a measure for scientific thinking

7/28/2019 A Measure for scientific thinking

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The International Journal of Educational and Psychological Assessment

December 2010, Vol. 6(1)

2010 Time Taylor Academic JournalsISSN 2094-0734

71

A Measure for Scientific ThinkingCarlo Magno

De La Salle University, Manila, Philippines

AbstractThe present study further explains the nature of scientific thinking by exploring and

confirming its factors. Scientific thinking is defined as the thought processes that are used in

science, including the cognitive processes involved in theory generation, experiment design,

hypothesis testing, data interpretation, and scientific discovery (Dunbar, 1997). A scale was

constructed where the items reflect the potent characteristics of scientists as identified from

previous research. A total of 240 items were initially constructed referring to characteristics

of scientific thinking and it was administered to 528 college students taking a science

course. The underlying factors of the 240 items were identified using a principal

components analysis. Analysis of the scree plot showed that four factors can explain the

total variance of 60.94%. The grouping of the items was reviewed and they were identified

as practical inclination, analytical interest, intellectual independence, and discourse

assertiveness. These new set of factors were administered to a similar sample (N=1839) andthe factors were confirmed using Confirmatory Factor Analysis (CFA). The results showed

that the four factors of scientific thinking significantly increase with each other. The model

also had an adequate fit (RMS Standard Residual=.02, RMSEA=.06, PGI=.95, GFI=.95).

These domains can serve as pillars of scientific thinking and the results closed the gap in

the process of identifying further characteristics.

Keywords: Scientific thinking, practical inclination, analytical interest, intellectualindependence, and discourse assertiveness

IntroductionTheories, technology, models, and solutions to problems are produced

from the generated thoughts of scientists. The scientist as the person who works in

the sciences posses characteristics that is at some extent more prominent than in

other fields. The activities, processes, and traits exemplified by scientists are

explained much by characterizing their thoughts or how they think. This concept is

called scientific thinking. Scientific thinking involves systematically exploring the

environment, constructing models as a basis for understanding the evidence of it,

and revising those models as new evidence is generated. In this perspective, the

scientist is like a child in the process of learning who endeavors to make sense of

their environments by processing data and constructing mental models (Inhelder &

Piaget, 1958; Kuhn, 1989). Likewise, Dunbar (1999) identified that scientific

thinking involves thought processes that are used in science including the cognitiveprocess involved in theory generation, experiment design, hypothesis testing, data

interpretation, and scientific discovery (p. 730). Moreover, according to Gorman

(2006), these activities engaged by scientists as mentioned must be measured as a

set of traits and dispositions that have something to do with whether one becomes

interested in science as a career or related careers that require scientific thinking

(Gorman, 2008). There is a rich literature explaining the nature of scientific

thinking as dispositions (i.e., Bachtold & Werner, 1972; Busse & Mansfield, 1984;


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Feist, 1998; Helmreich, Spence, & Pred, 1988; Van Zelst & Kerr, 1954). There is a

need to study scientific thought in a psychological perspective (Feist, 2006). A

psychological perspective in the study of scientific thought can (1) provide a model

to understand and further explain expertise and exemplified skills, (2) derive

processes that help educators develop students with potential scientific-related

careers, (3) focus on skills that further strengthen the scientific thinking forpractitioners in science, and (4) integrate other psychological variables to create

theories to explain it.

In the educational setting, there is a greater call to develop students who can

think scientifically. This is usually carried out by training students with research

skills (Feuer, Towner, & Shavelson, 2002; McGinn & Roth, 1999; Pine &

Aschbacher, 2006). An education centered on building a scientific culture

promotes better research. This culture also establishes the practice of openness,

continuous reflection, and judgment. Brown, Collins, and Duguid (1989) described

this approach as cognitive-apprenticeship models where students are enculturated

into the practice of laboratory sciences.

The present study involves the construction of the concept of scientific

thinking derived from the conceptualizations of different authors [such as Kuhn

(1989), Dunbar (1999), Rosser (1999), Feist, 2006, and others]. In order to facilitate

building the construct of scientific thought, a measure was developed where the

items reflect, represent, and exemplify the processes involved in scientific thinking

based on cognitive, personality (disposition), social, and motivational perspectives.

