A survey of scientific literacy to provide a foundation for designing science communication in Japan

Abstract

There are various definitions and survey methods for scientific literacy. Taking into consideration the contemporary significance of scientific literacy, we have defined it with an emphasis on its social aspects. To acquire the insights needed to design a form of science communication that will enhance the scientific literacy of each individual, we conducted a large-scale random survey within Japan of individuals older than 18 years, using a printed questionnaire. The data thus acquired were analyzed using factor analysis and cluster analysis to create a 3-factor/4-cluster model of people’s interest and attitude toward science, technology and society and their resulting tendencies. Differences were found among the four clusters in terms of the three factors: scientific factor, social factor, and science-appreciating factor. We propose a plan for designing a form of science communication that is appropriate to this current status of scientific literacy in Japan.

Keywords

cluster analysis public understanding of science questionnaire science communication scientific literacy

1. Introduction

What is “scientific literacy,” what is its current status, and what should we do about it? Various studies have been undertaken to define and measure scientific literacy (AAAS, 1991; NSF, 2002; Okamoto et al., 2002; Norris and Philips, 2003; European Commission, 2005; Bauer et al., 2007). Scientific literacy surveys currently conducted around the world are primarily focused on theoretically constructed true/false questions related to scientific knowledge (Miller, 1998). However, the definition of scientific literacy has been changing with the times, as we will discuss (Miller, 2001; Roberts, 2007; Bauer, 2009), and it is necessary for us to develop an evaluation method that corresponds with this change. For example, Rundgren et al. (2010) selected scientific terms used in the media to create a true/false questionnaire which they use to measure the civic scientific literacy of their students. Roberts (2007) perceives science education as education directed at raising the scientific literacy of all citizens, not just of students aspiring to become scientists or technicians. Our premise is that adults have different levels of scientific literacy, and there is a need to enhance the scientific literacy of all Japanese people through communication among people with different types of literacy. From this standpoint, we propose a new method of evaluation and set forth ideas for a scientific literacy improvement program.

Science and technology encompass issues that cannot be handled solely by the scientific community. Examples include mad cow disease (Bovine Spongiform Encephalopathy), genetically modified organisms, artificial enhancements of the human body and so on. If we are to accept the concepts of “trans-science” (Weinberg, 1972), “post-normal science” (Ravetz, 1999) and “mode 2” (Gibbons et al., 1994), it is necessary to clarify the complex scientific literacy that correlates with the interests, attitudes and activities of people outside the scientific community. Furthermore, we need to design a science communication that is based on such clarification so that we can promote scientific literacy that is appropriate to social conditions.

In Japan as well, as we enter the 2000s, it has been pointed out that we need to shift from a unilateral requisition of “public understanding of science” to dialog through “science communication” (Watanabe and Imai, 2003; Watanabe, 2008). In line with this, science communication education and science communication activities have been introduced in universities and elsewhere (Sugiyama, 2008), but at the same time it has not been sufficiently clarified as to what and how we should be communicating or to whom. Given that it is essential in communication to know whom you are trying to communicate with, we conducted a survey to find out the current state of scientific literacy in Japan with the intent of using the results to design science communication as social education.

2. The study context: Definitions and research on scientific literacy

What elements have been used to date to define scientific literacy? Norris and Philips (2003) compiled a report on the historical transition in definitions of scientific literacy, arguing that literacy in the fundamental sense is based upon the same epistemology that underlies science, while Bauer et al. (2007) divided the history of public understanding of science into the three periods of “science literacy” (1960–80s), “public understanding” (1985–90s), and “science and society” (1990s–present). Despite PUS adding a social facet to the concept of scientific literacy, there was criticism that these efforts were one-sided, a “deficit model” that attempted to inject scientific knowledge and value into people (Bauer et al., 2007). In the current definition of scientific literacy, social trust among stakeholders and other such social aspects are considered more important than just the knowledge and interest of individuals. Also, importance has come to be placed on multi-directional communication in which a variety of people representing various standpoints participate in discussion, rather than one-sided communication directed from the scientific community to the general public.

