Mining for 

Information in 

Accident Data

Nelda J. Milburn

Lena Dobbins

Julia Pounds

Scott Goldman

Federal Aviation Administration

Civil Aerospace Medical Institute

Oklahoma City, OK 73125

November 2006

Final Report

DOT/FAA/AM-06/26

Office of Aerospace Medicine

Washington, DC 20591
NOTICE

This document is disseminated under the sponsorship 

of the U.S. Department of Transportation in the interest 

of information exchange. The United States Government 

assumes no liability for the contents thereof.

___________

This publication and all Office of Aerospace Medicine technical reports are 

available in full-text from the Civil Aerospace Medical Institute’s publications 

Web site: 

www.faa.gov/library/reports/medical/oamtechreports/index.cfm
i

Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-06/26 

4. Title and Subtitle 5. Report Date 

Mining for Information in Accident Data November 2006 

6. Performing Organization Code 

 

7. Author(s) 8. Performing Organization Report No. 

Milburn N, Dobbins L, Pounds J, Goldman S 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

FAA Civil Aerospace Medical Institute 

P.O. Box 25082 11. Contract or Grant No. 

Oklahoma City, OK 73125 

12. Sponsoring Agency name and Address 13. Type of Report and Period Covered 

Office of Aerospace Medicine 

Federal Aviation Administration 

800 Independence Ave., S.W. 

Washington, DC 20591 14. Sponsoring Agency Code 

15. Supplemental Notes 

Work was accomplished under approved task AHRR522. 

16. Abstract 

Purpose. This project evaluated WinMine, an analytic tool developed by Chickering, Heckerman, 

Meek, Platt, and Thiesson (2000) to determine its usefulness for identifying higher-order relationships in 

research data from dynamic and high-consequence aviation events. Traditionally, researchers have relied on 

several types of analyses to better understand the relationships between factors related to an outcome. 

However, researchers need an analytic approach that can clearly illustrate the interactions among causal 

factors as probabilities associated with the chain of events. Method. A convenience sample of aviation 

accident data previously classified using the Human Factors Analysis and Classification System (HFACS; 

Shappell & Wiegmann, 2000; 2001) was used to evaluate WinMine in contrast to traditional methods, such 

as bar graphs, contingency tables, and odds ratios. Results. WinMine showed an advantage when compared 

with other methods because it graphs quantifiable interrelationships between factors and illuminates the 

underlying hierarchical structure of variables. Discussion. Each technique examined contributed toward 

understanding the causal factors; however, WinMine provided a better picture of the factor interrelationships 

than the other methods.

 

17. Key Words 18. Distribution Statement 

 

 

WinMine, HFACS, Accident Data, Decision Tree, 

Analytic Tool, Conditional Probability 

Document is available to the public through the 

Defense Technical Information Center, Ft. Belvior, VA 

22060; and the National Technical Information 

Service, Springfield, VA 22161 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 11 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 

1

Mining for Information in Accident Data

INTRODUCTION

Researchers have used a variety of analytic techniques 

to better understand factors related to aviation incidents. 

The format of the data, the type of analysis, and the method 

of presenting the results were all of interest for this project. 

For comparison, 3 reports involving aviation accident 

data were reviewed. 

1) Review of Aviation Accidents Involving Weather Turbulence in the United States: 1992-2001 NASDAC (2004) 

analyzed aviation accident/incident data extracted from 

the National Transportation Safety Board (NTSB) database. The report was a 10-year review in which there 

were “a total of 20,332 accidents that occurred in the 

United States” (p. 1). The NTSB “cited weather as a 

cause or factor in 4,326 accidents. Of these weather 

events, the NTSB cited weather turbulence as a cause 

or factor in 509 accidents, or eleven percent of the total 

weather accidents” (p.1). The report summarized the total 

number of accidents per year that were weather-related 

compared to weather turbulence accidents (in a bar 

chart), the percentage of weather turbulence accidents 

from all weather-related accidents per year (in a tabular 

format), the total weather accidents by phenomenon 

(in a pie chart), the number of general aviation weather 

turbulence accidents by month (in a bar chart), and many 

other graphs and charts that are limited by the number 

of variables that can be presented at once. Each of these 

methods permits an understanding of the frequencies 

of each factor, a comparison of frequencies of one factor 

(on the x-axis) with another factor (on the y-axis), or a 

particular factor (such as the number of injuries) as a 

function of another variable (such as type of turbulence). 