The identified domains of scientific thinking through an exploratory factor analysis

were further confirmed in a measurement model to arrive at a final solution of a

typology for the construct. To ensure the functionality of the items for each

scientific thinking domain, the one-parameter partial credit Rasch model was used.

Conceptualization of Scientific ThinkingIt is important to present first the different perspectives formed about

scientific thinking. These perspectives provide the backbone of conceptualizing a

construct for scientific thinking as a model.

Scientific thinking was initially explained as a metaphor on how the child

thinks and learns (Inhelder & Piaget, 1958). Towards the 1980s this metaphorwas

still considered valid because of the body of research that demonstrated the

similarity of scientific generation of thoughts and how the child process information

(i.e., Brown & Kane, 1988; Carey, 1985). In this aspect, Karmiloff-Smith (1988)

described the similarity in terms of the child spontaneously discover how the world

works by building theories and not simply by observing facts like a scientist.

Towards the end of this decade, Kuhn (1989) started to argue about the analogymade between scientific thinking and child learning. A point of argument includes

the nave type of thinking that occurs in a child which made it different with the way

adults and scientists think. This difference was shown in Kuhns different studies

engaging different age groups to respond to situation that would allow researchers to

explain how they scientifically think (such as forming evidence, ability to generate

alternative theories, and ability generate counterarguments). Kuhn (1989) explained

the patterns of difference in terms of differentiation and coordination of theory and


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evidence where at some point in time (during childhood), both theory and evidence

are differentiated and lacks integration. This lack of integration eventually

developed into a coordinated form or logical complement (occurs in most adults).

The coordination of theory and evidence is described as a paramount of scientific

thinking. The difference in the pattern of thinking from child to adult in the

differentiation and coordination of theory and evidence is described as adevelopmental perspective. One implication of Kuhns findings show that scientific

thinking as coordination between theory and evidence is most likely evident for

adults specifically for the college sample. Kuhn further explained that the

coordination between theory and practice develops along a continuum of

metacognition. As an individual develops, their ability to be aware and take control

of their own learning also improves. This developing awareness and control for

ones learning explains much the skills in generating and evaluating evidence and

coordination with theory. The studies of Kuhn elaborate scientific thinking as

reflected in the attainment of control over the interaction of theories and evidence

in one's own thinking. The general skills that encompass scientific thinking

therefore involve: The scientistbeing able to consciously articulate a theory that he

or she accepts, knows what evidence does and could support it, and what evidence

does or would contradict it, and is able to justify why the coordination of available

theories and evidence has led him or her to accept that theory and reject others

purporting to account for the same phenomena (p. 674).

At the onset of the 1990s differentperspectives were proposed that further

characterized scientific thinking as a construct. These perspectives organize

different taxonomies of scientific thinking based on how it is studied.

One classification in conceptualizing scientific thinking is explaining the

approach taken by scientists in their work. Dunbar (1999) classified scientific

thinking in terms of the experimental approach, computational approach, and real

world investigations. The experimental approach is the orientation of scientific

thinking that involves problem solving, hypothesis testing, and concept formation.The nature of the computational model in scientific thinking involves building

computational models that is tested mathematically. The real world investigations

explain how scientific thinking is studied based on the attitudes and characteristics

reported by scientists. These three approaches explain how scientific thinking is

focused and studied. This classification was further refined in the succeeding

studies. These two approaches in looking at scientific thinking were further broken

down to four ways (Dunbar & Fugelsang, 2005):

(1) Ex vivo research, in which a scientist is taken out of her or his

laboratory and investigated using in vitro tasks. (2) In silico research,

involving computational simulation and modeling of the cognitive processes

underlying scientific thinking, including a diversity of approaches and case-studies (Dasgupta, 1994; Magnani, Nersessian, & Thagard, 1999; Shrager &

Langley, 1990). (3) Sub specie historiae research, focusing on detailed

historical accounts of scientific and technological problem-solving (Gooding

& Addis, 1993; Nersessian, 1984; Tweney, 1989; Tweney, Mears, &

Spitzmuller, 2005). (4) In magnetico research, using techniques like MRI to

study brain patterns during problem-solving, including potentially both in

vitro and in vivo research (Dunbar & Fugelsang, 2005) (p. 113).


