Personality and Individual Differences 37 (2004) 1597–1613
www.elsevier.com/locate/paid
Environmental knowledge and conservation
behavior: exploring prevalence and structure in
a representative sample
Jacqueline Frick a, Florian G. Kaiser
b,*
, Mark Wilson
c
a
b
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Eindhoven University of Technology, Technology Management, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
c
University of California, Berkeley, USA
Received 7 July 2003; received in revised form 19 January 2004; accepted 19 February 2004
Available online 9 April 2004
Abstract
Knowledge is commonly seen as a necessary precondition for a person’s behavior. Consistent with this,
most educational interventions rely on knowledge transfer. However, for the most efficient informational
strategies for education, it is essential that we identify the types of knowledge that promote behavior
effectively and investigate their structure. A questionnaire consisting of three environmental knowledge
scales and a conservation behavior measure was sent to 5000 randomly selected Swiss adults. A completed
questionnaire was returned by 55% of them ðN ¼ 2736Þ. A series of structural equation analyses indicates
that the three knowledge forms exert different influences on conservation behavior: Action-related
knowledge and effectiveness knowledge have a direct effect on performance. In contrast, system knowledge
is more remote from behavior, exerting only a mediated influence on it by way of affecting the other two
knowledge types.
2004 Elsevier Ltd. All rights reserved.
Keywords: Knowledge level; Declarative knowledge; Environmental knowledge; Environmental education; Conservation (ecological behavior); Measurement; Item response theory; Rasch model
*
Corresponding author. Tel.: +31-40-247-4751; fax: +31-40-244-9875.
E-mail address: f.g.kaiser@tm.tue.nl (F.G. Kaiser).
0191-8869/$ - see front matter 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.paid.2004.02.015
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J. Frick et al. / Personality and Individual Differences 37 (2004) 1597–1613
1. Introduction
Knowledge is regarded as essential for successful action. This is not only the case for basic skills,
such as reading and writing, but also for highly sophisticated professional performance, such as
brain surgery. Consistent with this, knowledge-based campaigns have always been a popular
means of promoting certain behaviors in the general public, like conservation behavior (for a review, see Boerschig & De Young, 1993). In health education, as another example, knowledge is
provided to encourage people to avoid harmful behaviors such as excessive sun-bathing (for a
review, see Buller & Borland, 1999) or drunk driving (for a review, see Mann, Leigh, Vingilis, &
Genova, 1983). In sex education, teenagers are given advice about safer sex to avoid teenage
pregnancy or HIV infection (cf. Besharov & Gardiner, 1997; Moore & Sugland, 1997). In all these
enterprises, knowledge is regarded as a means to overcome psychological barriers such as ignorance and misinformation; it is viewed as a necessary, though generally insufficient, precondition
for successful action. In other words, although knowledge does not always have the intended effect
on a target behavior itself, it may at least fuel other mechanisms that facilitate behavior change (cf.
Pratkanis & Turner, 1994; Ronis & Kaiser, 1989; Schahn & Holzer, 1990).
To be fully effective, educational campaigns should be designed with a profound understanding
of the underlying knowledge structure. It is important to ascertain how much people already
know and what type of knowledge is essential to promote the target behavior. While cognitive
psychologists usually distinguish declarative knowledge (factual knowledge) from procedural
knowledge (skills that transform declarative knowledge into action; see, for example, Anderson,
1976), they normally do not differentiate among different types of declarative knowledge. However, it seems necessary, for both practical and theoretical considerations, to disentangle different
forms of declarative knowledge and to explore the ways they work together when the aim is to
foster certain kinds of behavior (see Kaiser & Fuhrer, 2003).
In the present study, we focus on environmental knowledge––conservation-relevant knowledge––and explore the prevalence of different types of knowledge in a large representative sample.
We also look at their role in shaping conservation behavior. Our goal is to demonstrate that with
a better understanding of the interrelationship of different types of knowledge, the enterprise of
changing behavior in the general public can become a more scientifically informed process, in
which individual differences and effective behavioral change efforts can be evaluated empirically
and understood theoretically.
1.1. Forms of environmental knowledge
To date, most research studies on environmental knowledge have examined only one (e.g.,
Gambro & Switzky, 1999; Leeming, Dwyer, & Bracken, 1995; Moore, Murphy, & Watson, 1994)
or, at most, two forms of environmental knowledge (e.g., Hines, Hungerford, & Tomera, 1986/87;
Schahn & Holzer, 1990; Schultz, 2002; Spada & Ernst, 1992). Naturally, these studies do not
analyze the relative effects of different knowledge forms on behavior comprehensively. This
negligence also results in a lack of understanding of the ways in which different knowledge forms
work together in promoting conservation behavior (cf. Kaiser & Fuhrer, 2003). For example,
before a person can act, he or she must have some understanding of the natural states of
J. Frick et al. / Personality and Individual Differences 37 (2004) 1597–1613
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ecosystems and the processes within them (system knowledge), and also know what can be done
about environmental problems (action-related knowledge). A third form of knowledge, knowledge about the benefit (effectiveness) of environmentally responsible actions, is particularly relevant when people have to choose from a pool of possible actions. In the next three paragraphs, we
expand on these forms of declarative knowledge.
