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Table 1: Variables identifi ed in human factors research on conceptual data modeling
Variable Type
Variable Category
Representative Examples
Independent variables
Data modeling formalism
(Data Model)
Hierarchical vs. relational
Relational vs. semantic
Semantic vs. semantic
Semantic vs. object-oriented
User characteristics (Human)
General MIS experience
Programming experience
Data modeling experience
Other modeling experience
Cognitive style
Task characteristics (Task)
Task type
Task complexity
Dependent variables
Performance
Model correctness
(facets, syntactic vs. semantic)
Time
Knowledge of the formalism
User attitudes
Confi dence
Preference
Perceived value
Ease-of-use
Control variables
User characteristics (Human)
Work experience
General IS/computer experience
Database experience
Age
Education
Intellectual ability
Cognitive style
Task characteristics
Complexity
Time
Structure
Diffi culty
Naumann, 1985; Liao & Palvia, 2000). Age (Liao & Palvia, 2000), education (Jarvenpaa &
Machesky, 1989; Liao & Palvia, 2000), intellectual ability (Juhn & Naumann, 1985), and
cognitive style measured with LSI (Jarvenpaa & Machesky, 1989) have been other types
of individual differences which have been controlled. In most studies, user characteristics
have been controlled by selecting subjects from a homogenous population and by random
assignment to experimental conditions.
Controlling for task characteristics ( Task in the framework in Figure 1) by keeping
them the same across the treatments is a natural approach and not very interesting at the
category level. Jarvenpaa and Machesky (1989) and Batra and Kirs (1993) both list spe-
cifi c characteristics of the task which were kept constant; these were complexity, structure,
diffi culty, and time, which are all related to a more general concept of diffi culty. Kim and
March (1995) specifi cally mentioned task complexity and time as task characteristics that
were controlled. Training was also identifi ed as a signifi cant control variable by Batra and
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