Vol 8 No 5 - October 2016

Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org
Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org Aajhss.org
Aajhss.org Aajhss.org Aajhss.org
Aajhss.org
International
Journal of
Humanities &
Social Sciences
Vol. 8, No. 5
IJHSS.NET
e-ISSN: 1694-2639
p-ISSN: 1694-2620
October 2016

Vol 8, No 5 – October 2016
Table of Contents
Effects of personal characteristics on susceptibility to decision bias: a
literature study
1
Alexander Toet, Anne-Marie Brouwer, Karel van den Bosch
and J.E. (Hans) Korteling
Likert Scale Development: Construction and Evaluation of Home
Environment Scale
18
Mukhtar Ahmad Wani & Aejaz Masih
Iqbal’s Response to Modern Western Thought: A Critical Analysis 27
Dr. Mohammad Nayamat Ullah and Abdullah Al Masud
Is more BENELUX cooperation the future for the low countries ? 37
Prof. dr. Herman Matthijs
AAJHSS.ORG

1 http://aajhss.org/index.php/ijhss
International Journal of Humanities and Social Sciences
p-ISSN: 1694-2620
e-ISSN: 1694-2639
Vol. 8 No. 5, pp. 1-17, ©IJHSS
Effects of personal characteristics on susceptibility to
decision bias: a literature study
Alexander Toet, Anne-Marie Brouwer, Karel van den Bosch, J.E. (Hans) Korteling
TNO
Kampweg 5, 3769 DE Soesterberg, The Netherlands
Abstract
Cognitive biases and heuristics are pervasive simplifications and distortions in judgement and
reasoning that systematically affect human decision making. Knowledge in this area may enable
us to foresee and reduce detrimental effects of biases or to influence others more effectively. We
therefore performed a literature study to assess the influence of personal characteristics
(cognitive abilities, expertise, personality, cultural background) on the occurrence of cognitive
biases. We found that each of the aforementioned factors can affect cognitive biases, though not
much is known about the effects of culture. Also, factors that appear to reduce a cognitive bias
may in fact mitigate (suppress or override) its behavioral effect rather than preventing the bias
from occurring at all. The general picture that arises is that bias susceptibility and the occurrence
of biases depend on thinking style (heuristic versus deliberate), where thinking style is associated
with an individual‟s personal characteristics. In general, biases are reduced when a deliberate
(analytical) thinking style is applied. However, whether a specific (heuristic or deliberate)
thinking style actually reduces or enhances a given type of bias also depends on the context.
Keywords: bias, heuristics, personal characteristics, decision making, judgement.
Introduction
People constantly make judgments and decisions, either conscious or unconscious. Most
decisions are made without knowing for sure what the effects of the decision will be and whether
the outcome will be positive or not. Making decisions in uncertainty is based upon at least two
factors: the desirability and the likelihood of the outcome. Decision theory studies the
development of algorithms that, for a given problem and likelihoods, produce the best outcome.
Decision theory can thus, in principle, provide recommendations how to best make decisions.
However, in real life people often make decisions that deviate from the best solution (Haselton,
Nettle, & Andrews, 2005).These deviations are the outcome of systematic distortions in human
judgment and reasoning.
Subjective estimations play a significant part in our daily life. Our decisions, conclusions
and explanations are based upon our beliefs about the likelihood of uncertain events but not on
their objective likelihood. For example, the decision to stay away from a particular neighborhood is
not driven by the actual likelihood of being robbed, but by our perception of the likelihood of being
robbed. The important conclusion of an immense body of research on decision making is that

people do not follow the laws of probability, but instead use relatively simple rules (heuristics).
These heuristics often perform well, but under certain conditions may lead to systematic and
serious errors. The distortion of human judgment and decision making is called „cognitive bias‟,
or shortly „bias‟.
The study reported here was performed to acquire further insight into biased (pre-
cognitive, automatic, heuristic) judgment and decision processes (that are influenced by
intuitions, emotions, biases, or associations) and to investigate whether biased thinking depends
on the personal characteristics of individuals. If there are indeed psychological characteristics
that predict biased thinking, this knowledge could in principle effectively be used to either
mitigate or deploy biased thinking. Knowledge of cognitive biases can for instance be used to:
 develop selection procedures (to develop tests that estimate an individual‟s susceptibility to
biases),
 reduce susceptibility to biases and ameliorate their effects (by training personnel and
institutions to recognize and cope with biases appropriately, and by developing fast and
frugal decision protocols),
 effectively deploy knowledge of biases against relevant actors (both on a strategic level as part
of a doctrine and operationally in the field).
The literature study reported here contributes to this goal by identifying characteristics of
individuals (e.g., cognitive abilities, expertise, personality, cultural background) that may predict a
person‟s susceptibility to cognitive biases, and in particular those leading to decision and
judgment biases.
The following section discusses the literature search strategy. Next we briefly discuss the
origins of human decision biases and factors that may affect the susceptibility to these biases.
Then we present a literature review on some personal characteristics that may predict an
individual‟s susceptibility to judgment and decision bias. Finally, we will present the conclusions
of this study.
Literature search strategy
Electronic searches were carried out using the databases ScienceDirect, PubMed and PsycINFO.
In addition, lliterature searches were also performed with Google Scholar. As search terms we
used the names of several well-known biases (Anchoring, Anchoring bias, Attribution error, Base
rate neglect, Belief bias, Confirmation bias, Conjunction fallacy, Framing, Halo effect, Hindsight
bias, Imaginability bias, Omission bias, Negativity bias, Outcome bias, Over-confidence bias,
Sunk cost effect) as well as some general terms (Bias, Cognitive bias, Decision bias, Decision
making, Judgement, Heuristic bias, Heuristics: for definitions see Appendix A) and combined
them with (conjunction: AND) terms related to personal characteristics (cognitive abilities OR
expertise OR personality OR individual differences OR culture OR reasoning OR thinking OR
trait) factors. The searches were restricted to articles reporting empirical studies in peer reviewed
journals. The relevant papers that were found in this initial search served as a starting point for
subsequent searches, that included all later papers referring to papers from the initial set (found
by using the „Cited by‟ function in Google Scholar. We included studies found using this method
in the review only if they involved influences of personal factors on human decision making. The
literature searches were performed in the second half of 2016.

Cognitive biases and their origins
The human mind is limited in its capacity to render judgments in a way that is perfectly rational
and fully informed. While rational thinking serves well to solve decision problems that allow
comprehensive analysis, it may fail in complex real life situations, where it is often very difficult
to have access to every relevant piece of information, and where a decision often has to be made
quickly. Even if such access were possible, our brains do not operate like computer algorithms,
capable of complex and multiple calculations in order to reach logically sound conclusions – not
to mention that we hardly have time to perform such rigorous analysis for every judgment that
we make. As a result, human decision making tends to rely on a variety of simple heuristic
decision rules that can be executed quickly. Oftentimes, heuristics produce judgements and
decisions that are „good enough‟ when measured against an acceptable cognitive load. However,
heuristics can also lead to irrational thinking and problem-solving in ways that produce errors or
illogical decisions, known as „cognitive biases‟. This is most likely to occur in complex situations
(when relevant information is ignored and/or irrelevant information interferes) or in situations
that are mistakenly perceived as familiar (while they are actually unknown). Cognitive biases are
pervasive in human reasoning and have important practical implications.
Dual-process heuristic-deliberate theories postulate a distinction between fast, intuitive,
automatic, heuristic, emotionally charged and fallible (heuristic or „Type 1‟ ) processes versus
slow, conscious, controlled, deliberate and analytic (deliberate or „Type 2‟) processes (e.g., Evans,
2006; Kahneman, 2003; Kahneman & Frederick, 2002; Sloman, 1996). When fast responses are
required, performance is based on low-effort heuristic processes. Deliberate processes supervise
and control the output of the heuristic system. In this view biases occur when deliberate
processing either (1) fails to successfully engage (Kahneman, 2003) or (2) fails to override the
biased heuristic response (De Neys, 2012). The serial deliberate processes are slower and require
more working memory and are therefore constrained by the limited capacity of the brain.
Conversely, heuristic processes function implicitly and in parallel and do not claim executive
working memory resources (De Neys, 2006). It is generally assumed that heuristic processes
result in biased outcomes, unless the analytical system takes over (Evans, 1984, 1989). In this
study we adopt the dual-process heuristic-deliberate theory of human decision making as a
framework to unify and understand our current findings.
Intuitive (heuristic) decision-making produces quick solutions based on general heuristics
(the Fast and Frugal view: Gigerenzer & Gaissmaier, 2010) or on experience-based (pattern-
matching) evaluations (Naturalistic Decision Making: G. A. Klein, 1993; see e.g. Kahneman &
Klein, 2009; G. Klein, 2015 and Davis, Kulick, & Egner, 2005 for a discussion on the differences
and similarities between both views). Intuition can provide access to information that would not
be beliedfble through deliberate thinking (Hogarth, 2010). However, intuition cannot generate or
acquire new knowledge. Also, since intuition is bias prone, it may lead to potentially dangerous
inaccurate perceptions of reality (Dane & Pratt, 2007; Kahneman & Frederick, 2002; Amos
Tversky & Kahneman, 1974).
In contrast to intuitive decision-making, deliberation uses abstract thinking and
generalizations, and allows the acquisition of additional information in the decision-making
process (Söllner, Broeder, & Hilbig, 2013).This is a clear advantage over intuition when problem
solving requires the application of complex rules (Kahneman & Frederick, 2002). In the real
world, decision makers often have to deal with three critical constraints that limit their
opportunities for deliberation: (1) limited access to information, (2) cognitive limitations inherent
in the human mind, and (3) limited time. These constraints result in „bounded rationality‟ (H. A.
Simon, 1972). Deliberation is time consuming and requires cognitive effort. Cognitive resources
claimed by deliberation cannot be deployed for other tasks (Kurzban, Duckworth, Kable, &