System knowledge usually relates to the question of how ecosystems operate (e.g., Schahn &
Holzer, 1990) or to knowledge about environmental problems (e.g., Hines et al., 1986/87). A
typical example is knowledge of the relationship between carbon dioxide (CO2 ) and global climate
change. Many of the scales currently used to measure environmental knowledge conform to a
system-knowledge scale that was originally proposed by Maloney and Ward (1973).
If environmental system knowledge is defined as ‘‘knowing what,’’ then action-related knowledge is ‘‘knowing how,’’ or knowledge of behavioral options and possible courses of action (Ernst,
1994). For example, if people know that CO2 contributes to global warming, they may still not
know what actions they can take to reduce their CO2 emissions. Action-related knowledge should
not be confused with the much less tangible procedural knowledge (i.e., discrete skills, action
schemas, or scripts; Schank & Abelson, 1977), which allows people to act appropriately in situations and can be automated through practice. In contrast to procedural knowledge, declarative
action-related knowledge is accessible by means of questioning, for it can be verbalized. It refers
to information that either has direct relevance for action (if I do not use my car, I produce less
CO2 ) or indirect relevance (gray energy is energy invested into products before I buy them––a fact
that I should consider when I buy certain products). Some findings suggest that action-related
knowledge represents a better predictor of conservation behavior than system knowledge (e.g.,
Sia, Hungerford, & Tomera, 1985/86; Smith-Sebasto & Fortner, 1994), which could be due to its
relatively more behavior-proximal nature (Martens, Rost, & Warning-Schr€
oder, 2001). Note that
system and action-related knowledge are frequently distinguished. In sex education for instance, it
is common to distinguish between reproductive physiology knowledge and birth control knowledge (e.g., Morrison, 1985; Volbert & Zanden, 1996).
Different conservation behaviors have different conservation potentials (cf. Stern & Oskamp,
1987). Buying a new, fuel-efficient car, for instance, can be a better way to cut down CO2 emissions than driving an old car less often (Stern & Gardner, 1981). The kind of knowledge required
here, environmental effectiveness knowledge, addresses the relative gain or benefit (i.e., the relative
conservational effectiveness) that is associated with a particular behavior. This sort of knowledge
has repeatedly been proposed as relevant for successful action (e.g., Kaiser & Fuhrer, 2003). It has
been labeled ‘‘relational knowledge,’’ ‘‘task knowledge’’ (van Raaij, 1988), and ‘‘impact knowledge’’ (Schultz, 2002). With this form of knowledge, the focus in action-related knowledge has
obviously been extended from a mere knowing how to conserve to knowing how to get the
greatest environmental benefit (e.g., Hanna, 1995). 1
1
A fourth knowledge type––social knowledge––also included occasionally, is omitted because of its rather subjective
and evaluative nature. Since reference groups and their norms are chosen individually based on personal preferences,
standards, and existing social ties, social knowledge can hardly be assessed as a unidimensional achievement and thus
cannot be compared with the three other knowledge forms.
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1.2. Assessment of environmental knowledge
Our knowledge generally allows us to solve certain tasks or problems. To measure a concept
like knowledge, one can organize tasks by determining how demanding they are. Ideally, to explore a person’s knowledge, we should include items that range from one extreme of this spectrum
to the other and many points between (cf. Guttman, 1944; Wilson, 2001). This is common practice
in educational and achievement assessment. The Rasch family of models is consistent with this
idea. One specific feature of the Rasch model is that the order of the task difficulties remains the
same for all persons regardless of their performance level (see Wilson, 2001). In other words, a
concept (i.e., a latent variable) for which a Rasch model holds true is explicitly defined by the
tasks and their unambiguous order. Thus, in the mathematical framework of the Rasch model, it
is possible to interpret different person performances straightforwardly, because the meaning of a
certain difference along the measurement instrument is uniform no matter where the person falls
along the measured dimension.
The multidimensional extension of the Rasch family of models necessary to calibrate a threedimensional knowledge test is a fairly new development. Based on the multidimensional random
coefficient multinomial logit (MRCML) model, a multidimensional extension of the Rasch model
(Adams, Wilson, & Wang, 1997; see Section 2), Kaiser and Frick (2002) developed an environmental knowledge measure designed to differentiate between system, action-related, and
effectiveness knowledge. According to the authors, a possible reason why the predicted threedimensional structure could not be confirmed empirically was, because a student sample was used
for this first scale calibration. To avoid such a bias, the present study was conducted with a
population sample. Aside from the lack of three-dimensionality in the student sample, the newlydeveloped environmental knowledge scale did reveal reasonable fit statistics and reliability
information (i.e., figures comparable to the ones in Tables 2 and 4). Regarding construct validity
this previous research showed that students (majoring in three different fields of study with different emphases on environmental issues) and environmental sciences lecturers differed as predicted with respect to environmental knowledge and conservation behavior. Moreover, it also
showed 48% common variance with a traditionally constructed (i.e., based on classical test theory)
environmental knowledge scale that was used in the Swiss Environmental Survey (cf. Diekmann &
Franzen, 1996).
