Cognition
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67
68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84
85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101
102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118
119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135
136 137 138 139 140 141 142 143 144 145 146
When maps have been elicited using highly structured rating scales common to each
participant, it is possible to construct one or more aggregate maps by combining
individual adjacency matrices. At the most basic level such aggregation can be accomplished
by simply adding or averaging participants’ judgments of common causal
relations (e.g., Bougon, Weick & Binkhorst, 1977; Ford & Hegarty, 1984; Voyer &
Faulkner, 1989).
Aggregated maps do not necessarily reflect the views of any one individual. However,
they are potentially insightful insofar as they enable the detection of overall group
tendencies, the possibility of widespread within-group variance notwithstanding (cf.,
Walsh, 1995). The latter, of course, is detectable by computing basic measures of spread,
such as the semi-inter-quartile ranges or standard deviations associated with particular
mean responses. Such aggregate analyses can be highly insightful, as, for example, in
the identification of mean sub-group differences in the perception of how much a given
construct influences, or is influenced by, other constructs, and its overall detrimental or
beneficial effect. Aggregate causal mapping methods also permit the study of the overall
structure of group-level mean perceptions of a given set of constructs, thus extending
the analysis beyond such basic bivariate relationships.
Table 2. Four indicators reflecting potentially significant differences between cause
maps (after Langfield-Smith & Wirth, 1992)
Difference
Meaning
1 The existence vs. non-existence of
particular variables
One individual or group believes that a particular
variable is important, whereas a second individual
or group does not
2 The existence vs. non-existence of
relationships between particular variables
One individual or group believes a given variable
has an influence upon or is influenced by another
variable, whereas a second individual or group
does not
3 The polarity of relationships represented
within the maps
One individual or group believes that the
relationship between two given constructs is
negative, whereas a second individual or group
believes the relationship is positive
4 The polarity strength
Two individuals or groups hold the same belief
about the polarity of a given relationship but one of
them believes the relationship is stronger than the
other