Myers, 2013). Decision-makers will therefore typically use their intuition when there is no
obvious need to use deliberate reasoning to make a good decision.
From a behavioral standpoint cognitive biases may be seen as systematic errors in
rational reasoning (Amos Tversky & Kahneman, 1974). However, many cognitive biases that
appear irrational from the viewpoint of rational choice theory may in fact be quite rational from
the perspective of evolutionary biology (Santos & Rosati, 2015). They may optimize decision
making in a given environment (context) by optimally using the available information (ecological
rationality: Goldstein & Gigerenzer, 2002). However, decision rules that are completely adapted
to a given (natural) environment may of course lead to maladaptive („biased‟) behavior in
different settings (Fawcett et al., 2014).
Factors affecting human cognitive bias
From a practical viewpoint, it would be useful to understand the factors that predict the
occurrence of cognitive biases. This literature review addresses the question whether
characteristics of individuals affect susceptibility to cognitive biases. The dual-process heuristic-
deliberate theory of human decision making (described above) is adopted as a framework to
unify and understand our current findings. This theory states that individuals are less prone to
biases if they apply the thinking style (deliberate or heuristic) that is most appropriate for the
problem context. Furthermore, an individual‟s tendency to make biased judgments and decisions
may be related to personal characteristics (e.g., cognitive ability, expertise, personality).
Cognitive ability
People differ in their cognitive abilities like intelligence, training (level of expertise), and thinking
styles. Studies investigating the correlation between measures of intelligence and a wide range of
different cognitive biases have shown that cognitive ability (both fluid and crystalized
intelligence) does not predict bias-proneness in general (Stanovich & West, 2008; Teovanovic,
Knezevic, & Stankov, 2015). Intelligent people are just as prone to cognitive bias as less
intelligent ones. However, highly intelligent people are more able than less intelligent ones to
avoid cognitive bias once they have been warned about the bias in advance and are instructed
how to avoid it (Stanovich & West, 2008).
Significant negative correlations have been observed between fluid intelligence and
several biases. Fluid intelligence is an individual‟s capacity to think in a logical way and to find
solutions for new problems, independent of acquired knowledge (Cattell, 1987). Fluid
intelligence has been found to correlate negatively with belief bias, over-confidence bias and base
rate neglect and also with the Cognitive Reflection Test (CRT: Frederick, 2005). The CRT is a
widely used tool to assess individual differences in intuitive–analytic cognitive styles. The CRT
has been found to correlate negatively with the sunk cost effect, belief bias and base rate neglect;
Teovanovic et al., 2015).Thus it seems that people with high reflective abilities and a high fluid
intelligence are less prone to these cognitive biases.
The literature shows mixed results on the relation between analytic intelligence and
proneness to the anchoring bias (for a review see: Furnham & Boo, 2011). While some studies
found that individuals with higher cognitive abilities are less susceptible to anchoring (Bergman,
Ellingsen, Johannesson, & Svensson, 2010), others observed no - or even the opposite - effect
(Oechssler, Roider, & Schmitz, 2009).
There is evidence that an individual‟s susceptibility to bias relates to structures and
emotional processes in the brain. Two key brain structures mediating emotional information

processing are the amygdala and the orbitofrontal cortex (OFC) (e.g., Kim & Hamann, 2007;
Zald, 2003). The amygdala can be seen as a primitive structure linking immediate threat with
rapid survival responses (Sander, Grafman, & Zalla, 2003). The OFC is associated with
deliberate thinking and has the function to gather and update information and use it to predict
possible outcomes of-, and to steer, human behavior (Rolls, 2004; Rolls & Grabenhorst, 2008).
De Martino et al. (De Martino, Kumaran, Seymour, & Dolan, 2006) examined the neural
mechanisms mediating the framing effect and the ability to control it. While placed inside an
fMRI scanner their participants performed a financial decision making task. In line with the dual-
process theory (i.e., the view that choices are typically affectively loaded and involve heuristic
thinking; see Kahneman & Frederick, 2007) this study showed that framing bias during financial
decision making correlated with a higher activity in the greater amygdala. In addition, they found
that subjects who acted more rationally also exhibited stronger OFC activation. Interestingly,
they also found a strong inter-individual variability in susceptibility to framing, which did not
correlate with amygdala activity. Instead, they observed a positive correlation between the ability
to control framing bias and OFC activation: increased orbital and medial prefrontal cortex
activity correlated with a reduced susceptibility to the framing effect (De Martino et al., 2006).
Although enhanced OFC activity does not necessarily imply the inhibition of emotional
processes (Aron, 2007), this result agrees with the view that controlling decision bias depends on
engagement of deliberate, rational thinking.
People who score high on the Need for Cognition (NFC: an individual‟s propensity to enjoy
and engage in thought: Cacioppo & Petty, 1982) are just as likely to be „framed‟ as anyone else.
However, compared to people scoring low on NFC, they are more consistent across different
frames of a problem. In accordance with the abovementioned results from brain research, the
magnitude of the framing effect is significantly reduced when decision makers are encouraged to
reflect on the options and to motivate their choice (Miller & Fagley, 1991; Sieck & Yates, 1997;
Takemura, 1993). For people who score high on the Need for Cognition this manipulation even
eliminates the framing effect altogether (A. F. Simon, Fagley, & Halleran, 2004). NFC has also
been found to moderate hindsight bias: hindsight bias is found for persons with low and
medium NFC scores, but not for people with high NFC scores (Verplanken & Pieters, 1988).
An individual‟s thinking style has often been associated with proneness to bias. An
individual‟s preference for an analytic-rational (deliberate processing) or an intuitive-experiential
(heuristic processing) thinking style can be assessed through the Rational-Experiential Inventory
(REI: Epstein, Pacini, Denes-Raj, & Heier, 1996). The relationship between thinking style and
various biases has been investigated in several studies. Persons that dominantly use an
analytic/rational thinking style tend to be less susceptible to the base rate neglect bias than
people using an intuitive/experiental thinking style (Ohlert & Weißenberger, 2015). There seems
to be no relation between thinking style and susceptibility to the conjunction fallacy (Lu, 2015).
Thinking style and belief bias appear to be linked: in contrast to people with an intuitive-
experiential thinking style, people with an analytic-rational thinking style are less susceptible to
belief bias (Svedholm-Häkkinen, 2015; Trippas, Pennycook, Verde, & Handley, 2015).
A specific cognitive ability that appears to be linked with thinking style is numeracy, or the
proficiency in basic probability and numerical concepts (Peters et al., 2006). An individual‟s
numeracy-competency is determined by (1) the degree of information processing (heuristic or
deep elaborative processing), (2) affective numerical intuition (e.g., framing); and (3) intuitive
understanding (e.g., gist-based representation and reasoning; see Ghazal, Cokely, & Garcia-
Retamero, 2014). Low numeracy is typically linked with intuitive (heuristic) thinking, whereas
high numeracy is typically linked with deliberate (analytical) processing (Brust-Renck, Reyna,
Corbin, Royer, & Weldon, 2014). In this view people with high numeracy are less susceptible to

biases because they show an analytical thinking style and information seeking behavior (Ghazal
et al., 2014). Numeracy has indeed been found to determine decision making quality across a
wide range of tasks (Sinayev & Peters, 2015). For instance, people with high numeracy are less
susceptible to framing bias (Gamliel, Kreiner, & Garcia-Retamero, 2015; Peters et al., 2006) and
conjunctions fallacies (Sinayev & Peters, 2015), are less over/under confident about their
decisions (Sinayev & Peters, 2015), and take less risks (Jasper, Bhattacharya, Levin, Jones, &
Bossard, 2013). People with higher numeracy are better able to extract the affective „gist‟ of a
problem and use it to determine the quality of a particular choice (Jasper et al., 2013). These
findings cannot be attributed to differences in general intelligence (Peters et al., 2006).
Somewhat in contrast, people using a combination of thinking styles (high
deliberate/high heuristic (also called „complementary thinking‟) and low deliberate/low heuristic
(also called „poor thinking‟) are found to be more susceptible to framing than those using a
dominant (either rational or intuitive) thinking style (Shiloh, Salton, & Sharabi, 2002). Thus, it
seems that people with a clearly dominant heuristic or deliberate thinking style are more resistant
to framing. This may be because both decision styles use strong internal guides (either logical or
experiential) to process information. In contrast, people with a more uniform thinking style
(either „complementary‟ or „poor‟) appear to depend more on coincidental external situational cues
(e.g., the way the information is formulated) when processing information.
Individuals with high emotional intelligence (the ability to recognize and distinguish
between emotions and to identify their causes) are able to reduce (or even eliminate) the effects
of decision bias by recognizing that the emotions they experience (for instance anxiety: Yip &
Côté, 2013) are irrelevant for the decisions they have to make. For example, military officers with
high emotional intelligence make better tactical decisions under stressful condition because they
are able to maintain a higher state of attentiveness for social cues and perform a more exhaustive
(deliberate) analysis of situational cues (Fallon et al., 2014).
Summarizing, several types of cognitive ability, as well as the ability to engage in
deliberate information processing at appropriate times, seem to protect an individual from
several cognitive biases (in particular, the sunk cost bias, the base rate fallacy, the over-
confidence bias, the belief bias, and framing). For anchoring and conjunction bias the evidence is
mixed. Emotional intelligence has been found to reduce the likelihood of falling prey to decision
bias. In general it can be concluded that cognitive ability does not safeguard an individual against
bias, but it may in some cases help in deploying countering mechanisms that reduce, or prevent
subsequent behavioral effects.
Expertise
Whether people deploy a heuristic or a more deliberate decision making mode depends for a
large part on the decision-maker‟s expertise (Fuchs, Steigenberger, & Lübcke, 2015). An expert is
an individual who has acquired special skills in a given domain (Chi, Glaser, & Farr, 1988). The
main distinction between experts and novices is the extent of their domain-specific knowledge
(Chi et al., 1988). Less experienced decision-makers increasingly use deliberative thinking to
solve subjectively complex problems while mainly following their preferred (either heuristic or
deliberate) decision style. In addition, the thoroughness of their information processing is
affected by their mood: a happy mood leads to more superficial (heuristic) processing, while a
sad mood leads to more thorough (deliberate) processing (Englich & Soder, 2009). In contrast,
more experienced decision makers use deliberation independent of their decision preferences,
subjective environmental complexity or mood (Englich & Soder, 2009; Fuchs et al., 2015).
Moreover, experts rely more on intuition than on deliberation. They can probably do so because