1.3. Research objectives
If different forms of knowledge are supposed to work together, the question arises as to how
they are interrelated. We assume that system knowledge provides a necessary foundation for
action-related and effectiveness knowledge (see also Kaiser & Fuhrer, 2003). While system
knowledge seems necessary to motivate a search for action-related knowledge as well as to generate effectiveness knowledge, it does not itself directly affect behavior. Action-related knowledge,
ideally, contains a wide range of behavioral alternatives, and effectiveness knowledge helps a
person choose among these different behavioral alternatives. Expectedly, these knowledge forms
are believed to affect behavior more proximally than system knowledge. Action-related knowledge, in turn, also co-determines effectiveness knowledge. Before you can seek to understand the
relative conservational benefits of an action, you have to be knowledgeable about behavioral
J. Frick et al. / Personality and Individual Differences 37 (2004) 1597–1613
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alternatives in the first place. In other words, without knowledge of behavioral options, no
effectiveness knowledge can be usefully accumulated (cf. Fig. 1).
In this paper we aim to explore the role of environmental knowledge in the promotion of
conservation behavior. We believe that a multidimensional knowledge test analyzed with the
MRCML model can meet the requirements of ability testing and thus reliably assess the status
quo of environmental knowledge in a representative sample.
2. Method
2.1. Procedure
From the population of all German-speaking Swiss people, a sample of N ¼ 5000 (about 0.1%
of the entire population) was selected by means of a two-stage random sampling procedure. First,
25 municipalities were selected from all 1743 German-speaking Swiss municipalities (i.e.,
municipalities where more than two-thirds of residents are German-speaking). Each municipality
was weighted by its number of residents. Thus, a municipality’s chance of being chosen was
proportional to its size. 2 Second, 200 persons each (all between 18 and 80 years of age) were
randomly drawn from the registers of residents of the 25 selected municipalities.
Participants received the questionnaire by mail. As incentives, a lottery with a prize of 1000
Swiss Francs in cash (approx. US$ 600) was announced, and participants were offered the option
for personal feedback on their own performance in the test. As the questionnaire contained an
identification code, our survey was confidential but not anonymous. Of the target sample
ðN ¼ 5000Þ, 23% returned a completed questionnaire within two weeks, at which time a reminder
was sent to non-responders. Another 14% responded within the next two weeks. A second
questionnaire was then mailed to those who had still not responded. This added 13% more responses over the following two weeks. Finally, a second reminder was sent out to all remaining
non-responders, resulting in another 5% more responses. Of all contacted persons, 6% actively
refused to participate. Overall, 55% ðN ¼ 2769Þ of the originally selected sample returned their
questionnaires. From these, 33 cases had to be eliminated due to missing data, obvious noncompliance or indications of multiple respondents, or language problems.
To explore the reasons for participation and how conscientiously the questionnaires had been
completed, a telephone interview was conducted with 100 randomly selected participants about
three months after the main survey. Of these participants, 85% reported that they had completed
the questionnaire without any help, 15% had asked other persons or looked up answers in a book.
About half of the people who needed assistance (i.e., 8%) had filled in the questionnaire jointly
with someone else (mostly a spouse). The other half had only received help with individual
questions. Of all respondents, 47% reported that their reason for participation was interest in the
topic; 12% indicated that the reminders had eventually made them participate.
2
Because of its size, Z€
urich, being by far the largest city in Switzerland, would have received a selection probability
exceeding p ¼ 1:00. To reduce below determination its chance of being selected, this weight was fixed at p ¼ 0:90.
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2.2. Participants
The final sample consisted of 2736 persons. Given the demanding quality of the questionnaire,
a response rate of 55% is quite remarkable. The median age of the participants is 44.0 years
(M ¼ 45:6, SD ¼ 16:6); 47.8% of them are male. As our sample selection procedure aimed at
attaining a representative sample of all German-speaking Swiss, we compared age, gender, and
educational background with the corresponding data from the most recent Swiss census (BfS,
1999, 2000). Both gender distribution ðv2 ð1Þ ¼ 2:40, p > 0:05Þ and age distribution ðv2 ð3Þ ¼ 6:12,
p > 0:05Þ resemble the distributions in the Swiss population (see Table 1).
In contrast, the participants of this study are better educated compared to the Swiss population
as a whole (v2 ð2Þ ¼ 121:27, p < 0:001). While basic education is underrepresented, college and
university education are slightly overrepresented. The questionnaire was probably more appealing
to persons with higher education and with better language skills. This finding is supported by the
fact that only 11% of all participants have a native language other than German, which is less than
the 20% average proportion in the selected municipalities.
2.3. Measures
The questionnaire consisted of (a) a set of conservation behavior statements and (b) three sets of
environmental knowledge questions. The conservation behavior measure represents a 50-item
version of the General Ecological Behavior scale (for the original scale, see Kaiser, 1998; for an
Table 1
Comparing Swiss census and municipality statistics with sample descriptors
Descriptor
Present sample
Swiss censusa
Selected municipalitiesa
Gender (% male)
47.8%
49.3%
48.1%
Age (years)
20–34
35–49
50–64
65–79
28.2%
30.2%
26.3%
15.3%
28.4%
31.7%
24.3%
15.5%
–
–
–
–
Education
Basicb
Additionalc
Higher
15.9%
73.4%
9.7%
28.4%
63.2%
6.1%
27.1%
67.3%
5.6%
Note. Comparisons of the first (sample) and the second column (census) provide information about the representativeness of the sample. Comparisons of the first and the third column (municipality statistics) reveal information about
response bias.
a
Swiss population data for age––published by the Federal Statistical Office (BfS, 1999)––were available only for the
whole country ðN ¼ 6; 873; 687Þ. For gender and education, we also found information for the selected municipalities
(N ¼ 714; 555; see BfS, 2000). For educational data, persons older than 24 years were included; persons with no formal
education (2.3% of the population) are missing.
b
Basic education is 9 years of mandatory school.
c
This represents a combination of several categories, which we combined to better match the available data published
by the Federal Statistical Office (BfS, 2000).