However, these graphs do not allow the researcher to 

understand complex relationships between variables.

2) Review of Aviation Accidents Occurring in the State of Alaska

Office of System Safety (ASY, 2003) analyzed 20,325 

accidents that occurred in the United States between 

1992 and 2001 extracted from the NTSB Aviation Accident and Incident Database. The database was filtered 

to include only accidents that occurred in the state of 

Alaska—a total of 1,569 accidents. The frequency data 

were summarized in tabular form as counts and percentages, bar charts, pie charts, and as a line graph depicting the number of accidents per year that was overlaid 

with a trendline. The frequency data were also used to 

compute monthly averages. The pie charts were easy to 

interpret—for example: as the percentage of accidents 

that occurred as a function of light condition. However, 

this type of analysis makes it difficult for the researcher 

to discern complex relationships between variables.

3) Work by Shappell and Wiegmann (2003) examined 

general aviation (GA) accidents classified as controlled 

flight into terrain (CFIT) –an “in-flight collision with 

terrain, water, or obstacle without indication of a loss 

of control” using the definition provided by the ICAO 

(International Civil Aviation Organization) Common 

Taxonomy Team (cited in Shappell & Wiegmann, 

2003).The data were dichotomous, that is, coded as 

either 1 or 0, depending upon whether the factor was 

judged by analysts to be present or absent in the accident. If present, the factor was classified into 1 of the 

17 Human Factors Analysis and Classification System 

(HFACS) categories (Table 1). Odds ratios were used to 

Table 1. List of the 17 Human Factors Analysis and 

Classification System (HFACS). 

Organizational Influences 

1. Resource Management 

2. Organizational Climate 

3. Organizational Process 

Unsafe Supervision 

1. Inadequate Supervision 

2. Planned Inappropriate Operations 

3. Failed to Correct Problem 

4. Supervisory Violations 

Preconditions for Unsafe Acts 

1. Substandard Conditions of Operators 

a) Adverse Mental States (AMS) 

b) Adverse Physiological States (APS) 

c) Physical/Mental Limitations (PML) 

2. Substandard Practices of Operators 

a) Crew Resource Mismanagement (CRM) 

b) Personal Readiness (PR) 

Unsafe Acts of Operators 

1. Errors

a. Decision Errors (DE) 

b. Skill-Based Errors (SBE) 

c. Perceptual Errors (PE) 

2. Violations (V) 

a. Routine

b. Exceptional

(Source: Shappell & Wiegmann, 2000, 2001) 
2

examine the likelihood of a causal factor resulting in a 

CFIT, and the results were presented in a tabular format 

(Table 2). The analysis showed the relationship of each 

factor independently with CFIT, but it could not show 

the interaction between the factors, nor the probability 

associated with each factor along a chain of events. 

Clearly, a better way of examining and reporting the 

affect of each factor is needed. Potentially, a probabilistic 

relational analysis could improve our understanding of 

the interaction between causal factors in dynamic aviation events. Therefore, the purpose of this project was to 

evaluate WinMine (Chickering, Heckerman, Meek, Platt, 

& Thiesson, 2000) as an analysis method to determine 

its usefulness for identifying higher-order relationships in 

an archival aviation database. To test this, a convenience 

sample of data was borrowed from an analysis of dynamic 

and high-consequence aviation events (the third study 

reported above). Because the main focus of this paper 

is an examination of the WinMine tool rather than a 

traditional hypothesis test, the paper will not interpret 

the results of the data analysis or the specific probabilities associated with the factors in GA accidents but will 

instead limit the scope of the research to an evaluation 

of the WinMine software. 