they have learned to match tools and strategies to problem structures (similar to chess players:
Sauter, 1999). For example, expert handball players are more intuitive than non-experts and tend
to rely on their first intuitively generated decision option (Raab & Laborde, 2011).
Expertise generally does not significantly reduce the anchoring bias effect (for a review
see: Furnham & Boo, 2011). However, expertise in a specific estimation context (task) may
reduce susceptibility to anchoring: experience in a card game was inversely correlated with
susceptibility to anchoring (Welsh, Delfabbro, Burns, & Begg, 2014). This implies that the exact
nature of the expertise should be clearly defined before being able to assess whether „experts‟ are
less affected by anchors than „non-experts‟. Individual differences in various traits may be more
useful for predicting the rate of learning, where it is their level of expertise that indirectly reduces
susceptibility to anchoring, rather than direct susceptibility to biases (Welsh et al., 2014).
Summarizing, expertise affects sensitivity to biases since it determines thinking style and
the way that information is processed in combination with its context. Expertise can stimulate
both deliberate and heuristic thinking. When the context is not appropriate, the latter may lead to
biases. Experts are less likely to misinterpret the context than novices. They are therefore more
likely to select the thinking style appropriate for the context, and are subsequently less prone to
bias than non-experts.
Personality
Personality is often defined in terms of five main personality traits (the „Big Five‟: John &
Srivastava, 1999): openness, conscientiousness, extraversion, agreeableness, and neuroticism.
The literature shows that individuals with high conscientiousness, agreeableness and
openness to experience or with low extraversion are more susceptible to the anchoring bias
(Caputo, 2014; Eroglu & Croxton, 2010; McElroy & Dowd, 2007; Teovanovic et al., 2015). It
has been suggested that because individuals with high conscientiousness engage in more
deliberate thinking when making decisions, they are more likely to perform a confirmatory search
for anchor consistent information. Individuals with high agreeableness tend to be more affected
by anchors than less agreeable persons. This is probably because individuals with high openness
to experience easily „adjust‟ their beliefs when considering situational information.
There are some indications that introverts are more susceptible to anchoring bias than
extraverts (Eroglu & Croxton, 2010; Furnham, Boo, & McClelland, 2012), but this relation is not
robust (Furnham et al., 2012). It has been suggested that low extraversion may be associated with
negative affect (Eroglu & Croxton, 2010), which may stimulate more deliberate thinking and
thereby activate a confirmatory search for anchor consistent information (Bodenhausen, Gabriel,
& Lineberger, 2000; Englich & Soder, 2009).
People scoring high on trait optimism (people who tend to believe in a bright future)
(Scheier, Carver, & Bridges, 1994) were more likely to update their judgments in response to
desirable information than to undesirable information, particularly for judgments that apply to
themselves (Kuzmanovic, Jefferson, & Vogeley, 2015). In other words, people with high trait
optimism show a pronounced self-specific optimism bias. It has been argued that the evolution
of the healthy mind to (optimistically) mis-predict future occurrences has led to an increased
resilience, improved coping behavior and reduced anxiety, resulting in overall improvements of
both physical and mental health (Dolcos, Hu, Iordan, Moore, & Dolcos, 2015; Sharot, 2011).
Optimism bias may sometimes even lead to better outcomes than do unbiased beliefs (Sharot,
2011). Recent brain studies have identified the OFC with trait optimism: higher OFC gray
matter volume (GMV) correlates with increased optimism (Dolcos et al., 2015).

Despite the hypotheses suggested above, it is not easy to determine the mechanisms
underlying the relationships between personality traits and susceptibility to cognitive bias.
Summarizing, personality appears to relate to bias susceptibility because it determines how
people weigh and process information.
Culture
Members of different social cultures may have different ways of thinking, because they have
been socialized from birth into different world views. Some researchers hypothesize that these
cultural differences affect an individual‟s susceptibility for bias. For instance, East Asians are
believed to have a holistic world view, attending more to contextual factors and assigning
causality to them, while they are less inclined to categorize and use formal logic. Westerners, on
the other hand, are typically more analytic and are more inclined to use pay attention to objects
of interest, to categorize them, and to use rules and formal logic to understand their behavior
(Nisbett, Peng, Choi, & Norenzayan, 2001; Strutton & Carter, 2013). As a result, East Asians
may for instance be less susceptible to attribution errors, since they see behavior primarily as a
product of external factors and not merely of the actor's dispositions. For the same reason, they
may be more susceptible to hindsight bias because they are readily able to find some explanation
for a given event since everything is connected in their world view (Choi & Nisbett, 2000; Yama
et al., 2010). Following this line of thinking, Westerners may be better able to withstand the
hindsight bias because they have a more rule-based thinking style.
Only a few studies address the influence of culture on bias susceptibility. Studies on the
effects of culture on hindsight bias show mixed results: while some studies confirmed the
abovementioned hypotheses (Choi & Nisbett, 2000; Yama et al., 2010) others found no cultural
differences in the sensitivity to hindsight bias (Pohl, Bender, & Lachmann, 2002). One study
(Scott, Christopher, & John, 1998) found evidence for the hypothesis that self-centered
Westerners, with their personal desire to be correct and to fortify one‟s choices, are more
susceptible to the sunk cost bias than collectivist East Asians who are more focused on
optimizing outcomes for the group. However, other studies found opposite results (Yoder,
Mancha, & Agrawal, 2014). They propose that East Asians may also be prone to sunk cost bias
because they are more concerned about saving face, resulting in more commitment to prior
decisions.
A recent study on choice framing (Haerem, Kuvaas, Bakken, & Karlsen, 2011) compared
military decision makers with business students (difference in organizational cultures). It was
found that business students showed the classic framing bias (risk avoidance behavior in the gain
frame an risk seeking behavior in the loss frame: A. Tversky & Kahneman, 1981), while military
decision makers consistently showed a risk-seeking behavior for both (gain and loss) choice
frames. In addition, military officers showed significantly higher levels of self-efficacy than
business school students. Self-efficacy correlated with risk seeking in the military group but not
in the civil group. This result agrees with the finding that people with little confidence in their
own competence do not like to gamble (Heath & Tversky, 1991). Military decision makers, on
the other hand, are probably so self-confident (or even over-confident) that they believe that
they can beat the odds.
Summarizing, while it is possible that culture affects sensitivity to bias (e.g., as a result of
different preferred thinking styles or levels of self-efficacy), only a few studies on this topic have
been conducted and their results are mixed.

Discussion and conclusions
The objective of this literature review was to investigate to what extent individual characteristics
(cognitive abilities, expertise, personality, cultural background) affect a person‟s susceptibility to
judgment and decision biases. This knowledge may for instance be used to develop strategies to
mitigate biased thinking of own personnel, or to deploy strategies to evoke biased thinking in
opponent parties.
Our findings indicate that each of the reviewed aspects can affect cognitive biases under
certain conditions (though with respect to culture the evidence is scarce). Note that the factors
found to reduce cognitive bias may in fact merely mitigate the behavioral effects rather than
preventing the bias from occurring at all.
Several types of cognitive ability, as well as employing deliberate information processing
may mitigate various cognitive biases (in particular: sunk cost, base rate, over confidence, belief,
framing). For anchoring and conjunction bias, the evidence is mixed. Emotional intelligence has
been found to reduce some biases. In general, cognitive ability does, by itself, not prevent biases
from occurring, but it may help to learn how to prevent or reduce its effects on behavior.
Expertise affects sensitivity to biases, because expertise largely determines thinking style
and contextual information processing. Depending on the problem context, expertise may
stimulate both deliberate thinking as well as heuristic thinking. Experts are more likely to select
the thinking style appropriate for the context, and are subsequently less prone to bias than non-
experts.
Personality affects the way people weigh and processes information, which in turn affects
the susceptibility to certain (but not all) biases. However, the relationship between personality
and susceptibility to cognitive bias is not fully clear. Predicting the occurrence and direction of
cognitive bias based on personality traits is therefore very hard.
Limitations of the present study
The results reviewed in this study have mostly been obtained in laboratory conditions involving
simplified tests that have specially been designed to induce cognitive biases. It is therefore not
clear how these results translate into real-life practice.
We used the dual-process heuristic-deliberate framework to summarize, unify and
understand our findings. However, not all reported effects fit into this framework. For instance,
effects reported on emotional priming are different from studies that demonstrate effects of
emotion, indicating a different underlying mechanism. Also, some of the effects upon judgment
and decision making seem to be working against each other. For example, people who need to
perform under pressure and time constraints are expected to apply heuristics which makes them
vulnerable for bias. However, at the same time, it is known that pressure induces a negative
mood which often elicits deliberate thinking strategies. And although deliberate thinking tends to
guard people against the risk of bias, this is not always the case. Exactly when it does, and when
not, is still difficult to quantify. This underlines the need to more fully understand the
mechanisms underlying cognitive bias.
Future research
The fact that the effects of cognitive biases on decision making are not completely fixed within
and between individuals suggests opportunities to apply this knowledge for selecting (bias free)
and training (de-biasing) personnel. Implicit association tests may be effective selection tools (De

Houwer, 2006). How these selection tests should be worked out for a given context (e.g.,
medical, financial, military, police) requires analysis of the characteristics and requirements of the
candidates, the nature of the task, and the contextual demands. The information and insights
resulting from the present study may also stimulate the development of new de-biasing
techniques to protect own personnel against (self- or externally- induced) cognitive bias in
concrete situations. Strategies that promote deeper information processing and thereby stimulate
the recruitment of the medial and lateral orbitofrontal cortex (regions associated with the
integration of affective and contextual information in decision making) may constrain decision
bias (Hughes & Zaki, 2015). Computer games that immerse the user into bias-invoking situations
that provide the experience to identify cognitive bias and to practice mitigation strategies, may
serve as effective debiasing tools. It has been shown that these types of “serious games” can
provide an effective method to train adults how to recognize and mitigate several cognitive
biases (confirmation, attribution and blind spot; Clegg et al., 2015; Dunbar et al., 2014;
Symborski et al., 2014). Models and decision support systems that provide tools and explicit rules
to guide decisions, may also help to counteract the adverse effects of judgement bias. This can
for instance be achieved by allowing the user to employ heuristics while warning for the likely
biases, and by anticipating the likely use of heuristics and providing information that offsets the
effects of such use (Larrick, 2004). In addition, the current findings may initiate innovative
methods to exploit biases to manipulate the behavior of opponents and other groups.
References
Aron, A. R. (2007). The neural basis of inhibition in cognitive control. The Neuroscientist, 13(3), 214-228.
doi:10.1177/1073858407299288
Bergman, O., Ellingsen, T., Johannesson, M., & Svensson, C. (2010). Anchoring and cognitive ability.
Economics Letters, 107(1), 66-68. doi:10.1016/j.econlet.2009.12.028
Bodenhausen, G. V., Gabriel, S., & Lineberger, M. (2000). Sadness and susceptibility to judgmental bias:
The case of anchoring. Psychological Science, 11(4), 320-323. doi:10.1111/1467-9280.00263
Brust-Renck, P. G., Reyna, V. F., Corbin, J. C., Royer, C. E., & Weldon, R. B. (2014). The role of
numeracy in risk communication. In H. Cho, T. Reimer, & K. A. McComas (Eds.), The SAGE
Handbook of Risk Communication (pp. 134-145). Thousand Oaks, CA, USA: Sage.
Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology,
42(1), 116-131. doi:10.1037/0022-3514.42.1.116
Caputo, A. (2014). Relevant information, personality traits and anchoring effect. International Journal of
Management and Decision Making, 13(1), 62-76. doi:10.1504/IJMDM.2014.058470
Cattell, R. B. (1987). Intelligence: Its structure, growth, and action. New York, USA: Elsevier Science Publishing
Comapny, Inc.
Chi, M. T., Glaser, R., & Farr, M. J. (1988). The nature of expertise. Hillsdale, NJ, USA: Lawrence Erlbaum
Assoiates, Inc.
Choi, I., & Nisbett, R. E. (2000). Cultural psychology of surprise: Holistic theories and recognition of
contradiction. Journal of Personality and Social Psychology, 79(6), 890-905. doi:10.1037/0022-
3514.79.6.890
Dane, E., & Pratt, M. G. (2007). Exploring intuition and its role in managerial decision making. Academy of
Management Review, 32(1), 33-54. doi:10.5465/AMR.2007.23463682
Davis, P. K., Kulick, J., & Egner, M. (2005). Implications of modern decision science for military decision-support
systems. Santa Monica, CA, USA: RAND Corporation.
De Houwer, J. (2006). What are implicit measures and why are we using them. In R. W. Wiers & A. W.
RStacy (Eds.), The handbook of implicit cognition and addiction (pp. 11-28). Thousand Oaks, CA: Sage
Publishers.
De Martino, B., Kumaran, D., Seymour, B., & Dolan, R. J. (2006). Frames, biases, and rational decision-
making in the human brain. Science, 313(5787), 684-687. doi:10.1126/science.1128356

