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CHAPTER 8:�TRANSFORMING DATA INTO�EVIDENCE (PART 1)

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Learning Objectives

Describe the role of data analysis in a forensic accounting engagement
Identify potential constraints and limitations that frame the data analysis task
Compare and contrast four common data sources used by forensic accountants
Explain the importance of planning for data analysis

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Learning Objectives

Identify ways data can be collected in a forensic accounting engagement
Discuss the process of data preparation
Identify and describe three data analysis tools: relationship charts, link analysis, and timelines
Describe interview transcription as a process of analysis and interpretation

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Describe the Role of Data Analysis in a Forensic Accounting Engagement

Learning Objective 1

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Role of Data Analysis

Data analysis should occur throughout the engagement to facilitates hypothesis development and refinement
This iterative process helps to focus the data collection effort (what to look for)
Drives reasoned and intellectually honest conclusions
Instead, much depends on the specifics of the engagement (such as scope limitations and time constraints)

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Data Analysis

Data are simply Items of information
It can include any information about the subject person or organization

Qualitative data cannot be objectively measured, such as observations (sights, sounds, and smells) and words (documents and interviews)
Quantitative data can be measured and expressed numerically, such as profit, weight, time, and age

in data analysis, we take some Set of information broken down into manageable pieces

Data are simply items of information. It can include any information about the subject person or organization, whether qualitative or quantitative.

Qualitative data cannot be objectively measured, such as observations (sights, sounds, and smells) and words (documents and interviews)
Quantitative data can be measured and expressed numerically, such as profit, weight, time, and age.

Analysis is defined as separation of a whole into its component parts. Thus, in data analysis, we take some set of information and break it down into manageable pieces. The purpose of this action is to drill down to the essence or meaning of the information, which may not be apparent when viewed as a whole.

This interpretation highlights an important aspect of data analysis—its strategic nature. In a forensic accounting engagement, successful data analysis is not conducted indiscriminately. It requires a well-defined purpose, careful planning, and a systematic process, all aimed at refining the working hypothesis.

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Data Analysis

Purpose is to drill down to the essence of the information, which may not be apparent when viewed as a whole
Requires a well-defined purpose, careful planning, and a systematic process for success
Aimed at refining the working hypothesis
In a forensic accounting engagement, successful data analysis is not conducted indiscriminately
It requires a well-defined purpose, careful planning, and a systematic process, all aimed at refining the working hypothesis

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Sufficient Relevant Data

Forensic accounting (expert) opinions must be based on sufficient relevant data
This mandate is found in

Rule 702 of the Federal Rules of Evidence (FRE)

Expert opinion must be based on sufficient facts or data derived from the use of reliable principles and methods

Rule 201 of the AICPA’s Code of Professional Conduct

Practitioners must obtain sufficient evidence to afford a reasonable basis for conclusions

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FRE 702 allows the testimony of an expert witness if it will help the trier of fact (judge or jury) through the maze of “scientific, technical, or other specialized knowledge
Under this rule, expert testimony is admissible only if it meets three specific criteria:
The testimony is based upon sufficient facts or data.
The testimony is the product of reliable principles and methods
The principles and methods have been applied reliably to the facts of the case

FRE Rule 702

FRE 702 allows the testimony of an expert witness if it will help the trier of fact (judge or jury) through the maze of “scientific, technical, or other specialized knowledge. Under this rule, expert testimony is admissible only if it meets three specific criteria:
The testimony is based upon sufficient facts or data.
The testimony is the product of reliable principles and methods.
The principles and methods have been applied reliably to the facts of the case.
Without sufficient facts or data, the application of reliable principles and methods is of little or no consequence and value. In other words, bad data lead to bad results.

AICPA Rule 201

As provided in Rule 201 of the AICPA’s Code of Professional Conduct, practitioners must “obtain sufficient relevant data to afford a reasonable basis for conclusions or recommendations in relation to any professional services performed.”

In the world of auditing (SAS No. 106, AU Sec. 326), sufficient is connected with appropriateness, with sufficient defined as “the measure of the quantity of the audit evidence” and appropriateness defined as “the measure of the quality of the audit evidence.” Extending these definitions, we propose that, in the world of forensic accounting, sufficient data means data of adequate quantity and quality to support the final opinion. Importantly, both quantity and quality are required; a greater quantity of poor data does not compensate for a lack of quality data. In forensic accounting, relevance is determined by the specific objectives of an engagement (that is, what the expert has been engaged to do). Considering any constraints or limitations that might exist, these objectives drive the formation of working hypotheses, which provide direction for the data analysis task.