Method

Database

Accident data previously classified using HFACS 

(Shappell & Wiegmann, 2000; 2001; 2003) were 

used to demonstrate the functionality of the WinMine 

software. Five certified flight instructors served as subject 

matter experts and analyzed 16,500 accidents to create 

the database of which 16,278 were used in this analysis. 

Two pilot-raters independently coded each accident as 

CFIT or not (coded 1 or 0, respectively) according to the 

definition provided by the ICAO Common Taxonomy 

Team. The pilot-raters classified the GA accidents into 

17 causal categories defined by the NTSB, 9 of which 

were used in this study: Adverse Mental States (AMS), 

Adverse Physiological States (APS), Physical/Mental 

Limitations (PML), Crew Resource Mismanagement 

(CRM), Personal Readiness (PR), Decision Errors (DE), 

Skill-Based Errors (SBE), Perceptual Errors (PE), and 

Violations (V). Notice that the category “Violations” is 

divided into 2 sub-categories (routine and exceptional) 

on the complete list (Table 1), but the higher-level classification was used for this analysis. Of the 4 levels of 

failure, only the factors described in the sub-categories 

under “unsafe acts of operators” and “preconditions for 

unsafe acts” were used for this analysis.

Apparatus

The WinMine Toolkit (Chickering, Heckerman, 

Meek, Platt, & Thiesson, 2000) is a “set of tools for 

Windows 2000/NT/XP that allow you to build statistical 

models from data and graphically represents the results. 

Development of the toolkit was performed by the same 

team that has contributed to the data-mining technologies 

in Microsoft’s SQL Server database product” (Chickering, 2002). Some of the tools are DOS command-line 

executables that can be run in scripts. For the most 

basic analyses, WinMine accepts dichotomously coded, 

Table 2. Chi-Square and odds ratio for CFIT for each HFACS causal category. 

HFACS Causal Category 

Chi-square Odds

Ratio

95% Confidence 

Interval

Lower Upper 

Unsafe Acts of Operators 

 

 Decision Errors 1.792 ns 0.923 0.822 1.038 ns 

 Skilled-Based Errors 6.229 ns 1.178 1.036 1.341 ns 

 Perceptual Errors 50.404 p<.001 1.847 1.555 2.193 p<.001 

 Violations 380.748 p<.001 3.264 2.883 3.695 p<.001 

Substandard Conditions of 

 

Operators 

 Adverse Mental States 146.069 p<.001 2.907 2.427 3.482 p<.001 

 Adverse Physiological States 7.097 ns 1.497 1.110 2.017 ns 

 Physical/Mental Limitations 29.826 p<.001 0.639 0.543 0.751 p<.001 

 Crew Resource Management 18.916 p<.001 0.631 0.512 0.778 p<.001 

 Personal Readiness 136.486 p<.001 4.089 3.168 5.276 p<.001 

(Source: Shappell and Wiegmann, 2003) 
3

categorical data. More advanced techniques allow both 

discrete and continuous variables to be used. 

WinMine graphically represents probabilistic dependencies found within a dataset in a network that is very 

similar to a Bayesian network and is referred to as a dependency network. The networks are similar because both 

methods use a graph and have a probability component. 

It is important to keep in mind that in its consistent form 

“the graph component is a cyclic directed graph such that 

a node’s parents render that node independent of all other 

nodes in the network. As in a Bayesian network, the probability component consists of the probability of a node 

given its parents for each node—the local distributions” 

(Heckerman, Chickering, Meek, Rounthwaite & Kadie, 

2000, p. 70). Refer to that original document for a full, 

in-depth description of the algorithms, assumptions, and 

intricacies of the WinMine product that are beyond the 

scope of this paper. 