J. Frick et al. / Personality and Individual Differences 37 (2004) 1597–1613
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extended version, see e.g., Kaiser & Keller, 2001; for external validity information, see Kaiser,
Frick, & Stoll-Kleemann, 2001; for ecological validity information, see Kaiser, Doka, Hofstetter,
& Ranney, 2003). The scale consists of 50 statements concerning a person’s own conservation
performance. These behaviors can be grouped into six domains: energy conservation, mobility and
transportation, waste avoidance, consumerism, recycling, and vicarious social behaviors toward
conservation. Two example items are ‘‘I bring empty bottles to a recycling bin,’’ and ‘‘I drive my
car to or in the city.’’ For 21 items, a dichotomous yes/no format was used, as the behavior relates
to one-shot decisions, such as the adoption of solar panels. By contrast, some behaviors, such as
commuting, do not obviously split into a dichotomous format. For these 29 items, a 5-point
polytomous response format was used, assessing the frequency by which the behaviors were performed. Contrary to common expectations though, a more diverse response format makes participants’ answers more arbitrary and less reliable (Kaiser & Wilson, 2000). Thus, we decided to
recode those 29 behaviors to a dichotomous response format by collapsing never, seldom, and
occasionally to a negative answer and categorizing often and always as affirmative responses.
Cannot be answered was a response alternative when giving an answer was, for whatever reason,
not possible. Such responses were coded as missing values with 1% of all behavior statements found
to be missing. The behavior measure was calibrated using the dichotomous Rasch model (for item
response theory details, see e.g., Embretson & Reise, 2000). Reliability coefficients for the General
Ecological Behavior scale are reasonable, and the model predictions for both the behavior items
and the persons acceptably fit the data (see Tables 2 and 4).
Based on a scale calibration study (Kaiser & Frick, 2002), we selected 60 items to assess three
types of environmental knowledge: 21 items are indicators of system knowledge, 20 of action-related knowledge, and 19 of effectiveness knowledge (see Table 3 for item examples). Forty-four of
these items were presented in a multiple-choice format, of which 11 actually allowed multiple
responses (partial credit was given for partially correct responses). Another 16 items were offered
as dichotomous true/false statements. Unanswered questions were coded missing; 3% of all answers to the knowledge questions were missing.
Table 2
Reliability information and scale descriptors
geb
Reliability information
MML reliabilitya
Scale descriptors
M
SD
SEM
sys
act
eff
know
0.76
0.67
0.66
0.50
0.71
0.21
0.59
0.011
0.27
0.42
0.008
0.39
0.42
0.008
0.12
0.23
0.004
0.27
0.35
0.007
49.3%
54.4%
55.4%
52.9%
54.5%
b
% affirmative/correct answers
Note. geb stands for the General Ecological Behavior scale, sys for system knowledge, act for action-related knowledge,
eff for effectiveness knowledge, and know for overall knowledge.
a
The Marginal Maximum Likelihood (MML) reliability represents the ratio between observed sample variance ðr2h Þ
and the MRCML estimate of the ‘‘true’’ population variance (r2T ; see Mislevy, Beaton, Kaplan, & Sheehan, 1992).
b
Mean (M), standard deviation (SD), standard error of measurement (SEM), are all expressed in logits (i.e., the
natural logarithm of the odds ratio for correct answers or endorsement, respectively).
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J. Frick et al. / Personality and Individual Differences 37 (2004) 1597–1613
Table 3
Two example items for each knowledge type
Knowledge type
Multiple-choice format
True/false format
System knowledge
(21 items)
What causes wind?
Thrust of the clouds
Temperature differences
Barometric pressure differences
Ocean currents
The ‘‘El Ni~
no’’ phenomenon is a direct consequence
of the greenhouse effect
(true/false)
Action-related knowledge
(20 items)
How can the ozone concentration in the summer be
lowered?
By not using solvents
By not using cars
By reducing electricity consumption
By turning off air conditioning
In recycling, there is no
energy loss (true/false)
Effectiveness knowledge
(19 items)
Energy-efficient light bulbs save about
20%
50%
80%. . . of electricity compared to conventional bulbs.
Non-returnable beer
bottles are just as
ecological as cans
(true/false)
Before exploring the knowledge–behavior interrelationship in some detail, we had to confirm
the predicted three-dimensional structure of environmental knowledge. The three-dimensional
environmental knowledge measure was calibrated by using the MRCML model (for the model
details see Adams et al., 1997). The MRCML model, similar to confirmatory factor analysis,
allows us to test a specific predicted item-factor structure. Multidimensionality, in our case, exists
solely on the test and not on the item level. In other words, each item is assigned to only one of the
three knowledge dimensions (cf. Wang, Wilson, & Adams, 1997).
