Deriving Revealed Causal Maps

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The causal mapping process used for this study is consistent with the steps provided

in Chapter II. We have expanded on those issues not previously addressed or unique to

this study.

Demographic Mean SD

Age 39.29 7.35

Gender (% male) 96.3% -

Years in IT 15.42 6.61

Years with current organization 3.35 2.91

Years of procedural experience 10.40 5.27

Number of procedural projects participated in 20.5 16.73

Years of object-oriented experience 4.73 1.54

Number of object-oriented projects participated in 6.79 9.46

Table 3 — Study I. OO interview demographic, N=24

Table 3 — Study II. Survey demographic, N= 142

Demographic Mean SD

Age (1 < 21, 2 = 21-30, 3=31-40, 4=41-50, 5=51-60, 6 > 60) 3.77 .870

Gender (% male) 85.9% -

Years in IT 19.98 8.90

Years with current organization 10.89 8.63

Years of procedural experience 16.52 7.76

Number of procedural projects participated in 32.01 34.86

Years of object-oriented experience 3.46 3.07

Number of object-oriented projects participated in 2.91 2.61

• Step 1: Identify Causal Statements. Due to the cognitive nature of this study it was

determined that in addition to explicit causal statements, implicit causal statements

should also be recorded. To establish the reliability of the identification procedure,

interview texts were coded by the researchers and one of two additional raters. The

raters were deemed appropriate to identify the causal statements because of their

familiarity with the technique, the domain under study, and they were not participants

in any portion of the study. There were two rounds of coding that covered

six OO interview texts chosen at random from the interviews. The reliability between

the researchers was calculated by measuring the level of agreement on terms and

linkages. The level of agreement between the researchers averaged 0.80, suggesting

an acceptable level of reliability.

• Step 2: Construct Raw Causal Maps. In the second step, the causal statements

identified in the first step were separated into “causes” and “effects” to construct

the “raw causal maps.”

• Step 3: Develop Coding Scheme. The relevant concepts were identified from the

statements. A software development expert reviewed the statements and independently

placed them into conceptual categories. The level of agreement between the

researcher(s) and rater averaged 0.87 and a total of 19 OO concepts were identified.

Once the conceptual level scheme was developed, the statements were placed into

the appropriate concepts.

Tying emergent concepts to established literature has been recommended to develop

categories that are distinct and uniform in breadth and level of abstraction (Carley &

Palmquist, 1992; Fahey & Narayanan, 1989; Priem, 1994). To do this, we assessed the

degree of congruence of the three theoretical frameworks identified in the literature

review to the evoked concepts.

Three object-oriented software development experts were asked to assess the congruence

of the emergent concepts with each theoretical framework. The developers were

provided the constructs from each theoretical framework (see Table 2) and a set of index

cards containing each of the 19 concepts, with a definition of the concept provided on

the back of the card for their reference. Each developer performed a card sort according

to each of the three theoretical frameworks. The card sort was conducted using a different

framework order for each developer. The average level of agreement among the developers

was 0.52 for Framework I, 0.33 for Framework II, and 0.70 for Framework III. Based

on the results of the card sort, we chose theoretical Framework III as the appropriate

construct level classification scheme. The correspondence between the 19 evoked

concepts and constructs is presented in Table 4.

• Step 4: Recast the “raw” maps into Revealed Causal Maps. Once the classification

scheme was completed, the causal statements for each respondent were placed into

the appropriate concept and construct level categories. The result is a concept and

construct level RCM for each respondent. The individual maps are then aggregated

(Axelrod, 1976; Bougon, Weick & Binkhorst, 1977; Nadkarni & Nah, 2003) into one

overall map.

• Step 5: Create Measures for the Maps. The analysis of the maps in this study was

based on past research in causal mapping (Bougon et al., 1977; Ford & Hegarty,

1984: Huff, 1990; Narayanan & Fahey, 1990; Nelson et al., 2000). The measures used

are borrowed from the social network analysis field (Knoke & Kuklinski, 1982) and

include the adjacency and reachability matrices, centrality and density measures

(see Chapter II for a detailed discussion of the measures).

Study II: Survey

• Data source. We developed an instrument to capture the concepts emerging from

the interviews. As a starting point, five statements per concept were selected for

inclusion in the instrument based on content and clarity. Internal validity and

construct validity were addressed through a pretest sort (Anderson & Gerbing,

1991). Based on the results of the pretest sort statements were re-worded or deleted.

The pilot instrument was developed from the remaining list of statements. The pilot

instrument was given to 31 software developers. We assessed scale internal

consistency reliability using Cronbach’s alpha (Huck, 2000). Correlations between

variables were analyzed, and questions were deleted or re-worded for clarity. The

final instrument covered 19 object-oriented concepts5.

Table 4. Construct and concept detail for theoretical Framework III

Construct Concept

Structure Abstraction




Information Hiding




Behavior Collaboration

Message Passing




OO Modeling/Analysis Identifying Objects

Noun-Verb Analysis

Object Model

OO Development Concepts Patterns


OO Development

• Sample. The validated instrument was then given to a large sample of software

developers. The sample was drawn from organizations selected based on their

identification of available “software developers” and their willingness to participate.