RESULTS

Researchers have counted, summarized, categorized, 

graphed, plotted, and tabulated accidents in innumerable 

ways—often with the goal of trying to make sense of 

accidents—such as what were the causes, how could the 

accident have been prevented, what can be learned from 

the accident to prevent others from happening, and countless other questions. Although researchers have employed 

a variety of data analysis techniques, the methodologies 

have failed to clearly illustrate the interactions among 

the causal factors and to provide probabilities associated 

with the factors. Therefore, in quest of a better tool to 

highlight or expose any relationships between possible 

causal factors, the WinMine product was evaluated. 

WinMine has several component parts (Toolkit), 

with one or more modules designed to 1) read the data 

files, 2) merge two files together (if necessary), 3) select 

variables for use (or to be ignored), 4) code missing data, 

5) identify the roles of variables (in the Plan Phase), 6) 

view the model as a function of the strength of relationships between variables, and finally 7) view a tree diagram with the detailed probabilities associated with each 

variable. Each of those component parts is valuable, and 

most are similar to functions available in other analytic 

software; however, items 5 through 7 are fairly unique 

to this software, therefore their usefulness was the focus 

of this evaluation. 

The Plan Phase

Prior to performing an analysis, the user should specify 

whether a variable is to be modeled as an input or output 

variable, or both. That distinction essentially establishes 

how variables will be modeled, the role they will play in 

the analyses, and how they will be displayed in WinMine’s 

graphic output called a decision tree. An “input” variable 

can only predict other variables. An “output only” variable 

is one that can only be predicted by other variables. For 

example, CFIT is actually an outcome or presumably 

the last event in a chain and could not precede a V, or 

a DE, or an AMS. (Granted, if the pilot walked away 

from the CFIT, it is likely that the pilot will experience 

an AMS and may contemplate possible DE or a V.) But 

the categories we are considering are precursors to an accident, not consequences. Logically then, CFIT should 

be established within the Plan Phase of WinMine as 

an “output only” variable with the goal of determining 

whether WinMine could show how the other factors 

might be related to this output variable.

In contrast, some variables may be an internal link in 

a chain of events that are therefore best defined as both

input and output variables. Thus, an input-output variable 

is one that is both predicted by other variables and can 

also serve as a predictor. Consequently, some knowledge 

of the role of the variables contained within a specific 

data set may aid in defining their expected roles. However, 

imposing a specific structure on the variables restricts the 

placement of the nodes within the graphic representation 

of the data but will not inhibit the calculated probabilities 

associated with each node. Rather, defining the role of 

a variable (in the Plan Phase) is a way of specifying the 

model to test. In some cases, it makes sense to predefine 

the placement or ordering of variables within the model. 

For example, a store owner wants to determine the likelihood that someone will purchase a new computer, given 

that potential buyers meet the following defined set of 

conditions: The purchaser is currently employed, lives 

within a 25-mile radius of the store, has an annual income 

greater than $40 thousand per year, has a high-speed 

Internet connection such as DSL (digital signal link), 

and had purchased a computer more than 2 years ago. 

In this example, the decision to purchase variable should 

be assigned as an output variable. However, because the 

business owner is unsure about the role that each of the 

other variables may play or the order of sequencing of 

those variables with regard to the output variable, each 

should be coded as input-output variables. 

Once the researcher specifies the structure, a stochastic 

analysis can be executed to reveal the relationships between variables and identify probabilistic dependencies. A 

further use of the Plan Phase will be reserved until other 

component parts, vital to understanding that aspect, have 

been described and evaluated. Items 6 and 7 are WinMine 

output screens, the DNet (dependency network) Viewer 

and the Decision Tree.
4

The DNet Viewer

WinMine’s output is displayed in DNet Viewer. The 

initial output screen contains an interactive graph composed of nodes connected by arrows that indicate relational 

links between the variables. An impressive aspect of the 

graphical output is that it is dynamic with respect to 

presentation of the strength of relationships between the 

variables. The user can evaluate the model as a function 

of the strength of the dependencies by using the mouse to 

move a slider bar along its vertical length. In DNet Viewer, 

a slider bar is available on the left side of the screen. The 

top endpoint of the bar is labeled “All Links.” When the 

bar is moved to this position, all relationships between 

the nodes are shown. The bottom endpoint of the bar is 

labeled “Strongest Links.” When the bar is moved to this 

position, only the strongest links (dependencies) between 

the nodes are shown. (See Heckerman et al. [2000] for 

a complete description of the method used for ranking 

the strength of links.)