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Another approach in scientific thinking was proposed by Rosser (1999)

where scientific thinking has typologies of domain-general thinking and domain

specific thinking. Domain general thinking is the aspects of scientific thinking that

can be used in a variety of contexts including designing and conducting

experiments, drawing inferences from data, and evaluating the validity of

conclusions (p.1000). This typology of scientific thinking involves strategies,heuristics, and methods that can be applied in different situations. On the other

hand, the domain-specific thinking is the kind of thinking that occurs in ordinary

situations and which overlaps or similar with scientific thinking. This kind of

thinking operates when ordinary people arrives and uses their own theories. This

form of thinking is also termed as nave thinking given the case (Kuhn, 1989).

Another classification involving scientific thinking is being an expert and

novice. Scientific thinking is a construct exhibited by expert that is defined as

someone who has spent many hours training or solving problems in a domain such

as geology, dance, linguistics, or auto repair (Ericsson & Lehman, 1996). Further

characterizations of experts are abstract thinking skills, problem-solving strategies,

storage and recall of a wide array of information, and ability to work flexibly within

a knowledge domain all exemplify what it means to be an expert (Bransford et al.,

2000). The scientist as an expert generates complex cognitive tasks by analyzing

underlying knowledge required by accurately interpreting concepts (Reif & Allen,

1992). These characteristics are exemplified in scientific thinking.

In another perspective, the characteristics of scientists as they engage in

producing their theories are described in scientific epistemology. Scientific

epistemology is manifested in the creation of scientific knowledge, instrumentation,

technical discourse, social relations, and visual displays used in scientific

publications (Kirby, 2003). The theoretical beliefs of scientists would affect their

evaluation and generation of evidence. The organizing influence of theoretical

concepts on forms of cognition range from simple categorization to complex

scientific thought (Alloy & Tabachnik, 1984; Fischhoff & Beyth-Marom, 1983;Holland et al., 1986; Murphy & Medin, 1985; Neisser, 1987). In another aspect,

Liang, Lee, and Tsai (2010) explains that the nature of science and epistemological

beliefs share commonalities. They pointed out that both are concerned with

certainty and developmental process of scientific knowledge and the process of

knowing science. In their study they conceptualized science epistemological beliefs

as source (scientific knowledge resides in authorities), certainty (evaluating scientific

belief as the right answer), development (scientific knowledge as evolving and

changing), and justification (role of experiments). They found in their study that

development and justification beliefs about science significantly increase the

variance in deep motive and deep strategy as approach to learning. This means that

the belief that science is evolving and its testability nature in experiments makesstudents engage in both deep motive strategy. The studies presented by Kirby

(2003) and Liang, Lee and Tsai (2010) contributes to the manifestations of scientific

thinking in the form of beliefs. Epistemological beliefs in science explain much how

scientists direct their thinking about the nature and source of science.

Other approaches in studying scientific thinking used personality and

interest frameworks for identifying characteristics inclined for scientists (Feist,

2006). The Big Five Factor Model was translated for scientists and examined


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which traits have positive and negative poles (Costa & McCrae, 1995). Hollands

hexagonal model of vocational interest was also applied among scientists, engineers,

and mathematicians and found specific map of traits that are geared in their

orientations. Some studies compared scientists and non-scientists and found

dominant characteristics of scientists like being extrovert, objective, mechanistic,

and rational (Arthur, 2001; Conway, 1988; Costa, McCrae, & Holland, 1984; Hart,1982; Johnson, Germer, Efran, & Overton, 1988; Simonton, 2000). Moreover, in a

metanalysis conducted by Feist (1998), he categorized scientific thinking into three

meaningful categories: Cognitive, motivational, and social. For the cognitive traits,

they found that scientists were more open to experience, flexible in thought, and

more creative. For the motivational aspect, scientists were more driven, ambitious,

and intrinsically motivated. For the social aspect, scientists were more dominant,

arrogant, hostile, self-confident, argumentative, and assertive.

The Present StudyPresently there is a host of scientific thinking characteristics that widely vary

according to each studys perspective (Bransford et al. 2000; Dunbar, 1999; Feist,

2006; Kirby, 2003; Rosser, 1999). The problem with some approaches is that the

characteristics identified are not specific to scientific thinking (e. g., Feist, 2006).

There is a need to make an explicit construction of the scientific thinking by

identifying its own typology and measure. The present study created a measure for

scientific thinking and the items reflect the different perspectives and frameworks

presented. The factor structure of the scientific thinking was uncovered and this was

further confirmed.