Chronometric and dual-task considerations. The Quarterly Journal of Experimental Psychology, 59(6),
1070-1100. doi:10.1080/02724980543000123
De Neys, W. (2012). Bias and conflict: A case for logical intuitions. Perspectives on Psychological Science, 7(1),
28-38. doi:10.1177/1745691611429354
Dolcos, S., Hu, Y., Iordan, A., Moore, M., & Dolcos, F. (2015). Optimism and the brain: Trait optimism
mediates the protective role of the orbitofrontal cortex gray matter volume against anxiety. Social
Cognitive and Affective Neuroscience, 11(2), 263-271. doi:10.1093/scan/nsv106
anchoring. Judgment and Decision Making, 4(1), 41-50.
Epstein, S., Pacini, R., Denes-
Journal of Personality and Social Psychology, 71(2), 390-405.
doi:10.1037/0022-3514.71.2.390
Eroglu, C., & Croxton, K. L. (2010). Biases in judgmental adjustments of statistical forecasts: The role of
individual differences. International Journal of Forecasting, 26(1), 116-133.
doi:10.1016/j.ijforecast.2009.02.005
Evans, J. S. B. T. (1984). Heuristic and analytic processes in reasoning. British Journal of Psychology, 75(4),
451-468. doi:10.1111/j.2044-8295.1984.tb01915.x
Evans, J. S. B. T. (1989). Bias in human reasoning: Causes and consequences. London, UK: Lawrence Erlbaum
Associates, Inc.
Evans, J. S. B. T. (2006). The heuristic-analytic theory of reasoning: Extension and evaluation. Psychonomic
Bulletin & Review, 13(3), 378-395. doi:10.3758/BF03193858
Fallon, C. K., Panganiban, A. R., Wohleber, R., Matthews, G., Kustubayeva, A. M., & Roberts, R. (2014).
Emotional intelligence, cognitive ability and information search in tactical decision-making.
Personality and Individual Differences, 65(0), 24-29. doi:10.1016/j.paid.2014.01.029
Fawcett, T. W., Fallenstein, B., Higginson, A. D., Houston, A. I., Mallpress, D. E. W., Trimmer, P. C., &
McNamara, J. M. (2014). The evolution of decision rules in complex environments. Trends in
Cognitive Sciences, 18(3), 153-161. doi:10.1016/j.tics.2013.12.012
Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives, 19(4), 25-42.
doi:10.1257/089533005775196732
Fuchs, H. M., Steigenberger, N., & Lübcke, T. (2015).
decisions in action? Paper presented at the Proceedings of the 12th International Conference on
Information Systems for Crisis Response & Management (ISCRAM 2015), Kristiansand,
Denmark.
Furnham, A., & Boo, H. C. (2011). A literature review of the anchoring effect. The Journal of Socio-
Economics, 40(1), 35-42. doi:10.1016/j.socec.2010.10.008
Furnham, A., Boo, H. C., & McClelland, A. (2012). Individual differences and the susceptibility to the
influence of anchoring cues. Journal of Individual Differences, 33(2), 89-93. doi:10.1027/1614-
0001/a000076
Gamliel, E., Kreiner, H., & Garcia-Retamero, R. (2015). The moderating role of objective and subjective
numeracy in attribute framing. International Journal of Psychology, 51(2), 109-116.
doi:10.1002/ijop.12138
Ghazal, S., Cokely, E. T., & Garcia-Retamero, R. (2014). Predicting biases in very highly educated
samples: Numeracy and metacognition. Judgment and Decision Making, 9(1), 15-34.
Gigerenzer, G., & Gaissmaier, W. (2010). Heuristic decision making. Annual Review of Psychology, 62(1),
451-482. doi:10.1146/annurev-psych-120709-145346
Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic.
Psychological Review, 109(1), 75-90. doi:10.1037//0033-295X.109.1.75
Haerem, T., Kuvaas, B., Bakken, B. T., & Karlsen, T. (2011). Do military decision makers behave as
predicted by prospect theory? Journal of Behavioral Decision Making, 24(5), 482-497.
doi:10.1002/bdm.704
Haselton, M. G., Nettle, D., & Andrews, P. W. (2005). The evolution of cognitive bias. In D. M. Buss
(Ed.), The handbook of evolutionary psychology (pp. 724-746). Hoboken, NJ, USA: John Wiley & Sons
Inc.
Heath, C., & Tversky, A. (1991). Preference and belief: Ambiguity and competence in choice under
uncertainty. Journal of Risk and Uncertainty, 4(1), 5-28. doi:10.1007/BF00057884

Hogarth, R. M. (2010). Intuition: A challenge for psychological research on decision making. Psychological
Inquiry, 21(4), 338-353. doi:10.1080/1047840X.2010.520260
Jasper, J. D., Bhattacharya, C., Levin, I. P., Jones, L., & Bossard, E. (2013). Numeracy as a predictor of
adaptive risky decision making. Journal of Behavioral Decision Making, 26(2), 164-173.
doi:10.1002/bdm.1748
John, O. P., & Srivastava, S. (1999). The big five trait taxonomy: History, measurement, and theoretical
perspectives. In L. A. Pervin & O. P. John (Eds.), Handbook of personality: Theory and research (2nd
ed.) (Vol. 2, pp. 102-138). New York, NY: Guilford Press.
Kahneman, D. (2003). A perspective on judgment and choice: Mapping bounded rationality. American
Psychologist, 58(9), 697-720. doi:10.1037/0003-066X.58.9.697
Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive
judgment. In T. Gilovich, D. Griffin, & D. Kahneman (Eds.), Heuristics and biases: The psychology of
intuitive judgment (pp. 49-81). New York, NY, US: Cambridge University Press.
Kahneman, D., & Frederick, S. (2007). Frames and brains: Elicitation and control of response tendencies.
Trends in Cognitive Sciences, 11(2), 45-46. doi:10.1016/j.tics.2006.11.007
Kahneman, D., & Klein, G. (2009). Conditions for intuitive expertise: A failure to disagree. American
Psychologist, 64(6), 515-526. doi:10.1037/a0016755
Kim, S. H., & Hamann, S. (2007). Neural correlates of positive and negative emotion regulation. Journal of
Cognitive Neuroscience, 19(5), 776-798. doi:10.1162/jocn.2007.19.5.776
Klein, G. (2015). Naturalistic decision making suggestions for improving intuitive decision making. Journal
of Applied Research in Memory and Cognition, 4(3), 164-168. doi:10.1016/j.jarmac.2015.07.001
Klein, G. A. (1993). A recognition-primed decision (rpd) model of rapid decision making. In G. A. Klein,
J. Orasanu, R. Calderwood, & C. Zsambok (Eds.), Decision making in action: Models and methods (pp.
138-147). Norwood, NJ: Ablex Publishing Corporation.
Kurzban, R., Duckworth, A., Kable, J. W., & Myers, J. (2013). An opportunity cost model of subjective
effort and task performance. The Behavioral and brain sciences, 36(6), 661-679.
doi:10.1017/S0140525X12003196
Kuzmanovic, B., Jefferson, A., & Vogeley, K. (2015). Self-specific optimism bias in belief updating is
associated with high trait optimism. Journal of Behavioral Decision Making, 28(3), 281-293.
doi:10.1002/bdm.1849
Lu, Y. (2015). Is experiential-intuitive cognitive style more inclined to err on conjunction fallacy than
analytical-rational cognitive style? Frontiers in Psychology, 6(Article nr. 85), 1-8.
doi:10.3389/fpsyg.2015.00085
McElroy, T., & Dowd, K. (2007). Susceptibility to anchoring effects: How openness-to-experience
influences responses to anchoring cues. Judgment and Decision Making, 2(1), 48-53.
Miller, P. M., & Fagley, N. S. (1991). The effects of framing, problem variations, and providing rationale
on choice. Personality and Social Psychology Bulletin, 17(5), 517-522. doi:10.1177/0146167291175006
Nisbett, R. E., Peng, K., Choi, I., & Norenzayan, A. (2001). Culture and systems of thought: Holistic
versus analytic cognition. Psychological Review, 108(2), 291-310. doi:10.1037//0033-295X.108.2.291
Oechssler, J., Roider, A., & Schmitz, P. W. (2009). Cognitive abilities and behavioral biases. Journal of
Economic Behavior & Organization, 72(1), 147-152.
Ohlert, C. R., & Weißenberger, B. E. (2015). Beating the base-rate fallacy: An experimental approach on
the effectiveness of different information presentation formats. Journal of Management Control,
26(1), 51-80. doi:10.1007/s00187-015-0205-2
Peters, E., Västfjäll, D., Slovic, P., Mertz, C. K., Mazzocco, K., & Dickert, S. (2006). Numeracy and
decision making. Psychological Science, 17(5), 407-413. doi:10.1111/j.1467-9280.2006.01720.x
Pohl, R. F., Bender, M., & Lachmann, G. (2002). Hindsight bias around the world. Experimental Psychology,
49(4), 270-282. doi:10.1026//1618-3169.49.4.270
Raab, M., & Laborde, S. (2011). When to blink and when to think. Research Quarterly for Exercise and Sport,
82(1), 89-98. doi:10.1080/02701367.2011.10599725
Rolls, E. T. (2004). The functions of the orbitofrontal cortex. Brain and Cognition, 55(1), 11-29.
doi:10.1016/S0278-2626(03)00277-X
Rolls, E. T., & Grabenhorst, F. (2008). The orbitofrontal cortex and beyond: From affect to decision-
making. Progress in Neurobiology, 86(3), 216-244. doi:10.1016/j.pneurobio.2008.09.001
Sander, D., Grafman, J., & Zalla, T. (2003). The human amygdala: An evolved system for relevance
detection. Reviews in the Neurosciences, 14(4), 303-316. doi:10.1515/REVNEURO.2003.14.4.303