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Terminology

Tool

Instrument that creates leverage in processing, understanding, and/or illustrating data

Relationship charts, link analysis, and timelines

Method

Determines what data are processed and how they are processed

Technique

Particular approach for applying a tool or method in a specific situation
Based on their individual preferences, experiences, and resources, different individuals may reasonably use different techniques for the same task.

The terms method, tool, and technique are often used interchangeably in discussions of data analysis. However, in the context of a forensic accounting engagement, these terms have distinct meanings based on different functions.
We describe a tool as an instrument that creates leverage in processing, understanding, and/or illustrating data. Several tools are introduced in this chapter, including relationship charts, link analysis, and timelines. Perhaps the most versatile tool of data analysis is the computer, which is used in the application of many other tools.
Throughout this chapter, we characterize data analysis as an ongoing and iterative process. The specific nature of the process is defined by the methods employed. In other words, the method determines what data are processed and how they are processed. In contrast to tools, which can be applied for a variety of purposes, methods are more targeted in their application.
Finally, a technique is a particular approach for applying a tool or method in a specific situation. Unlike the choice of methodology, which is driven primarily by the nature of the problem, the choice of technique is more subjective in nature. Based on their individual preferences, experiences, and resources, different individuals may reasonably use different techniques for the same task.

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Identify Potential Constraints and Limitations that Frame the Data Analysis Task

Learning Objective 2

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Framing the Data Analysis Task

Understanding the purpose of data analysis, we now shift our attention to the process
The process begins before the application of analytic methods, with efforts to properly frame the data analysis task
Includes consideration of various constraints

Introduced by specific circumstances of the engagement as well as limitations of the data

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Preliminary Considerations

Time constraints

Data analysis is always Limited by time constraints, especially when the volume is substantial
Access to data
Data cannot be analyzed unless they are available and can be obtained within the established time frame for the engagement
Forensic accountants need to determine what data are necessary and where they can be obtained

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Preliminary Considerations

Technological resources
Technological resources impact a forensic accountant’s ability to perform data analysis, as well as the efficiency (time) of the process
Include computers, software, cameras, recording devices, database subscriptions, and presentation aids

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Data Limitations

Some can be managed with proper disclosure, but others may render the data unsuitable for the specified objective (that is, unable to meet the threshold of sufficient relevant data)
Common examples of data limitations include:

Missing data:the analyst must consider whether all relevant data have been disclosed (such as bank statements without check copies)
Altered data:. In some cases, it is possible that the data have been altered
Different forms of the same data
Different definitions of the same data
Nonexistent data

Another factor in framing the data analysis task is identifying any limitations inherent in the data. Although the extent of such limitations cannot be fully assessed before the data are collected, potential limitations should be anticipated in advance. Some limitations can be managed (with proper disclosure), but others may render the data unsuitable for the specified objective (that is, unable to meet the threshold of sufficient relevant data).
Common examples of data limitations include the following:

• Missing data. The analyst must consider whether all relevant data have been disclosed. When reviewing source records, forensic accountants often find that certain types of data are missing altogether (such as bank statements without check copies) or that specific items or time periods have been omitted (such as various missing checks or no checks for a particular month).

•Altered data. In some cases, it is possible that the data have been altered. This may occur before the data set leaves the possession of the source, while it is being transported, or while it is in the possession of the analyst. Alterations may be intentional (with the intent to conceal or mislead) or unintentional. In either case, caution must be exercised to avoid not only the actual occurrence, but also the appearance of vulnerability.

•Different forms of the same data. It is possible that the same data are available in more than one form. For example, data may exist in both document and digital form, and certain data may be recorded in several different documents or digital files. To maximize efficiency, the analyst may wish to avoid processing multiple versions of the same data.

•Different definitions of the same data. In response to a data request, you will usually receive only what you specifically ask for—nothing more. In addition to willful concealment, the analyst must also consider the possibility of misunderstandings with regard to data definitions. Thus, careful wording of a data request is essential.

• Nonexistent data. A final potential limitation is that the data of interest simply do not exist, either now or at any time. Some data are only maintained for a certain period of time. This may be years (for paper records) or only days/hours (for digital data). The analyst should consider whether the life of the data may be limited and, if so, take necessary actions to preserve it.