MethodParticipantsTwo set of participants were used in the present study. For the initial

sample, 528 college students were selected purposively from different universities in

the National Capital Region in the Philippines. The inclusion criteria includes

students who: (1) are currently in the proposal or data gathering phase of their

thesis because this allows them to have experience of actual scientific method, (2)

are working with their mentor in the study, (3) have written other research reports

prior to their thesis, and (3) are majors in any courses in the social sciences (i.e.,

psychology, behavioral science, educational psychology, science education, etc.).

This initial sample was used to identify the factors of scientific thinking.

Another sample composed of 1839 college students was selected using the

same selection criteria. This sample was used to confirm the factor structureproduced from the initial analysis. The description of the sample for this set is very

similar with the first.

InstrumentThe items of scientific thinking were based on the characterization of the

thought processes exemplified by scientist from the frameworks presented


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(coordination of evidence and theory, metacognition, experimental approach,

computational approach domain-general and domain specific thinking, expert and

novice, science epistemology, cognitive, motivational and social aspects). The items

are comprised of dispositions and the kind of thinking that happens in the idea

generation and work of scientists. There were 240 items generated based on the

descriptions of the frameworks identified. The items were reviewed by two expertsin cognitive psychology and they determined whether the items are relevant.

Relevance of an item is judged whether they are within the frameworks provided.

Items that are rated by two reviewers as not relevant were removed or revised for

clarity. A 4-point Lickert scale was used for each item (4=strongly agree, 3=agree,

2=disagree, 1=strongly disagree).

ProcedureThe scale with 240 items was first pretested to 528 students who are

currently doing their thesis. Examiners were trained how to administer the scale to

ensure consistency of procedures across different administration. Students were

instructed to answer as honestly as possible and there are no right and wrong

answers. Principal components analysis was used for the initial data to uncover the

factor structure of the scientific thinking. The number of factors was determined by

examining the eigenvalues (higher than 1.0 in a scree plot). The items with factor

loadings above .4 were accepted for the next form of the scale. The items were

regrouped according to the factor where they highly load.

The items in the previous analysis were again administered to another set of

1839 respondents (with similar characteristics as the first) and the factors structure

was tested using Confirmatory Factor Analysis (CFA). The CFA can show whether

each factor is significant for the measured construct. The overall fit of the

measurement model was also tested.

Item analysis was conducted for each factor by the estimation of Rasch itemand person fit scores. The Rasch model ensures that each factor is unidimensional

and do not contain sources of variations. The software WINSTEPS was used for

the Rasch model item analysis. The analysis can determine (a) if the difficulty levels

of the items reflect the full range of respondents' trait levels, and (b) how well the 4-

point scale captures the distinctions between each category of agreement. This

software package begins with provisional central estimates of item difficulty and

person ability parameters, compares expected responses based on these estimates

to the data, constructs new parameter estimates using maximum likelihood

estimation, and then reiterates the analysis until the change between successive

iterations is small enough to satisfy a preselected criterion value (Linacre, 2006).

Although the estimates are called difficulty which refers to correct responses (suchas ability measures), the Rasch model is applicable for non-cognitive measures

where difficulty would refer to extreme low scores in a measure.

ResultsA principal components analysis was initially conducted among 240 items of

the scientific thinking scale. An examination of the scree plot showed that four


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factors can be produced because the eigenvalues are close from the fourth factor to

the fifth. The four factors extracted accounts for 60.94% of the total variance. The

remaining factors extracted were not considered because the same total variances

were produced and that were also low. The varimax rotation method was used

because it accounts for larger factor loadings under each of the four factors

extracted. The items with factor loadings below .40 were removed and 123 itemswere retained.