Santos, L. R., & Rosati, A. G. (2015). The evolutionary roots of human decision making. Annual Review of
Psychology, 66(1), 321-347. doi:10.1146/annurev-psych-010814-015310
Sauter, V. L. (1999). Intuitive decision-making. Communications of the ACM, 42(6), 109-115.
doi:10.1145/303849.303869
Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing optimism from neuroticism (and
trait anxiety, self-mastery, and self-esteem): A reevaluation of the life orientation test. Journal of
Personality and Social Psychology, 67(6), 1063-1078. doi:10.1037/0022-3514.67.6.1063
Scott, W. G., Christopher, J. R., & John, G., I. (1998). The impact of cultural values on escalation of
commitment. The International Journal of Organizational Analysis, 6(2), 165-176.
doi:10.1108/eb028883
Sharot, T. (2011). The optimism bias. Current Biology, 21(23), R941-R945. doi:10.1016/j.cub.2011.10.030
Shiloh, S., Salton, E., & Sharabi, D. (2002). Individual differences in rational and intuitive thinking styles
as predictors of heuristic responses and framing effects. Personality and Individual Differences, 32(3),
415-429. doi:10.1016/S0191-8869(01)00034-4
Sieck, W., & Yates, J. F. (1997). Exposition effects on decision making: Choice and confidence in choice.
Organizational Behavior and Human Decision Processes, 70(3), 207-219. doi:10.1006/obhd.1997.2706
Simon, A. F., Fagley, N. S., & Halleran, J. G. (2004). Decision framing: Moderating effects of individual
differences and cognitive processing. Journal of Behavioral Decision Making, 17(2), 77-93.
doi:10.1002/bdm.463
Simon, H. A. (1972). Theories of bounded rationality. In C. B. McGuire & R. Radner (Eds.), Decision and
organization (pp. 161-176). Amsterdam, The Netherlands: North-Holland Publishing Company.
Sinayev, A., & Peters, E. (2015). Cognitive reflection vs. Calculation in decision making. Front Psychol,
6(Article 532), 1-16. doi:10.3389/fpsyg.2015.00532
Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119(1), 3-22.
doi:10.1037/0033-2909.119.1.3
Söllner, A., Broeder, A., & Hilbig, B. E. (2013). Deliberation versus automaticity in decision making:
Which presentation format features facilitate automatic decision making? Judgment and Decision
Making, 8(3), 278-298.
Stanovich, K. E., & West, R. F. (2008). On the relative independence of thinking biases and cognitive
ability. Journal of Personality and Social Psychology, 94(4), 672-695. doi:10.1037/0022-3514.94.4.672
Strutton, D., & Carter, W. (2013). Reducing biases in cross-cultural top management team decision-
making processes. International Journal of Business Administration, 4(3), 1-13. doi:10.5430/ijba.v4n3p1
Svedholm-Häkkinen, A. M. (2015). Highly reflective reasoners show no signs of belief inhibition. Acta
Psychologica, 154(0), 69-76. doi:10.1016/j.actpsy.2014.11.008
Takemura, K. (1993). The effect of decision frame and decision justification on risky choice. Japanese
Psychological Research, 35(1), 36-40. doi:10.4992/psycholres1954.35.36
Teovanovic, P., Knezevic, G., & Stankov, L. (2015). Individual differences in cognitive biases: Evidence
against one-factor theory of rationality. Intelligence, 50, 75-86. doi:10.1016/j.intell.2015.02.008
Trippas, D., Pennycook, G., Verde, M. F., & Handley, S. J. (2015). Better but still biased: Analytic
cognitive style and belief bias. Thinking & Reasoning, online, 1-15.
doi:10.1080/13546783.2015.1016450
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science,
185(4157), 1124-1131. doi:10.1126/science.185.4157.1124
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science,
211(4481), 453-458. doi:10.1126/science.7455683
Verplanken, B., & Pieters, R. G. M. (1988). Individual differences in reverse hindsight bias: I never
thought something like chernobyl would happen. Did I? Journal of Behavioral Decision Making, 1(3),
131-147. doi:10.1002/bdm.3960010302
Welsh, M. B., Delfabbro, P. H., Burns, N. R., & Begg, S. H. (2014). Individual differences in anchoring:
Traits and experience. Learning and Individual Differences, 29, 131-140.
doi:10.1016/j.lindif.2013.01.002
Yama, H., Manktelow, K. I., Mercier, H., Henst, J.-B. V. d., Soo Do, K., Kawasaki, Y., & Adachi, K.
(2010). A cross-cultural study of hindsight bias and conditional probabilistic reasoning. Thinking
& Reasoning, 16(4), 346-371. doi:10.1080/13546783.2010.526786

Yip, J. A., & Côté, S. (2013). The emotionally intelligent decision maker: Emotion-understanding ability
reduces the effect of incidental anxiety on risk taking. Psychological Science, 24(1), 48-55.
doi:10.1177/0956797612450031
Yoder, C. Y., Mancha, R., & Agrawal, N. (2014). Culture-related factors affect sunk cost bias. Behavioral
Development Bulletin, 19(4), 105-118. doi:10.1037/h0101086
Zald, D. H. (2003). The human amygdala and the emotional evaluation of sensory stimuli. Brain Research
Reviews, 41(1), 88-123. doi:10.1016/S0165-0173(02)00248-5
Appendix A : Terminology
Anchoring: a tendency to make decisions biased toward previously presented information (the
"anchor").
Attribution error: the tendency to see behavior as a product of the actor's dispositions and to ignore
important situational determinants of the behavior.
Base rate neglect: a tendency to ignore statistical information (prior probabilities) and focus on
information only pertaining to a certain case.
Belief bias: a tendency to draw conclusions that agree with one‟s own beliefs - i.e., to evaluate the logical
strength of an argument on the basis of the believability of the conclusion.
Bias: errors in decisions that arise due to limitations of cognitive processing. Biases are often explained
using dual-process theory, which states that we have two cognitive systems, one that is fast and intuitive,
and another that is slow and deliberate. Biases occur when our fast system operates without the oversight
of the slow system.
Cognitive bias: a consistent deviation from an accurate perception or
judgmentjudgment of the world. Inferences about other people and situations may be drawn in an
illogical fashion. Individuals create their own "subjective social reality" from their perception of the input.
Cognitive reflection test: test to assess the ability or disposition to resist reporting the response that first
comes to mind.
Confirmation bias: a tendency to search for, interpret, focus on and remember information in a way that
confirms one's preconceptions.
Conjunction fallacy: a combination of conditions is considered more likely than a general condition.
Crystallized intelligence: the ability to use skills, knowledge, and experience.
Fluid intelligence: the capacity to think logically and solve problems in novel situations, independent of
acquired knowledge. It is the ability to analyze novel problems, identify patterns and relationships that
underpin these problems and the extrapolation of these using logic.
Framing: a bias in decision making depending on the way in the information is presented (e.g., whether
options are presented in terms of gains or loss).
Halo effect: a tendency to let the perceived valence of a single aspect dominate the overall judgment of a
person or situation.
Heuristics: simple decision rules (rules of thumb) that ignore part of the available information but work
well in a given environment.
Hindsight bias: the tendency to erroneously perceive events as inevitable or more likely once they have
occurred.

Imaginability bias: the tendency to use our imagination to make a subjective premonition of a future
event for which no memories of actual instances come to mind.
Need for cognition: an individual‟s propensity to enjoy and engage in thought.
Negativity bias: a tendency to weigh negative information more heavily than positive information.
Numeracy: the ability to process basic probability and numerical concepts.
Omission bias: the tendency to prefer harm caused by omissions over equal or lesser harm caused by
acts.
Optimism bias: the tendency to overestimate the likelihood of positive events (and to underestimate the
likelihood of negative events) happening to oneself, compared to others.
Outcome bias: a tendency to evaluate the quality of a decision based on its outcome rather than on what
factors led to the decision.
Over-confidence bias: an inclination of individuals to overestimate their own abilities to successfully
perform a particular task.
Sunk cost effect: a tendency to persist in an endeavor once an investment of money, effort, or time has
already been made.