By applying the MRCML model, the postulated three-dimensional structure was tested against
a one-dimensional knowledge measure. Model fit was based on the G2 statistic, which is a loglikelihood statistic approximately v2 -distributed (cf. Adams et al., 1997). The relative goodness-offit of models was tested using the G2 difference of the models (i.e., DG2 ). The data-model
expectation fit of the three-dimensional structure ðG2 ð76Þ ¼ 199; 413:1Þ was significantly better
than the comparable fit in the one-dimensional case (G2 ð71Þ ¼ 199; 616:7; DG2 ð5Þ ¼ 203:6,
p < 0:001). Evidently, the theoretical distinction between system, action-related, and effectiveness
knowledge was empirically supported. Besides the three specialized knowledge measures, the
compound overall knowledge measure also showed quite reasonable reliability coefficients (see
Table 2). Item fit and person fit statistics of all the knowledge scales appeared to be reasonable as
well (see Table 4). Note that effectiveness knowledge revealed a relatively low reliability compared
to the other scales.
A closer look at the proportion of correct answers indicates on the one hand that actionrelated knowledge seems––with 55% correct responses compared to 54% and 53% for system
and effectiveness knowledge, respectively––marginally more accessible in this sample than
the other two knowledge forms (see Table 2). Effectiveness knowledge, on the other hand,
turned out to be slightly less available (i.e., 53% correct answers) than the other two knowledge
forms.
J. Frick et al. / Personality and Individual Differences 37 (2004) 1597–1613
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2.4. Statistical analysis
Structural equation modeling was used to explore the knowledge–behavior interrelationship.
All structural equation models were assessed using the maximum likelihood method (by using
LISREL8; cf. J€
oreskog & S€
orbom, 1993). They were all tested confirmatorily (i.e., without ad hoc
model modifications; cf. MacCallum, Roznowski, & Necowitz, 1992). In other words, all model
modifications were theory guided and, thus, are reported upon in Section 3. The covariance
matrix was used as the input matrix (a copy can be obtained from the authors). Due to statistical
identifiability of the factor loadings (the reliability indices), we produced five indicator variables
based on multiple imputation methodology for each of the four latent concepts––system, actionrelated, effectiveness knowledge, and conservation behavior. Each of the five indicator values per
person represents a so-called plausible person score. Each score is derived as a random draw from
the estimated distribution of the partial-credit model-based person estimates with similar patterns
of item responses (for methodological details see e.g., Mislevy, 1991; for computational details see
Wu, Adams, & Wilson, 1998). All indicator variables are normally distributed and contain no
missing values. To avoid artificially correlated indicator variables, each set of items, e.g. the 21
system knowledge items, was calibrated separately on the basis of the partial credit model (for
item response theory details, see e.g., Embretson & Reise, 2000).
Table 4
Item and person fit statistics for all knowledge measures and the behavior scale
Fit statistic
geb
sys
act
eff
know
a
Item fit
MðMSÞ
SD(MS)
M(t)
SD(t)
Minimum (MS)
Maximum (MS)
Person fitb
MðMSÞ
SD(MS)
MðtÞ
SDðtÞ
Persons with poor fit ðt P 1:96Þ
1.00
0.03
0.12
1.79
0.94
1.09
1.00
0.04
0.11
3.51
0.93
1.06
1.00
0.06
)0.26
3.63
0.91
1.11
1.00
0.03
0.08
2.91
0.94
1.05
1.00
0.04
)0.02
3.59
0.91
1.09
0.99
0.38
0.05
0.88
6%
0.99
0.27
)0.05
1.00
3%
1.00
0.33
)0.02
1.03
3%
1.00
0.23
)0.01
0.98
2%
0.99
0.17
)0.07
1.11
4%
Note. geb stands for the General Ecological Behavior scale, sys for system knowledge, act for action-related knowledge,
eff for effectiveness knowledge, and know for overall knowledge. Ideally, the mean of the mean squared deviations of
expected and observed scores and the standard deviation of the t-values are one (MðMSÞ and SDðtÞ ¼ 1:0). Extreme
SDðtÞ values in the item fit statistics are due to the remarkable sample size in the current study and, thus, not very
informative. By contrast, the ideal mean of the t-values is zero ðMðtÞ ¼ 0:0Þ. No reference point for the standard
deviation of the mean squares (SD(MS)) can be given.
a
Due to the large sample size, and, hence, due to a large statistical power, significant t-values ðt P 1:96; p < 0:05Þ are
not very meaningful in the assessment of item fit. Therefore, one should rely more on the mean square (MS) statistics.
This fit index is independent of sample size and indicates the relative discrepancy in variation between model prediction
and actual data. For example, the averaged MS between 0.90 and 1.10 correspond to a maximum of 10% lack or excess
of variation in the model prediction compared to what was observed.
b
By contrast, since the item number is at most 60, t-values can be used as reliable fit indicators with respect to person fit.
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3. Results
Our theoretically proposed model consists of a particular knowledge structure, in which conservation behavior is directly influenced by action-related and effectiveness knowledge. Both of
these knowledge types are, in turn, determined by system knowledge, which does not have a direct
impact on conservation behavior. Moreover, action-related knowledge is expected to have an
immediate influence on effectiveness knowledge. Fig. 1 details the theoretically proposed
knowledge structure model.