Thirty-three organizations of diverse industries (e.g., telecommunications,

manufacturing, aerospace and financial services) provided access to their software

developers. Study respondents were chosen based on a key-informant method

(Bagozzi, Yi & Phillips, 1991; Seidler, 1974). Participants in this phase of the study

were different than the respondents used in the previous phase of the project. A

total of 177 responses were originally recorded with 35 being eliminated due to lack

of software development experience, duplicate responses, or other problems with

the data, leaving a sample of 142 respondents. A profile of the Study II (survey)

sample is presented in Table 3.

• Factor analysis. We used an exploratory principal components factor analysis

with varimax rotation to reduce the variables to a usable set of constructs for the

OO software development expertise framework. Past research has provided guidelines

for the minimum sample size needed to conduct factor analysis. Some have

suggested the ratio of sample size to number of variables as a criterion: the

recommendations range from 2:1 to 20:1 (e.g., Bryant & Yarnold, 1995). Others have

suggested using a minimum sample size as the criterion. For example, Lawley and

Construct Alpha Concept

Nesting 0.806 Inheritance

OO Development


Object Characteristics 0.918 Attributes





Behavior II 0.818 Collaboration

Message Passing


OO Modeling/Analysis II 0.872 Identifying Objects

Noun-Verb Analysis


OO Development Concepts II 0.883 Abstraction


Information Hiding


Object Model

Table 5. Factor analysis for Framework IV

Maxwell (1971) suggest that there should be 51 more cases than the number of

variables. In their 1988 study, Guadagnoli and Velicer found that absolute sample

size was more important than functions of sample size in determining stable

solutions and recommend 100 to 200 observations. Our sample of 142 respondents

fell within the acceptable range for the analysis. The number of respondents

represents a sufficient sample to perform principal components analysis.

The resulting factor structure is summarized in Table 5, with the associated Cronbach’s

alpha reliability scores. As shown in the table, the object-oriented concepts loaded on

five factors that we named Nesting, Object Characteristics, Behavior II2, OO Modeling/

Analysis II, and OO Development Concepts II, using an eigen value >1.00 criterion.

Cumulatively, the factors accounted for 71% of variance in the sample. The Nesting

construct (a = 0.81) dealt with managing the complexity in the object-oriented mindset

(e.g., inheritance, polymorphism). Similarly, the Object Characteristics construct (a =

0.92) dealt with the packages that the basic concepts are put into (e.g., class, instantiation),

thus focusing on the development and functioning of an object. The Behavior II

construct (a = 0.82) consisted of the communication and relationships between sets of

objects (e.g., collaboration, message passing). The OO Modeling/Analysis II construct

(a = 0.87) consists of concepts that are the fundamental techniques for object-oriented

analysis (e.g., object, identifying things as objects). Finally, OO Development Concepts

II (a = 0.88) focused on how objects function within the larger system to make up an

object-oriented application (e.g., abstraction, layer), i.e., finding, modifying, and assembling

the classes and methods needed to support an entire application.

Results and Discussion

We present the results in three steps. First, we present the constructs revealed in the

interviews that are central to understanding OO software development expertise, highlighting

the characteristics of the revealed linkages between constructs in the aggregate

RCMs. Second, we summarize the results of the recasting of RCMs based on classification

suggested by the factor analysis of survey responses. Third, we provide an

assessment of the congruence between the original and the reconstructed RCMs.

Study I: Revealed Causal Maps

The aggregate level RCMs from the interview data represented OO software development

expertise as being constituted by four major constructs: Structure, Behavior, OO

Modeling/Analysis, and OO Development Concepts and is shown in Figure 2(a). The

overall density of the map is 1.75, indicating a relatively high interconnectedness. As

shown in Table 4, eight concepts comprise the Structure construct, which has a centrality

of 0.57, suggesting that it occupies a prominent role in OO software development. The

Behavior construct contains five concepts and the centrality is 0.57 suggesting a

prominent role in OO development. The OO Modeling/Analysis construct evoked in this

study contains three concepts and has a centrality of 0.43 suggesting a slightly lesser

role in OO software development. The fourth construct, OO Development contains three

concepts related to overall OO development. The centrality of the construct is 0.43,

suggesting a slightly lesser role in the OO approach. While the results are consistent with

those found by Sheetz and Tegarden (2001), this is the first study that has found the

importance of these additional concepts as components of OO. The data indicate that

the structure and behavior of objects are equally central to OO development, with

modeling and overall development slightly less central.

The reachability matrix of the aggregated construct level RCM is presented in Table 6.

It reveals that experts see OO Modeling/Analysis as a cause construct (all arrows from

the construct) and OO Development as an effect construct (all arrows into the construct).

The Structure and Behavior constructs are both cause and effect constructs. The highest

reachability occurs for the linkages in which OO Development Concepts is the effect