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Compare and Contrast Four Common Data Sources Used by Forensic Accountants

Learning Objective 3

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Data Sources

Four common data sources:

First-Party Data are obtained from the subject individual or entity, Examples include the subject company in a business valuation, the plaintiff in an economic damages claim, and the victim of a suspected fraud (or the alleged fraudster, if engaged by the defense
This source is preferable for two primary reasons: it is direct and accessible. A direct source is often perceived to be the most relevant and comprehensive. With regard to documents and financial data, the subject has the best knowledge of what information is most critical to the activity of interest and the personal incentive to preserve it

Where can forensic accountants obtain sufficient relevant data? Four common data sources, listed in order of preference, are first-party, second-party, third-party, and fourth-party. These sources are labeled according to their proximity to the subject— beginning with subject itself and proceeding outward to related and unrelated parties. Each data source has advantages and disadvantages, as well as implications for reliability.

First-party data are obtained from the subject individual or entity. Examples include the subject company in a business valuation, the plaintiff in an economic damages claim, and the victim of a suspected fraud (or the alleged fraudster, if engaged by the defense).

This source is preferable for two primary reasons: it is direct and accessible. A direct source is often perceived to be the most relevant and comprehensive. With regard to documents and financial data, the subject has the best knowledge of what information is most critical to the activity of interest and the personal incentive to preserve it. For interactive evidence, such as interviews and observations, the subject is usually the most meaningful source and sometimes the only source.
The accessibility of first-party data depends on the forensic accountant’s relationship with the subject. If the subject is the engaging party, then direct access (that is, direct communication with the subject) is often possible. However, when working with the party who does have possession of the data (such as the non-asset spouse in a divorce or the defendant in a damages claim), then access usually requires involvement of the attorneys.

Second-party data are obtained from individuals or entities that are related (connected) to the subject. The relationship may be personal (such as family or friends) or business (such as co-owners, employees, customers, vendors, or external accountants). Due to their interactions with the subject, these sources may have some first-hand knowledge of the subject’s activities. Examples of obtaining data from second-party sources include interviewing a family member or requesting financial data from the subject’s CPA.

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Data Sources

Second-Party Data
Obtained from individuals or entities that are related to the subject
The relationship may be personal (such as family or friends)
(such as co-owners, employees, customers, vendors, or external accountants
Examples of obtaining data from second-party sources include interviewing a family member or requesting financial data from the subject’s CPA.

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Data Sources

Third-Party Data

Obtained from entities that maintain records regarding the subject , such as financial institutions and government agencies
For example, bank statements can be used to confirm components of financial statements and tax returns, such as cash balances, reported expenses, and loan payments. Some third-party data are publicly available, such as organizational data, deeds, liens, and bankruptcy filing

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Data Sources

Fourth-Party Data
Obtained from reference sources, such as news articles, academic journals, trade publications, case law, transaction databases, and government statistics
Falls within the realm of secondary research rather than primary research
The advantage of fourth-party data is that it can be obtained independently by the forensic accountant, without the assistance of the subject or engaging counsel

Fourth-party data are obtained from reference sources, such as news articles, academic journals, trade publications, case law, transaction databases, and government statistics.

Unlike the data sources described previously, this information does not reflect the actual activity of the subject.
Rather, it is used to gain insight on the environment in which the subject operates, including its local geographic market, its industry, and the broader economy.
Thus, it falls within the realm of secondary research rather than primary research. The advantage of fourth-party data is that it can be obtained independently by the forensic accountant, without the assistance of the subject or engaging counsel. Still, secondary research is not without cost. Substantial time and effort can be invested without any guarantee of results, and some reference sources require a fee.

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Explain the Importance of Planning for Data Analysis

Learning Objective 4

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Planning for Data Analysis

It is necessary to begin the data analysis process with proper planning
Involves determining:

What data will be collected, (and from what sources),
How it will be analyzed, and
How results will contribute to refining the working hypothesis

It is necessary to begin the data analysis process with proper planning. This involves determining what data will be collected (and from what sources), how the data will be analyzed, and how the results will contribute to refining the working hypothesis.
Although not all practitioners take such a structured approach, a written data analysis plan (document) is commonly developed that outlines specific tasks and expected time requirements. Recording the plan has several benefits: (1) it forces the analyst to explicitly consider the previously noted constraints; (2) it helps the analyst to remain on task, avoiding any unnecessary efforts that consume valuable time and resources; (3) it provides a standard against which to monitor the progress of the data analysis; and (4) it facilitates a detailed description of the analysis, which is a necessary component for communicating results at the completion of the engagement.