The 123 items were classified under each of the new factor solution. The

names of the factors were generated based on the common content of the items

that loaded together. The first factor contain items reflecting the real world

experience (Dunbar, 1999), domain specific thinking (Rosser, 1999), and scientific

thinking as everyday thinking (Kuhn, 1989) and it was labeled as practical

inclination with 24 items (e. g., I can improvise tools to fix objects). The second

factor extracted contain items indicating strategies, heuristics, methods (Kuhn,

1989), computational approach (Dunbar, 1999), and justification science

epistemology (Liang, Lee, & Tsai, 2010) used to generate scientific knowledge and

this was labeled as analytical interest with 31 items (e. g., I enjoy following step-

by-step procedures in completing tasks.). The third factor contain items that show

expert thinking (Ericsson & Lehman, 1996), intrinsic motivation, autonomy (Feist,

2006), experimental approach (Dunbar, 1999), and certainty science epistemic

belief (Liang, Lee, & Tsai, 2010)which was labeled as intellectual independence

with 35 items (e. g., I do not let others influence me in my work without hard

evidence). The last factor was dominated by items about the social aspect of

scientific thinking as proposed by Feist (2006). The items are about being

dominant, arrogant, hostile, self-confident, argumentative, and assertive. This factor

was named as discourse assertiveness with 33 items (e. g., I engage when

someone argues with me).

The four factors of the scientific thinking scale were confirmed in another

sample (N=1839) with similar characteristics as the first set. To prove the evidenceof a four factor solution, it should show a better fit as compared to a one-factor

model, two-factor model, or a three-factor model by comparing its fit indices.

In a one factor model, one latent variable for scientific thinking was tested

where all 123 items were used as indicators. In the two-factor model, practical

inclination and analytical interest were combined in one latent variable (r=.89**)

and intellectual independence and discourse assertiveness (r=.86**) in another. In

the three factor model, discourse assertiveness is one latent variable, another for

intellectual independence, and one for practical inclination and analytical interest

together. The combination of factors was selected based on a zero-order correlation

of the four factors (see Table 1). The last model tested is the four-factor model that

was generated from the previous principal components analysis. The best fittingmodel is determined by comparing the fit indices of the models produced using

change in chi-square (2), Root Means Square Standardized Residual (RMS), Root

Mean Square Error Approximation (RMSEA), Akaike Information Criterion

(AIC), Schwartz Bayesian Criterion (SBC), Browne-Cudeck Cross Validation

(BCC). These indices should show low values to that indicates better fit.


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Table 1Zero Order C orrelation of the Four Factors of Scientific Thinking

Factors M SD (1) (2) (3) (4)

(1) Practical Inclination 3.04 0.62 ---

(2) Analytical Interest 2.99 0.62 .89** ---(3) Intellectual

Independence

3.06 0.61 .88** .89** ---

(4) Discourse

Assertiveness

3.06 0.61 .83** .81** .86** ---

**p


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calibrations for practical inclination are, -9.12, 1.23, 2.74, and 5.16, for analytical

interest, -12.51, -2.32, -.71, and 1.34, for intellectual independence, -7.60, .93, 2.24,

and 4.43, and for discourse assertiveness, -7.74, 1.16, 2.15, and 4.44. All average

step functions are increasing monotonically indicating that a 4-point scale for each

factor attained scale ordering where there is a high probability of observance of

certain scale categories.To determine if the items under each factor has a unidimensional structure,

item fit mean square (MNSQ) was computed. MNSQ INFIT values within 1.3 and

less than 0.7 are acceptable. High values of item MNSQ indicate a lack of

construct homogeneity with other items in a scale, whereas low values indicate

redundancy with other items (Linacre & Wright 1998). Four Rasch analyses were

conducted separately for each factor. For practical inclination, item 3 (I am

bothered when structures are not built properly) had an infit of 3.05 which is above

1.3 indicating that this item lack construct homogeneity with other items. All other

items for practical inclination fitted the Rasch Model. For analytical interest, item

55 (I like to engage in activities that involves critical thinking) with an infit of 2.05

also lacks construct homogeneity. For intellectual independence, three items also

lacked construct homogeneity (I evaluate the outcomes of science and technology; I

support conclusions that I draw from accurate evidence; Conclusions are valid

when they are based on scientific observations). For discourse assertiveness, two

items lack construct homogeneity (I engage when someone argues with me; I am

comfortable facing others whom I know do not like my ideas). These items do not

share a similarity as with the pool of items in the factor. These items can either be

removed or revised.

DiscussionThe study was able to construct a four factor model of scientific thinking

that is composed of four domains: Practical Inclinations, analytical interest,intellectual independence, and discourse assertiveness. These four domains are

empirically derived through factor analysis and further confirmed having the best fit

for the observations. These four constructs also significantly increase with each

other indicating convergent validity (correlations) and high internal consistencies

(Cronbachs alpha). The Rasch model also showed that very few items in each

factor lack construct homogeneity which can still be improved and none of the

items were redundant.

