construction projects. [PERSON] et al. [60] employed a causal analysis to examine the underlying causes of hazardous behaviours that contribute to fall hazards on construction sites. In order to achieve this, video footage was employed to record and subsequently analyse the unsafe behaviours exhibited by workers. To verify the identified causal patterns, the association rules method and a risk matrix were employed. The findings revealed that the primary factors influencing unsafe worker behaviours are habits, motivation, perceived behavioural control, and subjective norms. [PERSON] et al. [73] developed an eight-stage process for the management of near-miss events, comprising the following stages: discovery, reporting, identification, prioritisation, causal analysis, resolution, dissemination, and evaluation.