p-ISSN: 1694-2620
e-ISSN: 1694-2639
Vol. 8 No. 5, pp. 18-26, ©IJHSS
Likert Scale Development: Construction and Evaluation of
Home Environment Scale
Mukhtar Ahmad Wani & Aejaz Masih
Department of Educational Studies
Jamia Millia Islamia (A Central University)
New Delhi, India- 110025
Abstract
The purpose of present paper is the construction and evaluation of home environment scale. An
attempt was made in this study to evaluate validity, reliability and to determine the appropriate
standards to interpret the results of home environment scale. The final form of the scale includes
ten sub-scales (dimensions) with seventy one (71) items/statements. The scale was validated for
its content by more than 20 experts from the related and allied fields. The construct validity was
calculated through factor analysis for items, and by correlating the score of each sub-scale with
the total score of the sale. t test for two independent samples was used (high group 27% and low
group27%) to find the discrimination validity for each sub-scale. The reliability for the whole
scale as well as for each sub-scale was calculated through Alpha coefficients. From the reliability
coefficients, it can be concluded that the scale is reliable as all the values are above .70. Finally,
the appropriate standards for interpretation of scores based on stanine procedures are
highlighted. However, interpreting the scores through stanine the investigator has to calculate z
values of raw scores after the data have been collected from all the sample participants. The
stanine procedure is the standardized technique for the categorization of the scores for
meaningful interpretation of Likert scale responses.
Keywords: Construction, Evaluation, Home Environment, Likert Scale, Reliability, Validity,
Stanine
Introduction
Various instruments have been developed for assessing the home environment in foreign as well
as in India. Some foreign scales are developed by Watson 1957, Williamson 1961, Moos 1974,
Bradley 1981 and some Indian scales by Prerna Mohete, Beena Shah, Reena Sharma, K. S. Mishra etc.
As we know the foreign tools are not culture fair and this makes them unsuitable for use in the
particular context. Another issue with these foreign scales (Moos) is that they lack reliability with
respect to adolescent sample (Boyd, C. P. et al, 1997). Other issue is their language of the items
in which they have been originally developed and their lack of measuring all the major
dimensions, that are universally acceptable on which home as a social institution is based.
Similarly, Indian scales measuring home environment are not without the pitfalls like that of
assessment of home environment for the age group of 20 to 50 years (Reena Sharma) and with
only five areas of home environment and others only devised for measuring home environment
for I-VIII class students (Prerna Mohete). While others for high school students (Beena Shah)
and others assessing only the psychological nature of the home (Mishra). Hence a complete tool
to assess the major parameters on which any institutions is based seems to be lacking in these

scales and this demands a new scale to be developed, which measures broad three parameters of
home environment which are interrelationship parameter, individual development parameter and
system organization parameter.
Objectives
1. To construct a Likert scale measuring the attitude of senior secondary school students
towards their home environment.
2. To evaluate the validity of home environment scale.
3. To evaluate the reliability of home environment scale.
4. To formulate appropriate standards to interpret the results of home environment scale.
Methodology
Descriptive statistical method was employed in the present study. The process of description as
employed in this research study goes beyond mere gathering and tabulation of data. It involves
an element of interpretation of the meaning or significance of what is described. Thus,
description is combined with comparison or contrast involving measurement, classification,
interpretation and evaluation.
Sample
The samples of the study is comprised of 106 senior secondary school students currently
enrolled in class 11th of different (Govt./Private) schools of South Kashmir of Jammu and
Kashmir during the year 2015. This study was delimited to students of class 11th. Secondly the
age range of the members of the population is 16-17 years.
Stages of tool construction
As with the tool construction, there is no total agreement of experts about the precise steps for
tool construction. Nevertheless, when constructing a tool, it is necessary to go through a number
of stages in order to ensure its good quality (Alderson, 1995). Although their needs a proper
procedure for tool construction. The graphical representation for the stages of tool construction
as depicts in figure 1.
Fig. 1: Stages of tool construction

Preparation of the preliminary draft
Having gone through the literature and previous tools as mentioned above in introduction, ten
dimensions based on three mentioned broad parameters were selected which are related to the
overall environment of home. Interrelation parameter includes dimension as Family integration,
Conflict, Self-expression and Social climate of the family. Individual development parameter
includes dimension as Guidance provided at home, Emotional support, Success orientation and
Independence and System organization parameter includes dimensions like Organization and
management of family and Control. Then the items associated with ten dimensions were selected
and each item was selected according to the nature of the dimension. For the selection of the
items, previous tools and studies related with home environment were consulted along with the
available literature. While selecting items, the nature of item measured the desired dimension of
home environment were taken into consideration. In this way the initial draft was prepared and
110 items (11 in each dimension) were included in the scale. Then, draft items were given to
experts from different universities who were well versed in the field and scale construction with a
request to review the statements and evaluate their content accuracy coverage, editorial quality
and suggestions for additions, deletion and modification of items. On the basis of 80% of
unanimously 30 items were deleted and 80 items were retained, which are reported below with
the number of items:
Table 1: Dimensions with number items in HES
Dimension No. of Items
A. Family integration 8
B. Social climate of the family 8
C. Guidance (Assistance) provided at home 8
D. Organization and management of the family 8
E. Conflict 8
F. Emotional Support 8
G. Success Orientation 8
H. Control 8
I. Self-expression 8
J. Independence 8
Try-out of the tool
The initial format with 80 items was administered on the sample of 106 higher secondary school
students from Kashmir (J&K). This is an attitude scale measuring the children‟s attitude towards
their family environment. The scale requires pupils to tell the favorableness or unfavorableness
with which a particular behavior has been observed by them in their homes, i.e., he/she is
requested to tell whether they are Strongly Agree, Agree, Undecided, Disagree and Strongly
Disagree respectively with the items in the scale.
Scoring of the Responses to HES Items
There are five options namely, “Strongly Agree”, “Agree, “Undecided, “Disagree” and “Strongly
Disagree”, for each statement of the scale. 5 marks were assigned to 'Strongly Agree', 4 marks to
'Agree', 3 marks to 'Undecided', 2 mark to „Disagree', and 1 marks to „Strongly Disagree'
responses and for negative items scoring is done in reverse order like 1,2,3,4,and 5 for Strongly
Agree, Agree, Undecided, Disagree and Strongly Agree respectively. Then the marks were
counted which were assigned to A, B, C, D, E, F, G, H, I and J dimension statements and then
they were added to get the total composite score on the particular dimension. Ten composite

scores for ten dimensions of the scale consists the children‟s attitude towards their home
environment.
Item Analysis
For assessing the item analysis bi-serial correlation was used to sharpen the scale. The responses
were collected and scored. Individual item score was correlated with the total score of the tool.
Item analysis was done for the 106 response sheets by using Item Vs Whole correlation method.
The sum of the scores on each dimension of value was calculated. Then „r‟ was calculated by
correlating the individual item and the corresponding component score. The correlation
coefficient at the 5% level of significance is 0.196 when the degree of freedom is 100 (Best, J. W.
2006). So the items having „r‟ values 0.196 and above were selected. It was found that out of the
total 80 items, there are 71 items which are having significant correlations with the total score of
the scale except 9 items which are having no significant correlation with the total score of the
tool. The correlation table is given below:
Table 2: r Values for HES
Item No. r value Item r value item r value
1 .323**
29 .298**
57 .410**
2 .510**
30 .258**
58 .302**
3 .200*
31 .219*
59 .380**
4 .590**
32 .214*
60 .543**
5 .527**
33 .349**
61 .262**
6 .574**
34 .218*
62 .431**
7 .493**
35 .106 63 .287**
8 .355**
36 .268**
64 .409**
9 .476**
37 .191*
65 .341**
10 .096 38 .289**
66 .281**
11 .372**
39 .119 67 .036
12 .559**
40 .235*
68 .436**
13 .465**
41 .331**
69 .260**
14 .629**
42 .365**
70 .315**
15 .327**
43 .230*
71 .423**
16 .299**
44 .389**
72 .309**
17 .412**
45 .334**
73 .301**
18 .238*
46 .130 74 .262**
19 .349**
47 .332**
75 .378**
20 .028 48 .226*
76 .249**
21 .487**
49 .377**
77 .343**
22 .407**
50 .435**
78 .153
23 .329**
51 .301**
79 .357**
24 .384**
52 .209*
80 .342**
25 .214*
53 .380**
Bold Italic items not selected
26 .212*
54 .322**
27 .245*
55 .141
28 .181 56 .205*
**
Correlation is Significant at 0.01
*
Correlation is Significant at 0.05

From the perusal of table 2 above, it is clearly reflected that some of the item (bold and
italics) were not having a significant correlation with the total scores of the scale and
hence were deleted. After the rejection of 9 unsuitable items from the scale, a total of 71
items in ten dimensions of home environment scale were selected which are shown
below in table 3 along with the possible range of scores:
Table 3: No. of items and range of scores in each dimension of HES
Evaluation of tool validity
When a test measures what it has been suposed to measure, is said to be valid. To determine the
validity of the test, the researchers tested face validity, construct validity and discrimination
validity.
Face validity or content validity
The content validity of the „Home Environment Scale‟ was tested by more than 20 experts. It is
evident from the assessment of experts that items of the test are directly related to the different
dimensions of Home Environment.
Construct validity
In order to find out the construct validity, the researcher calculated correlation between each
sub-scale‟s score and total score of the scale.
Table 4: Correlation between Each Dimension and Total Score
From the perusal of the table 4 above, it can be concluded that the correlation
coefficient of all dimensions (.712, .684, .609, .379, .431, .519, .513, .616, .550, and .515
respectively) are significant at .01. This indicates that all dimensions are related to
home environment and the tool has good construct validity.
Dimension No. of Items Possible range of scores
A Family integration 8 8-40
B Social climate of the family 7 7-35
C Guidance (Assistance) provided at home 7 7-35
D Organization and management of the family 7 7-35
E Conflict 6 6-30
F Emotional Support 7 7-35
G Success Orientation 7 7-35
H Control 7 7-35
I Self-expression 8 8-40
J Independence 7 7-35
Total 71 1-355
Dimension ‘r’ values Dimension ‘r’ values
A 0.712**
F 0.519**
B 0.684**
G 0.513**
C 0.609**
H 0.616**
D 0.379**
I 0.550**
E 0.431**
J 0.515**

Factor Analysis
However the scale was also subjected to exploratory factor analysis as the minimum
number of cases required for factor analysis is 100 (Kline, 1986). All the ten
components were retained as the eigenvalues are above 1. From the exploratory factor
analysis items loading .4 were selected and items below .4 were dropped from the scale.
From the factor analysis it can be concluded that all the items are measuring the same
construct.
Discrimination validity
To find out the discrimination validity of the items the researchers used item analysis
(difficulty level value and discrimination value). For knowing the level of discrimination
validity for each dimension of the scale, ‘t’ test for two independent samples was used
(high group 27% and low group 27%). Finally the discrimination validity of whole test
was also determined by using ‘t’ test. Discrimination validity for each domain and whole
test is given in the table below. It indicates that all ‘t’ values are significant at level 0.01
and the means of high group are also higher than low group which support the high
validity of home environment scale.
Table 5: t values for each dimension of the HES
**
Significant at 0.01 level
Reliability of the Home Environment Scale
The degree of consistency among test scores is called reliability. The values of reliability
coefficients (Cronbach alpha) for each sub-scale and for the whole scale are shown below:
Dimensions Group N Mean Std. D Df t value
A High
Low
28
28
28.32
14.71
1.44
1.38
54 36.03**
B High
Low
28
28
24.17
11.57
1.94
0.92
54 31.00**
C High
Low
28
28
24.17
12.50
1.18
1.47
54 32.58**
D High
Low
28
28
24.46
12.46
2.31
1.34
54 23.69**
E High
Low
28
28
19.85
9.32
2.64
1.46
54 18.41**
F High
Low
28
28
24.39
12.57
1.49
1.28
54 31.64**
G High
Low
28
28
24.32
13.21
1.46
1.47
54 28.25**
H High
Low
28
28
27.03
14.39
1.66
1.19
54 32.60**
I High
Low
28
28
24.85
13.53
1.64
1.83
54 24.27**
J High
Low
28
28
23.78
12.67
1.37
1.18
54 32.40**
TOTAL High
Low
28
28
245.39
126.64
16.60
12.93
54 29.70**