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Planning for Data Analysis

Recording the plan has several benefits:
It forces the analyst to explicitly consider the previously noted constraints;
It helps the analyst to remain on task, avoiding any unnecessary efforts that consume valuable time and resources;
It provides a standard against which to monitor the progress of the data analysis; and
It facilitates a detailed description of the analysis, which is a necessary component for communicating results at the completion of the engagement

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Universe of Data

Must be identified before data analysis can begin
Data universe: also known as the population includes all items of interest, whereas a sample is a subset of these items
Important to be defined

Involves identifying any relevant data that may exist and disregarding any irrelevant data

Forensic accountants may face challenges in

Processing an abundance of data
Absence of existing data

Before data analysis can begin, the proper universe of data must be identified. This is a drilldown process, beginning with a broad assessment of different types of data and data sources and then identifying specific items of interest.
In statistics terminology, the data universe, also known as the population, includes all items of interest, whereas a sample is a subset of these items. In most cases, forensic accountants do not have access to the entire universe of relevant data, nor the time and resources to analyze it all. Thus, we must select a smaller sample to which various analytic methods and tools can be applied. Ideally, the sample should be random, which allows for inferences (or generalizations) from the sample to the larger universe of data, an application of inductive reasoning. However, in some cases, sampling is driven by some specific criteria, such as transactions within a given time period or above a particular dollar value.
Although the entire universe of data usually cannot be analyzed, it is important that it be defined. This involves identifying any relevant data that may exist and disregarding any irrelevant data. Without properly defining the universe, the forensic accountant cannot select a meaningful sample for detailed analysis.
Forensic accountants may face various challenges in identifying relevant data. These challenges depend on specific situations, they may relate to processing an abundance of data or absence of existing data.

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Identify Ways Data Can Be Collected in a Forensic Accounting Engagement

Learning Objective 5

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Means for Data Collection

In corporate investigations

Engaging party may be able to provide any necessary information
provide access to individuals who can assist in obtaining the information
Access to personal data for their employees through prior consent ,which describes situations in which individuals sign a release allowing specific information to be disclosed for a specific purpose

For a corporate investigation, the engaging party may be able to provide any necessary information or provide access to individuals who can assist in obtaining the information.
Companies may also have access to personal data for their employees through prior consent, which describes situations in which individuals sign a release allowing specific information to be disclosed for a specific purpose.
In civil cases, the availability of data depends on the engaging party. If the forensic accountant has been engaged by the party who has possession of the data (such as the plaintiff in a personal injury claim or the asset spouse in a divorce), the data may be obtained directly from the client or through the engaging attorney—an example of first-party data.
In practice, a letter drafted for this purpose is known as a “data request.” If the forensic accountant has been engaged by the party who does not have possession of the data, the engaging attorney will use the data request to draft discovery requests.
If the opposing party fails to comply with discovery requests or does not have access to the data, it may be necessary for the attorney to issue a subpoena. A subpoena is a legal notice directing the attendance of a person at a deposition or trial; similarly, a subpoena duces tecum directs the production of documents or other items.

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Means for Data Collection

In civil cases

Depends on the engaging party
If the forensic accountant has been engaged by the party who has possession of the data (such as the plaintiff in a personal injury claim or the asset spouse in a divorce)
If the opposing party fails to comply with discovery requests or does not have access to the data, it may be necessary for the attorney to issue a subpoena
Subpoena: legal notice directing the attendance of a person at a deposition or trial

In civil cases, the availability of data depends on the engaging party. If the forensic accountant has been engaged by the party who has possession of the data (such as the plaintiff in a personal injury claim or the asset spouse in a divorce), the data may be obtained directly from the client or through the engaging attorney—an example of first-party data.
In practice, a letter drafted for this purpose is known as a “data request.” If the forensic accountant has been engaged by the party who does not have possession of the data, the engaging attorney will use the data request to draft discovery requests.
If the opposing party fails to comply with discovery requests or does not have access to the data, it may be necessary for the attorney to issue a subpoena. A subpoena is a legal notice directing the attendance of a person at a deposition or trial; similarly, a subpoena duces tecum directs the production of documents or other items.

sw

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Means for Data Collection

In criminal cases

Data can be obtained by law enforcement through a search warrant
A search warrant is a court order issued by a judge authorizing a law enforcement agency to perform a search of a person or location and confiscate any evidence that is found