WinMine’s output in DNet Viewer is a graph containing nodes and arrows indicating relational links between 

the variables. WinMine graphically displays the structure 

of the data and also redundantly color-codes the variables 

(as structural nodes) for easy interpretation of their role 

in the structure. Figure 1 shows the result when the slider 

bar is set to show all relationships between all variables. 

An analyst interested in this type of data might interpret 

this pattern of links to mean that there is at least a weak 

relationship between most variables. When the slider bar 

is set at approximately 30% from the top position, the 

variables V, PE, and SBE show a relationship to CFIT 

(Figure 2). When the slider bar is moved to approximately 

75% from the top position (Figure 3), only V shows a 

link to CFIT. 

The Decision Tree

More specific details can be obtained from the tool by 

examining the decision tree. The decision tree shows the 

variable nodes including an itemized list of preliminary 

conditions that led to each specific point within the 

chain and can be examined by double clicking on each 

leaf node. A branch on the decision tree is formed from 

conditional probabilities and is a graphical way of displaying dependencies found within the data. The number of 

observations (accidents) is shown on each branch of the 

decision tree. Specific interpretation of the decision path 

Figure 1. Sample output when slider bar is set to show all relationships between all variables.
5

Figure 2. Example of output when slider bar is set at approximately 30% from the top 

position.

Figure 3. Example of output when the slider bar is approximately 75% from the top position.
6

(formed from the Bayesian conditional probabilities) and 

based on the specific conditions that led to a particular 

point) is also available by double clicking on the variable name within the decision tree. Double clicking on 

the variable name will display a dialog box that lists the 

pre-conditions—the given(s) taken into consideration 

in the probability. The dialog box may indicate that 

the program is providing probabilities (likelihoods) for 

purchasing a computer (the selected output variable) or 

in our dataset, the likelihood of a CFIT if certain other 

variables were coded in a specific way—meaning that 

certain other conditions co-exist. This may be more 

clearly understood within the scenario of the likelihood 

of rolling a 4 on a die after having rolled a 1, a 2, and a 

3 (in that order). 

For example, the dialog box in Figure 4 shows the 

specifics for a CFIT in the sample of accident cases used 

for this test. The number of cases in the data set analyzed 

was 16, 278; the probability that an accident case was 

missing data for the CFIT variable was 0.000123. The 

probability that the coders did not identify a CFIT in 

an accident case was 0.909. The probability that the 

coders identified a CFIT in an accident was 0.0905. 

This type of information can be used to make inferences about the data sample. For example, most accident cases apparently did not include a CFIT. That fits 

the percentages reported by Shappell and Wiegmann 

(2003); their report also compares the “percentage of 

CFIT and non-CFIT accidents associated with at least 

one instance of each particular causal category” (p.12). 

All of that is good information. But, one of the benefits 

of WinMine is that it allows the researcher to see other 

associated probabilities. For example, Figure 4 indicates 

that, of the accidents that did not involve a violation 

(n=14,431), 9,728 did involve a skill-based error and 

154 did involve both personal readiness and skill-based 

errors. Although this same information is obtainable using 

a series of “select if” statements in SPSS, this is a level 

of analysis that the other techniques rarely explore. The 

point being that the researcher can delve into the data 

without spending hours conjuring up many “what if” 

scenarios because these are readily available in the many 

other output screens within WinMine by double-clicking 

on a selected variable node. 

Figure 4. Example of a decision tree and the dialog box with specifics for a CFIT.
7

Discussion

Each method of data analysis has strengths and 

weaknesses. The challenge for the researcher is to use 

an appropriate analysis to get the most meaningful information from the data. For example, if the researcher 

wanted to get an initial look at the frequencies within 

each category, the bar chart is a perfect tool, but if a more 

in-depth examination of the data is desired, similar to the 

one previously described, then conventional bar charts, 

correlations, pie charts, and line graphs cannot convey 

that level of information. 