To test our model and not to capitalize on chance (cf. MacCallum et al., 1992), we randomly
selected five subsamples of 500 persons each and performed the model test five times. Table 5
summarizes all standardized multiple regression coefficients (i.e., b-weights) and the amount of
behavior variance explained. It also lists the fit statistics for the tested models.
For four of the five relationships, the picture is conclusive: system knowledge predicts actionrelated knowledge and––with only one exception––effectiveness knowledge too. Action-related
knowledge predicts effectiveness knowledge, and––also with one exception––it determines
behavior. Inconsistent and non-conclusive, however, are the findings with respect to the influence
of effectiveness knowledge on behavior. This relationship seems doubtful, as it turned out to be
non-significant in three out of five model tests. Note, however, that––based on a binomial test––
the likelihood of finding two or more significant ðp < 0:05Þ relationships in five tests by chance
alone is unlikely at p ¼ 0:02.
By selecting a smaller subsample, we may also unwittingly have sacrificed statistical power to
detect valid relationships between latent concepts. A post-hoc analysis of statistical power revealed that the power to detect an effectiveness knowledge effect on behavior in these five model
tests was greater than 0.99 (for statistical details, see e.g., Loehlin, 1998). In other words, it seems
evident that the chances of missing a valid path are very low here, even with only 500 persons per
test. Thus, the existence of a link from effectiveness knowledge to behavior seems likely but is not
fully conclusively supported by these five subsample tests.
Subsequently, to obtain further evidence for the existence of the proposed five relationships, we
tested the hypothesized model with the entire sample ðN ¼ 2736Þ. The fit statistics proved reasonable (see Table 5). Note that the v2 statistic depends on sample size, and the current sample is
relatively large ðN ¼ 2736Þ. Note also that the model fit indicators that are relatively insensitive to
sample size (i.e., SRMR, RMSEA, CFI) unanimously suggest that the model fits impressively
well. Nevertheless, the overall fit statistics of structure equation models can be misleading and can
appear satisfactory although the theoretically meaningful, substantial part of a model is not
correctly specified (McDonald & Ho, 2002). Thus, the overall fit statistics have to be decomposed
into their measurement component (i.e., a factor analytical model without directed relationships
between the latent variables) and their theoretically meaningful constituent (in our case the core
knowledge structure model). After subtracting the contribution of the measurement model
ðv2 ð164Þ ¼ 218:5, p < 0:01Þ from the overall fit statistics ðv2 ð165Þ ¼ 218:5, p < 0:01Þ, we can
conclude that the theoretically meaningful, substantial part of the tested model appears well
specified ðDv2 ð1Þ ¼ 0:0, p > 0:05Þ.
Returning to the path from effectiveness knowledge to conservation behavior, it proved significant and, of course, the power to detect a significant effectiveness knowledge–behavior link in
the overall sample also was beyond 0.99. Out of the variance of effectiveness knowledge, 18% is
J. Frick et al. / Personality and Individual Differences 37 (2004) 1597–1613
1607
Fig. 1. The Knowledge Structure Model. Note. Five plausible values per person represent the indicator variables for
each of the four latent concepts (system, action-related, effectiveness knowledge, and conservation behavior) in this
model (e.g., geb1 to geb5). Arrows indicate directed relations between constructs. b-coefficients (i.e., standardized
multiple regression coefficients) represent their strength. Arrows without origin indicate proportions of error and
unexplained variances. The dashed arrow indicates the added path in the comparatively tested alternative model.
p < 0:001 (N ¼ 2736). In the measurement part of the model, no significance levels are indicated for factor loadings
of the observed variables and their measurement errors, for they are without exception highly significant.
predicted jointly by action-related ðb ¼ 0:25Þ and system knowledge ðb ¼ 0:23Þ. System knowledge alone determines 29% of the variance of action-related knowledge ðb ¼ 0:54Þ. Out of the
total behavior variance, 6% is explained by action-related ðb ¼ 0:18Þ and effectiveness knowledge
ðb ¼ 0:12Þ, which is fairly close to the approximately 9% or r ¼ 0:30 that is normally found in
comparable studies (cf. Hines et al., 1986/87).
To challenge the finding that system knowledge exerts no direct influence on conservation
behavior, we tested an alternative model in which we assumed system knowledge directly affected
behavior (see dashed path in Fig. 1). System knowledge failed to be a significant direct predictor
of behavior (b ¼ 0:00; t ¼ 0:02; p > 0:05). The statistical power to find the relationship in
question was greater than 0.99. Strictly speaking, it is not the lack of power that can be held
responsible for not finding an immediate system knowledge influence on behavior. Behavior does
not seem to be affected directly by system knowledge, and we can thus omit the path without loss
of model fit or explanatory power.