Having derived the four domains explains scientific thinking better with its

exclusive characteristics. Previous studies (i.e., Feist, 2006) identified scientific

thinking by using other measures (mostly personality) and concluding that certain

variables are present for a sample of scientists. These characteristics then made ittypical of scientific thinking. However, the present study generated specific

constructs that are scientific thinking in nature. Previous studies also explain

scientific thinking by classifying the type of thinking for a given approach (i.e.,

Dunbar, 1999; Rosser, 1999) which describes more of the activities engaged by

scientists and not patterns of scientific thinking. However, the present study arrived

with specific labels that exemplify and describe the thinking of scientists.


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The four specific domains in the present study are practical inclination,

analytical interest, intellectual independence, and discourse assertiveness. A model

that is comprised of these four factors is an attempt to unite different perspectives

in characterizing scientific thinking. This derived and confirmed model indicates

that these four domains significantly increase with each other. It follows that if an

individual have a strong tendency to manifest one domain, they would likelyexemplify in the other domains. For example, an individual who exemplify the use

of mathematical heuristics in solving problems encountered in daily phenomenon

would (practical inclination) most likely be strategic in thinking for other tasks

(analytical interest). In another case, individuals who show expertise and strong

background on a specialized knowledge (intellectual independence) have a

tendency to be high in asserting their ideas during conversations and debates.

Practical inclination was derived from items reflecting the real world

experience (Dunbar, 1999), domain specific thinking (Rosser, 1999), and scientific

thinking as everyday thinking (Kuhn, 1989). This involves items that typify scientific

thinking in ordinary living such as everyday reasoning, and math applications.

Everyday reasoning may involve encountering problems that needs solution and the

kind of thinking required is backed up by theory and evidence. There may also be

scenarios that require the use of mathematics such as purchasing and budgeting.

This concept is consistent to Sternberg, Castejn, Hautamki, and Grigorenkos

(2001) practical intelligence where the individual thinks of adapting to, shaping of,

and selecting of real-world environments. People high in practical intelligence are

strong in using, implementing, and applying ideas and products. This kind of

thinking is represented in everyday parlance by expressions such as I am able to

apply theories in dealing with household chores or I can predict the durability of

materials by looking at its specific characteristics.

Analytical interest is a range of thinking that involves mental strategies,

heuristics, methods (Kuhn, 1989), computational approach (Dunbar, 1999), and

justification science epistemology (Liang, Lee, & Tsai, 2010) used to generatescientific knowledge. The goal in analytical interest is to discover knowledge, and

such thinking deals with concepts, hypotheses and theories, and abstractions. The

scientific method is emphasized in this kind of thinking. The thinking involved in

the use of scientific method is linear and hierarchical and the individual is

independent of personal and cultural value system so that results can be repeated

by anyone. For example, Santi and Higgins (2005) explained that geologists or

hydrogeologists can gain the technical knowledge and skills they need through

experience and self-education. Part of this skill is analytical interest. Analytical

thinking skills can be taught through a variety of exercises that enhance the geology

curriculum without adding new topics, including in-class discussion questions,

homework and laboratory problems, and add-ons to mapping and semesterprojects. Dunn (1982) described analytical thinkers to be linear sequential and

logical. Analytic individuals capture and remember information best when it is

presented in a step-by-step, methodical, sequential, little by little, leading toward an

understanding of the concept or lessons presented. Analytics are usually persistent

because they follow directions to complete a task and do things sequentially.

They move from the beginning of a task to the end in a series of small, focused and

goal-oriented steps.Analytical interest is manifested in statements such as I believe


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that the scientific method is the best way in acquiring accurate data or I believe

that processes needs to be systematic to make it work efficiently.

Intellectual independence includes expertise (Ericsson & Lehman, 1996),

intrinsic motivation, autonomy (Feist, 2006), experimental approach (Dunbar,

1999), and certainty science epistemic belief (Liang, Lee, & Tsai, 2010).