Analysis using WinMine reveals interrelationships 

between variables in ways that other methods cannot 

do in a direct manner—to accomplish the same task using other software, the researcher must select cases that 

fit specifically, pre-defined criteria, analyze that sub-set 

of data, and continue to select sub-sets of cases until a 

specific chain of “select if” statements leads to a desired 

path. However, that type of analysis assumes that the 

direct path is known in advance; otherwise, given 9 variables with 2 possible states for each (1=yes, and 0=no), 

the exercise would be especially time consuming if all 

possible permutations were tested. 

Caution

Interpreting relational data should be done with care. 

When interpreting the graphical output, we can assume 

that, if there is an arrow from one variable to another, 

the previous variable is a predictor of the latter. Figure 1 

suggests that PR,V, PE, SBE, and DE are predictors of 

a CFIT, that is consistent with Shappell and Wiegmann 

(2003, p. 12). On Figure 3, the arrow from V (Violation) 

to CFIT indicates that a violation predicts a CFIT. When 

interpreting uni-directional links between variables, one 

might conclude that each variable helps to predict the 

other. However, Heckerman et al. (2000) state that the 

relationships are “significant only in whatever sense was 

used to learn the network with finite data” (p. 67). One 

must cautiously interpret the results because they are 

only appropriate in the context of the other variables 

also present in the network and, thus, only within the 

context of those included in the analysis. 

Challenges

Two things make the WinMine program difficult to use. 

One is related to the user’s level of expertise using DOS 

to do data manipulations. It is clumsy to switch back and 

forth between the user-friendly dialog boxes of a Windows 

environment to DOS-level interactions. Also, the software has a somewhat limited amount of documentation 

available for users. A search of the research literature did 

not find any documented use of the software that might 

have facilitated the interpretation of some of the features, 

options, output, and possible applications. 

WinMine requires some investment of time to learn; 

however, there is a WinMine users group available via 

E-mail that is very helpful if problems are encountered 

or questions arise. Frequently, the lead programmer 

personally answers those inquiries. 

Strengths

The decision tree is the greatest advantage of the WinMine program, because it can be used to quantify the 

likelihood of an event (for a specific dataset), given that 

other circumstances exist, such as quantifying the HFACS 

hierarchical structure using coded accident cases. The same 

analytical method used by WinMine can also be obtained 

using traditional analytic software but would require 

many more steps. For example, to use SPSS to quantify 

the likelihood of an event, you must first determine the 

path or chain of conditions that you want to test. Suppose 

that a computer storeowner, needing some information 

to plan a marketing campaign, wanted to determine the 

likelihood that someone who purchased a computer from 

the store more than 2 years ago still lives within a 25-mile 

radius from the store and has DSL service. The owner 

must write a series of “select if” statements (e.g., select if 

DSL = yes and 2+years = yes and miles_25 = yes) to match 

the predetermined filtering restrictions. Then, from that 

reduced data set, the owner can determine the percentage 

of purchasers that fit that specific description. For every 

set of conditions to explore, a separate analysis must be 

run. That is, you must start over to define the analysis 

to know the proportion of purchasers who do not have 

DSL but satisfy the other set of conditions. In contrast, 

WinMine would calculate the probabilities given that the 

variable DSL was coded yes; and, on a separate branch of 

the tree, given that the variable DSL was coded no. After 

searching for dependencies between the variables, each 

branch (e.g., each HFACS causal factor in our dataset) 

is placed within the hierarchy; and its structure is based 

on calculable, verifiable conditional probabilities found 

within the data rather than on a subjective (however wellinformed) structure. Therefore, rather than performing 

analyses that simply answer questions such as how often 

each type of error occurs, the data can be exhibited as 

probabilities of an event, given a certain set of events.