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Drafting a Data Request

The engaging party commonly relies on the forensic accountant to identify any data that are necessary for the analysis
Data requests outline specific items to obtain
Guidelines for constructing data requests follow:

Specificity: To encourage compliance, a data request should be as specific as possible

Data requests should be specific with regard to several elements:
Form of the data—digital or paper
Form of paper data—inspection of originals, if available
Time periods—duration of the entire period and reporting
Parties (individuals and/or entities) to which the request applies

The engaging party commonly relies on the forensic accountant to identify any data that are necessary for the analysis. To this end, an early task in the engagement is to draft a data request outlining specific items to obtain. Several suggestions/guidelines for constructing data requests follow.

Specificity: To encourage compliance, a data request should be as specific as possible. Vagueness is a basis for objection (noncompliance) by the opposing party.

Data requests should be specific with regard to several elements:

Form of the data—digital or paper
Form of paper data—inspection of originals, if available
Time periods—duration of the entire period and any specific reporting intervals
Parties (individuals and/or entities) to which the request applies

Supporting documents: If any schedules, attachments, or other supporting documents are needed, they should be specifically identified.
Interviews: Data requests can include requests for witness interviews or site visits.
Alternatives: Identify any alternative data or forms of data that are acceptable if the requested item does not exist or is unavailable.

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Drafting a Data Request

Data requests should be specific with regard to several Supporting documents: If any schedules, attachments, or other supporting documents are needed, they should be specifically identified
Interviews: Data requests can include requests for witness interviews or site visits.
Alternatives: Identify any alternative data or forms of data that are acceptable if the requested item does not exist or is unavailable

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Discuss the Process of Data Preparation

Learning Objective 6

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Data Preparation

Must be be prepared in a form to which analytics can be applied
Involves processes by which data are gathered, transformed, to specific rules and staged

Both time- and la bor-intensive

Involves maintaining a data inventory

Allows the analyst to note any gaps in the data
Enhances the efficiency of the analysis process
Acts as a progress meter for the data collection

Once the data have been collected, they must be prepared in a form to which analytics can be applied.
Data preparation involves processes by which data are gathered from diverse sources, transformed according to specific rules, and staged for later use.
As a rule, the preparation task is both time- and labor-intensive. Substantial effort is invested because the task is critical: Data cannot be analyzed unless and until they are properly prepared. Any failures in data preparation will certainly be reflected in the analysis, either through inability to conduct a test or inaccurate results of the test.
The first step of data preparation is to create a data inventory of all items that have been received, including a list of specific items, sources, and dates requested/received. This inventory can be compared against the data request to determine which requested items are still outstanding. Moreover, maintaining an inventory allows the analyst to note any gaps in the data, which may indicate missing records.
The data inventory enhances the efficiency of the analysis process by allowing the analyst to determine which tools or methods can be applied now and which must be delayed until additional data are received.
The data inventory is continuously updated as new data are received, acting as a progress meter for the data collection process. Obviously, the larger the volume of data you are working with, the more important the data inventory becomes.

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Working with Databases

Data preparation involves the manipulation of one or more databases

May also require creation of new database

Key considerations

Data fields
Accuracy
Standardization

Preparing data often involves the manipulation of one or more databases. A database is an organized collection of data that has been created for a specific purpose. In a forensic accounting engagement, data compilation efforts are driven by the nature of the data and the anticipated testing procedures. The necessary data may be in one database or may need to be combined from several databases.
In many cases, an entirely new database must be created from source records. Key considerations in creating a database include:

Data fields: The relevant data fields (information to be extracted from the source records) must be identified. All relevant data fields must be identified before the extraction process begins. Failure to do so may require the records to be processed more than once, which is a waste of time and resources that can easily be prevented.
Accuracy: The most important consideration in data entry is accuracy. As the saying goes, “garbage in, garbage out.” To ensure the integrity of the database, any incomplete, erroneous, or irrelevant components must be detected and then corrected. This process is commonly referred to as “data cleansing.”
Standardization: Data recorded in a specific data field must be presented in a consistent format. This is an important aspect of data quality that is often overlooked. Although a data subset may be valid in its original form, it may become inconsistent when merged with another database. Consistency in format is important because it facilitates sorting and other forms of data mining.