Intellectual independence can be defined as the ability of a learner to makeknowledge claims independent of the traditional authorities of the teacher and

textbook (Oliver & Nichols, 2001). It involves awareness that knowledge could be

created as a result of the examination of empirical evidence that is independent of

the traditional authority. Intellectual independence is manifested by a person who is

an investigator, who seeking by means of his own efforts to find out what is truth-

not a mere imitator or verifier of the results obtained by others. The conclusions

reached must be deductions from the evidence observed, not statements

memorized from a text or learned from a teacher. The laws and principles derived

must be inferences warranted by the conclusions from the evidence. In describing

an intellectual independent student, they should learn to trust his own powers and

grow strong in the assurance of first-hand knowledge. They test and observe for

themselves, and receive nothing upon mere authority. No other exercise so

develops the freedom and confidence of independent thinking (Poteat, 1999).

Poteat (1999) dissuaded teaching that would encourage students to accept assertions

"upon mere authority." Examples of statements for intellectual independence are I

explore my own ideas and provide evidence for its truthfulness or I dislike

teachers that discourage students from arguing their ideas with others.

Discourse assertiveness includes the thinking that characterizes dominance,

arrogance, hostility, self-confidence, argumentative, and assertive. Paterson (2000)

defined assertiveness as the ability to express ones needs, wants, and feelings

directly and honestly and to see the needs of others as equally important. It is the

ability to say "no" or "yes," as appropriate, to requests-to express positive/negative

feelings and conveniently initiate, sustain or terminate a social discuss (Lazarus,1973). Examples of statements for discourse assertiveness include I engage when

someone argues with me or I can express my opinion even when others may not

agree with me.

The four domains derived in the study form the pillars of scientific thinking.

Having an integrated typology of scientific thinking forms a foundation for a unified

conceptualization as a variable. In the present study, scientific thinking is described

as (1) composed of four domains (practical inclination, analytical interest,

intellectual independence, and discourse assertiveness), (2) the identified domains

are interrelated. Having an initial construct of scientific thinking allows other

researchers to use and further test the construct to strengthen its generalizability.

The insight into scientific thinking advance understanding in science learning. Sincethe dispositions of scientific thinking are identified, these can be included in the set

of expectations for students who major in the sciences or undergoing a science

curriculum. A science curriculum that is geared in developing students who would

be engaged in scientific work can use the derived model of scientific thinking as

basis of the outcome or students produced in the program. Research in the past 30

years have emphasized the development of scientific thinking


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across science curriculum, however, despite efforts in the Philippines, students are

reported to have low scores (i.e., TIMSS). One possible intervention is to clarify

and refocus the competencies, skills, and traits that students need to develop to

succeed in science. The derived model of scientific thinking can function as a set of

traits that provide benchmark for students to develop.

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Appendix AFour-Factor Measurement Mo del of Critical Thinking

Item 2

Item 1

Item 3

Item 4

Item 5

Item 6

Item 7

Item 8

Item 9

Item 10

Item 11

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Item 14

Item 15

Item 12

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Item 23

Practical

Inclination

Analytical

Interest

Discourse

Assertiveness

Item 25

Item 27

Item 28

Item 29

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Item 31

Item 32

Item 33

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Item 35

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Item 38

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Item 55 Item 91

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Item 92

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Item 56

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Item 90

Intellectual

Independence

.90**

.92**

.87**

.93**

.85**

.89**

.60

.58

.58

.60

.62

.60

.61

.59

.59

.63

.59

.58

.59

.62

.65

.62

.60

.61

.58

.62

.61

.60

.57

.61

.63

.61

.62

.61

.66

.61

.66

.61

.60

.61

.56

.63

.60

.60

.56

.60

.60

.59

.64

.63

.62

.61

.62

.61

.61

.65

.62

.64

.61

.63

.67

.62

.63

.59

.64

.63

.58

.60

.60

.63

.61

.61

.63

.64

.60

.58

.57

.60

.64

.61

.60

.59

.44

.60

.58

.56

.59

.59

.59

.59

.62

.61

.59

.57

.59

.59

.57

.57

.59

.58

.57

.58

.57

.60

.60

.57

.60

.62

.62

.62

.59

.65

.60

.62

.63

.60

.60

.58

.57

.57

.57

.58

.64

.60

.60

.59

.63

.62

.61


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About the AuthorDr. Carlo Magno is presently a faculty of the Counseling and Educational

Psychology Department of the De La Salle University, 2401 Taft Ave, Manila,

Philippines. This study was funded by the University Research and Coordination

Office of the said university. Correspondence can be addressed to the author [email protected]

a measure for scientific thinking

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