WinMine allows the user to construct a graphical model 

either as a dependency network or a Bayesian network. 

When comparing the two approaches, Heckerman et al. 

(2000) admitted that “a dependency network is not useful 

for encoding causal relationships.... Nonetheless, there are 

straightforward and computationally efficient algorithms 
8

for learning both the structure and probabilities of a dependency network from data; and the learned model is 

quite useful for encoding and displaying predictive (i.e., 

dependence and independence) relationships. In addition, 

dependency networks are well suited to the task of predicting preferences—a task often referred to as collaborative 

filtering—and are generally useful for probabilistic inference, the task of answering probabilistic queries” (p. 49). 

Furthermore Heckerman et al. discussed the possibility 

that a causal interpretation of the graph may be suspect if 

“one uses a computationally efficient learning procedure 

that excludes the possibility of hidden variables” (p. 50). 

In these situations, the graphed relationships should be 

considered predictive or correlational and cannot be 

interpreted as causal. 

The WinMine Toolkit is made up of several distinct 

units (tools), each with a specific function, much like 

tools within a carpenter’s toolkit. Not all of the tools 

were discussed in this paper because they were not used 

in the present analysis; however, the toolkit includes a 

feature that allows the user to link (match) and then 

merge two separate data files that are keyed on a common variable, such as an identification number. Another 

feature allows the user to predefine an imposing structure 

on the data (using the Plan Phase) to determine whether 

the variables within the dataset adhere to a specifically 

defined structure. For example, one use of this feature 

would be to examine the HFACS hierarchical structure 

proposed by Shappell and Wiegmann (2003) to determine the fit of that structure to the data. The next step 

would be to contrast the hierarchical-structured model 

with a model in which all variables (except CFIT) were 

defined as “input-output variables” (as was the case in this 

evaluation, thus allowing the data to define the model) 

and subsequently compare the fit of the models using a 

metric similar to those used in advanced statistics such 

as structural equation modeling products like LISREL, 

AMOS, and CALIS. Furthermore, a follow-on study 

should evaluate whether the HFACS hierarchical structure 

also appears in other datasets, thereby providing evidence 

of the robustness of the HFACS-proposed taxonomic 

structure. Consequently, such evidence could be used 

to further develop future taxonomies. 

The WinMine Toolkit is useful for visualizing relationships between variables, such as the 9 HFACS categories 

examined in this study. In summary, this evaluation found 

that the WinMine tool is a good visual aid to exploring, interpreting, and perhaps even making predictions 

from or finding structure within dichotomously-coded 

categorical data.

REFERENCES

Chickering, D. (2002). The WinMine Toolkit. Redmond, 

WA. Microsoft technical report no. MSR-TR2002-103.

Chickering, D.M., Heckerman, D., Meek, C., Platt, J.C., 

& Thiesson, B. (2000). Goal-oriented clustering. 

Technical report, MSR-TR-2000-82, Microsoft, 

Redmond, WA.

Heckerman, D., Chickering, D., Meek, D. Rounthwaite, 

R. & Kadie, C. (2000). Dependency networks for 

inference, collaborative filtering, and data visualization. Journal of Machine Learning Res 1:49-75.

Review of Aviation Accidents Involving Weather Turbulence 

in the United States: 1992-2001 (2004) NASDAC 

National Aviation Safety Data Analysis Center, FAA 

Office of System Safety, reference no. 04:551. 

Review of Aviation Accidents Occurring in the State of Alaska

Office of System Safety (ASY, 2003). NASDAC 

National Aviation Safety Data Analysis Center, 

Federal Aviation Administration. April 2003.

Shappell, S. & Wiegmann, D. (2000). The human factors analysis and classification system (HFACS). 

Washington, DC: Department of Transportation/

Federal Aviation Administration; FAA report no. 

DOT/FAA/AM-00/7.

Shappell, S. & Wiegmann, D. (2001). Applying reason: 

The human factors analysis and classification system 

(HFACS). Human Factors and Aerospace Safety,

1(1), 59-86.