Table 6: Reliability coefficients of HES
Dimensions Alpha Dimensions Alpha
A .753 G .747
B .774 H .736
C .708 I .706
D .729 J .725
E .781 Total Reliability
of the Scale .895F .719
Final form
The final form of the scale along with serial no. of items for affirmative and negative statements
is presented in the below table 7.
Table 7: Item Presentation in the final form of HES:
Dimension Affirmative items Negative items
A Family integration 1, 2, 3, 4, 5 6, 7, 8
B Social climate of the family 10, 11, 12, 14, 15 9, 13
C Guidance (Assistance) provided at home 16, 18, 20, 21, 22 17, 18
D Organization and management of the family 23, 24, 26, 26, 29 27, 28
E Conflict 30, 31, 32 33, 34, 35
F Emotional Support 36, 37, 38, 39, 40 41, 42
G Success Orientation 43, 44, 46, 46, 47 48, 49
H Control 50, 51, 52, 54, 55 53, 56,
I Self-expression 57, 58, 59, 60, 61,
62
63, 64
J Independence 65, 66, 67, 69, 71 68, 70
Table 8: Scoring table for all dimensions of HES
Dimensions A B C D E F G H I J
Score
Mean
Stanine
The table 8 above represents the scoring for each dimension of the scale. The table is blank,
because the raw scores, mean and stanines will differ from sample to sample. The table is to be
filled after the data have been collected. The interpretation of the scores is done separately for
each dimension based on the Z value. Then the nine levels based on Z values ranging from -1.75

to +1.75 are to be divided. The stanine procedure is the standardized technique for the
categorization of the scores for meaningful interpretation.
Results
After following these steps to construct the scale and after analyzing the data from the first and
the last application by using adequate statistical methods, it has been concluded that:
 The study has produced a scale measuring the attitude of senior secondary school
students towards their home environment. This scale includes 71 items which measures
ten dimensions of home environment viz, Family integration, Social climate of the
family, Guidance (Assistance) provided at home, Organization and management of the
family, Conflict, Emotional Support, Success Orientation, Control, Self-expression and
Independence.
 The scale has been validated through content, construct and discrimination validity. The
content validity has been evaluated by experts, construct validity has been calculated by
Pearson‟s correlation. The correlation coefficients of all dimensions are (.712, .684, .609,
.379, .431, .519, .513, .616, .550, and .515 respectively) which are significant at .01 level.
This indicates that all dimensions are related to home environment and the scale has
good construct validity. The discrimination validity has been evaluated through „t‟ test
between high group 27% and low group 27%. All „t‟ values are significant at level 0.01
and the means of high group are also higher than low group which support the high
validity of HES.
 The reliability of the scale was evaluated by calculating Alpha Cronbach Coefficient. All
reliability coefficient values are above .70. Thus home environment scale is a reliable
scale whose reliability is 0.89 and the reliability for each dimension of HES is .75, .77, .70,
.72, .78, .71, .74, .73, .70, & .72 respectively.
 To categorize the students into different categories with respect to their attitude towards
home environment, the researchers used the stanine procedures.
References
Anastasi, A. (1987). Psychological Testing. New York: Macmillan Co.
Bandhana, & Sharma, D. (2012). A Study of Home Environment and Reasoning Ability among
Secondary School Students. Developing Country Studies, 2 (1), 73-80. Retrieved from www.iiste.org.
Best, J. W., & Kahn, J. V. (2010). Research in Education. New Delhi: PHI Learning Ltd.
Biel, A. (1986). Childrens Spatial Knowledge about Their Home Environment. Children's Environments
Quarterly, 3(4), 2-9.
Boyd, C. P. et al, (1997). The Family Environment Scale: Reliability and Normative Data for an
Adolescent Sample. Family Process36:369-373, retrieved from
https://www.researchgate.net/publication/51334514
Cohen, L., Manion, L., & Morrison, K. (2007). Research Methods in Education (6th ed.). New York:
Routledge.
Cunningham, J. B., & Aldrich, J. O. (2012). Using SPSS: An Interactive Hands-On Approach. New Delhi: Sage
Publications India Pvt Ltd.
Ebel, R. L., & Frisbie. (2004). Essentials of Educational Measurement. New Delhi: PHI Learning Pvt Ltd.
Ferguson, G. A., & Yashio, T. (1989). Statistical Analysis in Psychology and Education. New York: McGraw
Hill Book Co.
Field, A. (2014). Discoevring Statistics using IBM SPSS Statiatics (4th ed.). New Delhi:Sage Publications India
Pvt Ltd.
Garret, H. E. (1973). Statistics in Psychology and Education. Bombay Vakils: Feffer & Simons Pvt Ltd.
Guilford, J. P. (1987). Psychometric Methods. New York: McGraw Hill.
Guilford, J. P., & Beryamin, F. (1946). Fundamental Statistics in Psychology and Education. Singapore:
McGraw Hill Book Co.

Gupta, N., Joshi, R. & Pasbola, H. (2008). Effect of Home Environment upon Emotional Maturity
among female adolescents of Joint and Nuclear Family Structure. Behavioural Scientist, 9(2): 87-
92.
Kundu, G. (1975). A New Technique of Attitude Measurement. Calcutta: Annupurna Publishing House
McLeod, S. A. (2008). Likert Scale. Retrieved from www.simplypsychology.org/likertscale.htm
Mishra, K. S. (1989). Manual of Home Environment Inventory. Allahabad: Ankur Psychological
Agency, India.
Mishra, S., & Bamba, V. (2012). Impact of family environment on academic achievement of
secondary school students in science subject. International Journal of Research in Economics & Social
Sciences, 2(5), 42-49.
Mohanraj, R and Latha (2005). Perceived family environment in relation of adjustment and academic
achievement. Journal of the Indian Academy of Applied Psychology: 31(1-2): 18-23.
Thondike, R. M., & Tracy, C. T. (2011). Measurement and Evaluation in Psychology. New Delhi: PHI Learning
Pvt Ltd.
Wani, M. A., & Masih, A. (2015). Facilitating Learning by linking the two Environments: Interface
between Home and School through Technology. International Education Conference on Learning
Technologies in Education (pp. 334-342). New Delhi: Excel India Publishers.

p-ISSN: 1694-2620
e-ISSN: 1694-2639
Vol. 8 No. 5, pp. 27-36, ©IJHSS
Iqbal’s Response to Modern Western Thought: A Critical
Analysis
Dr. Mohammad Nayamat Ullah
Associate Professor
Department of Arabic
University of Chittagong, Bangladesh
Abdullah Al Masud
PhD Researcher
Dept. of Usuluddin and Comparative Religion
International Islamic University Malaysia (IIUM)
ABSTRACT
Muhammad Iqbal (1873-1938) is a prominent philosopher and great thinker in Indian Sub-
continent as well as a dominant figure in the literary history of the East. His thought and
literature are not simply for his countrymen or for the Muslim Ummah alone but for the whole
of humanity. He explores his distinctive thoughts on several issues related to Western concepts
and ideologies. Iqbal had made precious contribution to the reconstruction of political thoughts.
The main purpose of the study is to present Iqbal‟s distinctive thoughts and to evaluate the
merits and demerits of modern political thoughts. The analytical, descriptive and criticism
methods have been applied in conducting the research through comprehensive study of his
writings both in the form of prose and poetry in various books, articles, and conferences. It is
expected that the study would identify distinctive political thought by Iqbal. It also demonstrates
differences between modern thoughts and Iqbalic thoughts of politics.
Keywords: Iqbal, western thoughts, democracy, nationalism, secularism
1. INTRODUCTION
Iqbal was not only a great poet-philosopher of the East but was also among the profound,
renowned scholars and a brilliant political thinker in the twentieth century of the world. The
contemporary scholars acknowledge his influence in modern movement. His literature was a
source of inspiration for the hopeless Muslims who had subtle ability, potential and intellectual
capability. He endeavoured substantially for the sake of mankind. He can be best described as
the embodiment of “being a beacon of light in the dark night of the wilderness” (Khalil,
2000:182), a characteristic most needed for the Muslim Ummah. Iqbal was a „warner‟ who made a
clarion call to the oppressed of the world to rediscover the infinite sources of their creative
power and develops their faith. His message was not simply for his countrymen, nor for the
Muslim Ummah alone but for the whole of humanity (Munawwar: 1982). This is study
investigates modern western thoughts of Iqbal from Islamic perspective. It also attempts to
clarify Iqbal‟s position on the modern Western political philosophy. In particular, his views on
democracy, nationalism, secularism, capitalism, socialism and Liberalism are analyzed.