The fit statistics remained unaffected despite the fact that the comparatively tested alternative
model has one degree of freedom less than the theoretically proposed knowledge structure model:
Dv2 ð1Þ ¼ 0:0, p > 0:05, DSRMR ¼ 0.00, DRMSEA ¼ 0.00, DCFI ¼ 0.00. The zero change in the
chi-square statistic between the two models could be seen as overfit, indicating that the models are
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J. Frick et al. / Personality and Individual Differences 37 (2004) 1597–1613
Table 5
Model descriptors and fit statistics for six knowledge structure model tests
Five random subsamples ðn ¼ 500Þ
Descriptor/
statistic
Total sample
ðN ¼ 2736Þ
1
2
3
4
5
sys fi act
sys fi eff
act fi eff
act fi geb
eff fi geb
0.54
0.23
0.25
0.18
0.12
0.52
0.27
0.18
0.25
0.16
0.52
0.24
0.22
0.13
0.14
0.54
0.22
0.22
0.09
0.19
0.57
0.15
0.33
0.25
0.05
0.54
0.21
0.26
0.20
0.03
% (geb)
6%
12%
5%
5%
7%
4%
v2 ðdf ¼ 165Þ
SRMR
RMSEA
CFI
218.5
0.016
0.011
1.00
137.2
0.029
<0.001
1.00
145.0
0.028
<0.001
1.00
181.2
0.031
0.011
1.00
191.8
0.038
0.017
0.99
215.5
0.037
0.024
0.99
Note. sys stands for system knowledge, act for action-related knowledge, eff for effectiveness knowledge, and geb for the
General Ecological Behavior scale. All figures in the upper half of Table 5 represent standardized multiple regression
coefficients (i.e., b-weights); significant ðp < 0:05Þ values are in bold. % (geb) represents the proportion of explained
behavior variance. Fit statistics are presented in the lower half of the table. Besides the v2 statistic, we adopted the twoindex presentation strategy suggested by Hu and Bentler (1999). Their results suggest that a cutoff value close to 0.95 for
indices like the Comparative-Fit-Index (CFI) and a cutoff value close to 0.08 for the Standardized-Root-MeanSquared-Residual (SRMR) index are required in order to conclude that the model implied and the observed data fit
reasonably well. Like the SRMR index, the Root-Mean-Square-Error-of-Approximation (RMSEA) value is an indicator of badness of fit and represents a frequently used alternative to the CFI. Its suggested cutoff value is 0.06.
mathematically identical (e.g., Williams, Bozdogan, & Aiman-Smith, 1996). However, they are
not mathematically equivalent (i.e., one contains a direct link from system knowledge to conservation behavior, and the other does not), but virtual equivalence such as this can arise when
one of the theoretical relations turns out to be zero. Nevertheless, this is not a real matter for
concern––it simply reflects the strength of our findings.
4. Discussion
This study contributes to the understanding of the interrelationship of different forms of
declarative environmental knowledge and their differential significance in promoting conservation
behavior. Specifically, we explored (1) the dimensionality of environmental knowledge, (2) the
prevalence of different types of knowledge in the Swiss population, (3) the relative influence of
different types of knowledge on each other and on conservation behavior, and (4) we established
the explanatory power of environmental knowledge on behavior.
Dimensionality. Contrary to an earlier scale calibration and validation study (Kaiser & Frick,
2002), this time we were able to empirically confirm the theoretical distinction between system,
action-related, and effectiveness knowledge. The results, based on the MRCML model (i.e., a
multidimensional extension of the Rasch model), imply that an oblique three-dimensional
knowledge space represents environmental knowledge significantly better than a single knowledge
dimension. The obliqueness of this knowledge space––correlations of r ¼ 0:36, 0.54, and 0.38 were
J. Frick et al. / Personality and Individual Differences 37 (2004) 1597–1613
1609
found between the different knowledge forms in the present study––consequently was more extreme in the student sample of Kaiser and Frick (2002). A reason for this could be the less restricted variability in a student sample (due to the inclusion of more knowledgeable participants)
compared to a population-representative sample, which could have caused a tighter correspondence between knowledge forms as well as between knowledge and conservation behavior (18%
explained behavioral variance compared to 6% in the present study). While the fit statistics of
both the overall knowledge scale as well as the three subscales proved to be excellent (see Table 4),
the reliabilities appeared a little low but still were fairly reasonable (see Table 2).
Prevalence. Overall environmental knowledge in the Swiss population is relatively low. Even on
topics that seem to be prominently covered by the media, a lack of knowledge is not uncommon.
For example, less than half of the population knew the region where the ozone hole was most
spread out, and 77% of all persons could not say how to fight the greenhouse effect. These findings
correspond to earlier studies that found a lack of knowledge about current environmental
problems among Swiss (e.g., Diekmann & Franzen, 1996). Specifically, we found that relative to
system knowledge, action-related knowledge is slightly more available. Effectiveness knowledge,
by contrast, is the least prevalent. Although we had constructed the questions of this subscale to
be as easy as possible, this form of knowledge seems to be largely absent. For example, only 31%
of all persons provided a roughly accurate estimate of the energy that is saved with energy efficient
light bulbs. The restricted range of the variance (SD ¼ 0:23 compared to SD ¼ 0:42 with the other
two knowledge types) is further evidence of an even more pronounced lack of knowledge in this
dimension. Due to its particularly restricted variability, effectiveness knowledge also may not have
been as reliable as the other two knowledge scales. Note that the restricted range in variability
seems a possible cause for the generally relatively low reliabilities (see Table 2) and the moderate
factor loadings (see Fig. 1) that we found for all three knowledge measures.