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Identify and Describe Three Data Analysis Tools: Relationship Charts, Link Analysis, and Timelines

Learning Objective 7

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Data Analysis Tools

Initially applied early in engagements to create context
With the progress in engagements, continually updated to reflect new information and perspectives
Value stems from key advantages

Widely applicable
Multifunctional
Easy to apply

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Table 8-1—Common Forensic Accounting Tools

Relationship Charts:

A visual chart is a useful tool for mapping relationships. The chart usually contains shapes that represent individual objects and lines that connect them. Specific shapes (such as circles, squares, or rectangles) can be used to identify certain classes of individuals/entities, and different types of relationships can be represented by different lines (such as solid or dashed). Moreover, colors can be used to provide additional clarification or detail. Most diagrams of individual relationships fall into one of two categories: genograms, which map family or personal relationships, and organizational charts, which map relationships (based on responsibilities and/or authority) within an organization. Similarly, an entity chart can be used to illustrate relationships among entities, as well as entity structure and ownership. In addition to illustrating relationships, charts can also be used to illustrate flows or the steps in a process. In this case, the chart is called a flow chart.

Link Analysis:

Forensic accountants often find evidence that, in and of itself, is not directly relevant to the investigation. They must combine such indirect evidence to create a “bigger picture.” Link analysis, a method of identifying relationships among objects, is useful in this endeavor. Link analysis can be applied whenever elements are related in some way. Relationship charts, which illustrate relationships and flows, are a form of link analysis. Such charts are useful not only for mapping known relationships, but also for revealing hidden relationships. Link analysis is an application of ripple theory, which describes the tracing of the incremental effects of some initial stimulus. According to ripple theory, any action has a series of resulting actions or consequences. This proposition is clearly evident in business transactions.

Timelines:

In almost every forensic accounting engagement, it is useful to create a timeline of key events. A timeline is a summary of events arranged in chronological order along a line. Suitable material for timelines includes data that have numerous dates/times and activities, such as witness statements (including interviews, depositions, and trial transcripts), organizational records, and communications (such as letters, phone calls, and emails). Even if precise dates or times are not available, a timeline can still be created if the sequence of events is known.

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Describe Interview Transcription as a Process of Analysis and Interpretation

Learning Objective 8

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Interview Transcription

Involves the concurrent activities of data collection, analysis, and interpretation
Equally important as the end product
Should be done by the interviewer

Ensures accuracy
First opportunity to transform facts into story

Discretion of the interviewer is involved
Interpretations involves

Selection
Reduction

Although the obvious goal of transcription is to record and preserve the data collected in the interview, the transcription process is a valuable tool that can serve a variety of purposes. As with most forms of qualitative research, interview transcription involves the concurrent activities of data collection, analysis, and interpretation.
The process of transcribing an interview is equally as important as the end product. Recognizing the importance of this process clarifies the value of writing as a method of inquiry, a characterization that is embraced in the qualitative research disciplines. Scattered thoughts about the subject must be assembled into a coherent structure that facilitates understanding. Moreover, writing is the means by which this analytic process is made apparent and available for review.
These propositions highlight two reasons why an interview should be transcribed by the person who conducted the interview. First, it ensures that the record of the conversation is as accurate as possible and allows for the inclusion of contextual elements (such as aspects of the physical setting and sensory observations). Second, it is the forensic accountant’s first opportunity to extract meaning from the data—to transform facts into a story.
The story emerges as the forensic accountant organizes the data into a logical structure. Usually, interview notes are transcribed in order (that is, following the actual flow of the interview) and then rearranged into categories based on themes. In qualitative research, this is known as thematic analysis or coding. Identifying key themes and linking the data according to these themes is the primary way that meaning is created.
Interview transcription is not considered creative writing, but there is still a great deal of discretion involved. Although the content is driven by the information that was actually exchanged in the interview, this information can be compiled and presented in any number of ways. Interview transcripts have been characterized as “textual products” that result from a three-part process of construction, each of which is impacted by the active role of the interviewer: (1) the verbal exchanges by which the data are created; (2) collecting the data (through video/audio recording or handwritten notes); and (3) drawing on literary and grammatical conventions to transform verbal accounts into textual accounts.
In the third part of this process, the interviewer is challenged with communicating not only what was said, but how it was said. This allows for the exercise of two key forms of influence—selection and reduction.

Selection: Determining what information to include in the interview transcript. The challenge is that, early in an engagement, the forensic accountant may not yet have a clear understanding of what is or is not relevant.
Reduction: Determining how the information obtained in the interview is reduced to words.

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