2. BIOGRAPHICAL SKETCH OF IQBAL AND HIS WORKS
Iqbal was born at Sialkot in the Punjab province of present Pakistan on 9 November, 1877. He
hailed from a family descended from Kashmiri Brahmins of Supra-caste1
, who had embraced
Islam in 17th Century (Mustansir: 2006). Iqbal refers to his Brahmin ancestry in several writings.
His father Shaikh Noor Mohammad was a skilled and enterprising businessman but a pious Sufi
saint and a God loving man (Munawwar: 1982). His mother Imam Bibi was a generous and kind
but also a deeply religious woman.
Iqbal completed his early education in Sialkot and migrated to Lahore, Pakistan in 1895. He
studied under a teacher named Maulvi Mir Hasan (1844-1929), who was a renowned scholar of
Islamic Studies, Persian and Arabic. His teacher was impressed by the inborn poetic talent of
Iqbal and encouraged him to continue his writing (Munawwar: 1982). On May 5, 1893, Iqbal was
admitted to the Scotch Mission College2
in Sialkot and successfully passed his intermediate
examination in1895. At the same year he got enrolled at B.A. in the Lahore Government College.
In 1897 he graduated and got admit in the same collage for Master‟s Program in the Department
of Philosophy. Iqbal‟s academic performance was excellent and continued brilliant record result
at all level and won many gold medals (Mir, 2006).
At Lahore Iqbal came under the influence of Sir Thomas Arnold, who was a profound professor
in Philosophy and had a echoing insight in Arabic and Islamic Studies. Thomas‟s role influenced
him to study the Western thoughts and instigated him into the modern methods of criticism. In
1899, he obtained the Master‟s degree and was appointed as a Macleod Reader in the Oriental
College, Lahore, whereas in March 1904, he joined as an assistance Professor in the Department
of Philosophy and English at the Government College, Lahore (Hafeez: 1971). As Iqbal was
advised by Thomas Arnold, he intended to go Europe for higher education in 1905. Iqbal
studied both in England and Germany. He studied in England at the Lincoln‟s Inn qualifying for
Bar (Hilal: 1995) as a barrister and at the Trinity College of Cambridge, where he studied with R.
A. Nicolson, a renowned orientalist and John M. E. McTaggart, idealist Metaphysician. At the
same time he went to Germany and got admitted in the University of Munich, where he was
awarded his PhD degree on his dissertation entitled “The Development of Metaphysics in
Persia” on November 4, 1907 (Hilal: 1995).
Iqbal returned from Europe to Lahore in August 1908 (Vahid: 1948) and began his professional
career as Professor, lawyer and poet. During his study in Europe, he developed his ideas and
thoughts, where “he was vivacious, gregarious, eager to engage himself in scholarly discussion
with his juniors and superiors and politely self-assertive” (Hilal: 1995:36). His stay in Europe
provided him with the opportunity to observe carefully the realities of Western societies. At his
time, the modern methods of criticism in true sense had not been developed in India. As Iqbal
employed the method of comparison between Islamic thoughts and Western ones, critical
analysis of then several philosophical aspects and so on, he was regarded as one of the pioneers
of the modern methods of criticism. He realized the shortcomings and dark aspects of modern
Western civilization, where chaos and crisis are visible in human life more than ever. That is why,
1
According to Vanina, in mediaeval Indian regions there existed big supra-caste bound by status, corporate
interests and common socio-ethical and moral values (2012: 139) which oversaws the political, economic and
juridical spheres of everyday existence (Gadgil & Guha, 1993: 113). This supra-caste presupposed a certain
hierarchy, and contradictions and conflicts within each of them (Vanina, 2012: 134). There are various castes;
however, in Indian societies who are peculiar in terms of beliefs, culture and social phenomenon which
explanation is not relevant to this article. As long time ago, Iqbal’s ancestors embraced Islam, generation after
generation the influence and traditions of castes gradually disappeared
2
This college was later renamed Murray Collage, which exists under that name.

he highly criticized Western political thoughts and civilization as well. Although, Iqbal spent his
time in law practice, cultural activities and literary works, he later took part in politics and
became an elected Legislative Council of the Punjab in 1926. In 1930, he was appointed
president of the annual session of the Muslim League and delivered a remarkable address which
led to a new idea of a separate Muslim state in Indian subcontinent. (Mujahid: 1986). He also
represented India at the Round Table Conference in London in 1931 and 1932 (Vahid: 1948).
Iqbal was a creative author in both poetry and prose a he left prolific intellectual works for the
next generation. He wrote 17 books in Persian, Urdu and English. Among his 12,000 verses of
poetry, about 7,000 verses are in Persian3
. Asrar-e-Khudi (Secrets of the Self) was the first
work of Iqbal in Persian composed in 1919, which deals with human personality or individual
self. Bang-i Dara (The Call of the Caravan Bell) was the first work in Urdu which was written in
1924 (Hilal: 1995). Iqbal also composed two books in English on the topic of “The Department of
Metaphysics in Persia”, which is a valuable contribution in the history of Muslim Philosophy, and
“The Reconstruction of Religious Thought in Islam”, where he attempted to restructure the history of
Islamic thought. Besides these, he wrote hundreds of articles, short essays, and letters as well as
issued several statements. All these publications would help researchers to understand the real
perception of Iqbal in various subjects. It is stated by Ali (1978:17) that, “… a leading exponent
of recent interpretations of Islam, Iqbal was in correspondence with people from all walks of life.
Religious leaders, journalists, politicians and scholars were his life-long correspondents with
whom he exchanged views on various subjects”. William O. Douglas, justice of the Supreme
Court of USA says: “that (Iqbal's) simple tomb is a place of pilgrimage for me. For Iqbal was a
man who belonged to all races; his concepts had universal appeal. He spoke to the conscience of
men of goodwill whatever their tongue, whatever their creed” (Hafeez, 1971).
Among the Muslim scholars of South Asia, Iqbal was the first who defined the concept of state
in Islam. In 1930, Iqbal delivered a famous presidential speech at Allahabad where he pointed
out a way for the political deadlock in the Indian subcontinent. He also stated: “I would like to
see the Punjab, North West Frontier Province, Sindh and Baluchistan amalgamated into a single
state of self- government within the British Empire or without the British Empire...I therefore,
demand the formation of a consolidated Muslim state in the best interests of India and Islam”
(Sing & Roy, 2011: 174). This statement proves that Iqbal wanted a Muslim state for the Muslims
in North- West of India where Muslim could develop and nurture their culture, religion,
language, literature besides other religious communities of India. For long time of the past, two
religions (Muslims and Hindus) were dominating. To the great extent peace, social security and
justice in India were dependent on their mutual understanding. Islamic ideologies of unity,
equity, humanity and justice played an important role through ages all over the globe in
alleviating any differences among human beings. Indeed, Iqbal emphasized this fact. Thus, he
also believed that the peace and prosperity of India would depend upon Muslims and Hindus
when the spirit of mutual understanding, love and unity in diversity will be maintained. However,
he sought a separate state for Muslim; to maintain the spiritual life of Muslim community; to
protect the Muslim culture from harmful influences4
and rapid invasion of un-Islamic elements.
In the beginning of 1938, Iqbal‟s health sharply declined and it took a serious turn for the worse
on March 25, 1938. Finally, he passed away at 5:15 in the morning on April 21, 1938.
Rabindronath Tagore sent a condolence message as soon as he heard the demise news of Allama
Iqbal that, “The death of Sir Mohammad Iqbal has created a void in literature which like a deep wound, will
3
2 http://www.poemhunter.com/allama-muhammad-iqbal/biography/ Retrieved on 20-07-2016, and
https://en.wikipedia.org/wiki/Muhammad_Iqbal Retrieved on 07-10-2016
4
Harmful influences or behaviours that are not allowed in Islam like polytheism, injustice and any unethical
behaviour such as free mixing, drug addiction, illegal sexual relation and so on.

take a long time to heal. India which occupies but a limited place in the world can ill afford to lose a poet whose
poetry has such appeal” (Maire, 1981: 4).
3. IQBAL ON MODERN WESTERN THOUGHTS
Muhammad Iqbal was not only a prominent scholar, writer, poet and philosopher but also he
was a political and social reformer of the Indian subcontinent. He was the first personality who
generated a unique idea of an independent Muslim state in Indian subcontinent. Through His
scholarly writings he more detailed his philosophical thoughts and ideas, especially his political
ideas. His political thoughts are deeply rooted in Islamic values which are derived from the Holy
Quran and the Sunnah. According to Iqbal, “Islam believes in a universal polity- a politico-
religious system or social polity- based on fundamentals that were revealed to the Prophet” (Sh.
Muhammad Ashraf, 1952:238) His attention was to protect the Muslim Ummah from the attack
of Western thought and to encourage and preserve their own cultural heritage and thoughts.
3.1 DEMOCRACY
Democracy is a political system of a state and government where sovereignty and power belong
5to the majority people and the government is elected by people‟s opinion through election.
That is why democracy as a political ideal was defined by Abraham Lincoln as “Democracy is a
government of the people, for the people, by the people”, while Alex Woolf (2007:4) described it
as “a system in which people decide matters together, or collectively”. Haneef Nadvi (1973:205)
explains that, “Democracy is composed of two Greek components; one means the people and
the other means government and law”. Technically, democracy is applied to a system of
government for the majority.
Iqbal was a democrat, who was actively involved with the politics of Indian subcontinent. In
1926, Iqbal participated in the election, where he was elected to the Punjab Legislative council.
L.S. May (1974:179) states that Iqbal was “an active member of this Council, speaking often on
land revenue and taxation, demanding greater justice in land assessment and even land revenue
deductions in hardship cases”. Although Iqbal preferred democracy, he has pointed out some
demerits in this regard. Iqbal 1opposed the Western democracy as it is a methodology rather
than an ideology or philosophy (Qureshi, 1983). He had his own observations and he suggested a
new term known as „spiritual democracy‟5
(Iqbal, 2011a:180), which is based on the Quranic
teaching. This spiritual democracy means, “A democracy where laws of God Almighty are
observed and enforced” (Munawwar, 2001:142). In Western democracy, sovereignty belongs to
the people. As opposed to this, sovereignty in Iqbal‟s “spiritual democracy” belongs to God
alone, which is an inseparable part in the Islamic world-view.
Iqbal was a lover of innovations. That‟s why when democracy evolved as a system of
government; he welcomed it as thought that the new system might be helpful in alleviating the
sufferings of the exploited and oppressed people (Abbas, 1997: xxiii). But he soon realized its
serious drawbacks and became a strong critic of the Western democratic form of government
where persons are counted, not weighted; it is a material fact but not of personality, which is a
spiritual fact. He says that, Democracy is a form of government in which People are counted but
their worth is not assessed (Iqbal, 1941:150). According to him, humanity needs three things
today “a spiritual interpretation of the universe, spiritual emancipation of mankind and basic
5
The Spiritual democracy is based on the principle of Tawhid, which speaks bout unity of thoughts, action and
value across humanity (Begum, 2001: 21). In case of spiritual democracy the Quran, Prophet’s Tradition, Ijma
and Qiyas are the Sources of the Islamic law, which would be interpreted according to the demand of ages. This
kind of democracy distinguishes man with significance that is sacred kind. ‘This democracy also recognizes
value of individual and rejects blood relationship as a basis of human unity’ (Iqbal, 2011a:116).

Vol 8 No 5 - October 2016

Recommended

Recommended

More Related Content

Similar to Vol 8 No 5 - October 2016

Similar to Vol 8 No 5 - October 2016 (14)

More from ijhssnet

More from ijhssnet (16)

Recently uploaded

Recently uploaded (20)

Vol 8 No 5 - October 2016