Knowledge structure. The most significant practical finding with respect to knowledge-based
information campaign design and evaluation relates to the knowledge structure that we predicted
(see Fig. 1). The fit statistics indicate that the proposed knowledge structure fits the data
impressively well. Note that the quality of the model is also supported by its negligible residuals
(i.e., the residuals do not exceed ±0.01). System knowledge, that is a basic scientific understanding
of ecosystems, only seems to exert an indirect influence on behavior, but most strongly influences
action-related and effectiveness knowledge.
In samples of this magnitude ðN ¼ 2736Þ, the chance of finding marginal but still significant
effects is high. Therefore, we tried to verify the results by using five randomly drawn subsamples of
500 participants each. The interrelationships between different knowledge types were largely
confirmed. In some subsamples, however, the path from effectiveness knowledge to behavior
seemed doubtful. The excellent statistical power even in the subsample tests challenges the conclusion that effectiveness knowledge significantly determines conservation behavior. This weak
and unreliable relationship may be partly caused by the limited variance and the general lack of
effectiveness knowledge. Investigating this effect in an experimental setting by means of increasing
effectiveness knowledge may shed more light on this matter.
Behavior prediction. The low overall explained behavioral variance of 6% was comparable to
other studies (e.g., Hines et al., 1986/87). Although apparently small, this figure should not be
underestimated, since influences of knowledge on behavior are thought to be indirect, which
means that they are mediated by other variables (cf. Kaiser & Fuhrer, 2003). If such mediators
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J. Frick et al. / Personality and Individual Differences 37 (2004) 1597–1613
were included in a causal chain from knowledge to behavior, the role of knowledge may be even
better understood. Investigating these processes with regard to the different knowledge types
represents an important research question for the future.
The limitations of this study can be seen in its cross-sectional design and in the use of self-report
measures. Employing the three knowledge measures proposed in a longitudinal experimental
design in environmental education would certainly be promising. This study, however, aimed to
assess the status quo of environmental knowledge and to investigate relations between different
knowledge forms and conservation behavior in a representative sample. Probably, the use of selfreports only represents a minor problem concerning knowledge assessment. While the measure’s
construct validity was previously demonstrated (see Kaiser & Frick, 2002), social desirability
effects are not expected due to the assessment of knowledge in the form of an achievement test. As
regards conservation behavior, however, social desirability could theoretically play a role. Yet, the
instrument used in this study revealed satisfactory correspondence to observed behavior (Kaiser
et al., 2001). Moreover, in non-student samples, people with high scores on the General Ecological
Behavior scale were not more likely to respond in socially desirable ways (Kaiser, Ranney, Hartig,
& Bowler, 1999).
Based on this study, it can be concluded that declarative knowledge still represents a valid (i.e.,
necessary, though not sufficient) means of promoting conservation behavior, especially if
knowledge is viewed in a more sophisticated way than is usual. Nevertheless, knowledge alone
does not suffice in this endeavor, since only 6% of the behavioral variance can be explained by it.
In Switzerland, it definitely seems necessary to increase the level of environmental knowledge in
general. Specifically, effectiveness knowledge proved to be only marginally present in the sample.
Due to methodological reasons, such as a restriction of range, the influence of effectiveness
knowledge on conservation behavior still is questionable and appears weaker than the influence of
action-related knowledge. Nevertheless, we expect effectiveness knowledge to be more important
than it seems, and we believe that this form of knowledge deserves further investigation. With
regard to environmental decisions, behavioral costs often are obvious, but the environmental
benefits generally are unknown to the public (cf. van Raaij, 1988). Thus, strengthening effectiveness knowledge might be a promising approach in promoting conservation behavior.
We conclude that declarative knowledge should generally not be seen as a one-dimensional
construct. So, if knowledge forms can be discerned empirically as in our case, investigating
relationships among them may become particularly important. In this study, the proposed
knowledge structure proved to be well supported by the data. In practice, information about the
knowledge structure is highly relevant in the design of knowledge-based campaigns and educational curricula. For example, if a person lacks a basic understanding of a problem (i.e., system
knowledge), he or she may only partly be able to acquire action-related knowledge. Basic scientific
knowledge alone cannot lead to the target behavior, either. Furthermore, even if a person knows
what actions can be taken, the final decision to choose a behavioral alternative may additionally
be based on effectiveness knowledge. Following these findings then, knowledge-based education
should focus on all three knowledge forms. In particular, education should foster valid expectations about the impact or effectiveness of one’s own behavior as a necessary additional input to
promote desired behavior. This sort of knowledge has not been systematically incorporated in
educational campaigns so far, be it in environmental education or other fields of application.
J. Frick et al. / Personality and Individual Differences 37 (2004) 1597–1613
1611
Acknowledgements
This research was supported by grant #1114-55433 from the Swiss National Science Foundation, by the Human-Technology Interaction Division at the Eindhoven University of Technology, and by the Berkeley Evaluation and Assessment Research (BEAR) Center at the
University of California, Berkeley. We wish to thank participants for their time and effort, all the
community administrations for providing addresses, Fritz Spahni of the Swiss Federal Statistical
Office for providing us with municipality information, Ellen Russon and Steven Ralston for
language support, and Therese Kohler and Niklaus Stulz for collecting and entering the data. We
also are grateful to the editors and to two anonymous reviewers for their comments on an earlier
draft of this paper.
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