The definition of scientific literacy has been changing over time, and also varies with the purpose of the survey. The Organisation for Economic Co-operation and Development (OECD) Program for International Student Assessment (PISA) is intended to provide data with which to compare the levels of learning of 15-year-old students; scientific literacy is assessed by the degree to which a student “engages in science-related issues and with the ideas of science, as a reflective citizen,” as well as by the student’s scientific knowledge (OECD, 2007: 12). The American Association for the Advancement of Science (AAAS) has published a report, “Science for All Americans,” defining scientific literacy as the levels of scientific understanding to be achieved by all American children, from kindergarten to high school, that should be targeted in planning curriculums with the aim of building an economically strong democratic nation (AAAS, 1991). Using this as a reference, a similar report, “Science and Technology for All Japanese” (Kitahara, 2008) was prepared that sets forth a scientific literacy blueprint¹ for Japan. As all of this indicates, scientific literacy today is defined in terms of diverse facets that encompass more than just knowledge.

Recent large-scale scientific literacy surveys include the “Europeans, Science and Technology” (2005) survey by the European Commission and the “Science and Engineering Indicators” (2002) survey by the National Science Foundation (NSF) (Table 1). The objective of these surveys is to make comparisons among different nations and reflect the results in science and technology policies. Survey questions are primarily related to promises and reservations concerning science and technology and true/false quizzes on basic scientific knowledge (Miller, 1998; Roberts, 2007). International comparisons are based on knowledge-testing statements, such as, “The center of the Earth is very hot” (see Table 2 for other quiz true/false statements). The quizzes are based on the theoretical background of the scientific literacy model posited by Shen (1975) and Miller (1998).

Table 1.

Comparison of major surveys on science and technology interest and attitude.

	Present survey	Survey of Public Attitudes Toward and Understanding of Science & Technology	Eurobarometer, Europeans, Science and Technology	PISA	Science and the Public
Country	Japan	Japan	EU¹	57 countries/areas	UK
Organization	Present project	NISTEP	European Commission	OECD	OST/Wellcome Trust
Year	2007–08	2001	2005	2006	2000
Subjects	Age: 18–69	Age: 18–69	Age: ≥ 15	Age: 15 (students)	Age: ≥ 16
Answered	1,286	2,146	32,897	Approx. 400,000	1,839
Object	To clarify clusters for science communications	To clarify interests and literacy on scientific literacy for policy decision	To clarify interests and literacy on scientific literacy for policy decision and international comparison	To clarify student level for nurturing international talents	To clarify clusters for science communications
Method	Post mailed questionnaire	Face-to-face interview	Telephone interview	Paper test at school	Face-to-face interview
Number of questions²	90 (7)	115 (9)	240 (14)	4 clusters³	121 (11)
Contents	Aimed for clustering by social questions/literacy structure	Internationally comparable scientific knowledge/technique problems	Scientific knowledge/technique questions, various social questions for international comparison purpose	Questions about thoughts including free writing (open-ended question)	Questions on science and social consciousness
Knowledge quiz	Yes	Yes	Yes	No	No
Analysis	Factor analysis	Intent scale translation	Intent scale translation	Intent scale translation	Factor analysis
	Cluster analysis				Cluster analysis

Collected approximately 1000 answers from each of 25 EU countries, 4 candidate countries, and 3 European Free Trade Association countries.

Numbers in parentheses represent number of questions in face sheet (age, sex, income etc.). Since there are branching type questions, not all people responded in these numbers.

Four clusters of questions, each cluster to be answered in 30 minutes.

Table 2.

Correct answer rates (%) for scientific knowledge questions.

Question	Present survey	NISTEP	Eurobarometer	NSF
	2008	2001	2005	2001
	n = 1,286	n = 2,146	n = 24,895	n = 1,574
4-1. The center of the Earth is very hot.	82.0	77	86	80
4-2. All radioactivity is man-made.	73.8	56	59	76
4-3. The oxygen we breathe comes from plants.	61.7	67	82	-
4-4. It is the father’s gene that decides whether the baby is a boy or a girl.	32.6	25	64	65
4-5. Lasers work by focusing sound waves.	29.6	28	47	45
4-6. Electrons are smaller than atoms.	32.3	30	46	48
4-7. Antibodies kill viruses as well as bacteria.	27.9	23	46	51
4-8. The universe began with a huge explosion.	66.4	63	-	33
4-9. The continents have been moving their location for millions of years.	86.7	83	87	79
4-10. Human beings are developed from earlier species of animals.	75.0	78	70	53
4-11. The earliest humans lived at the same time as the dinosaurs.	43.7	40	66	-
4-12. Radioactive milk can be made safe by boiling it.	84.2	84	75	-
4-13. All natural objects such as plants are safe, while all artificial substances are dangerous.	75.9	-	-	-
4-14. Does the Earth go around the Sun, or does the Sun go around the Earth?¹	-	58²	66	75
4-15. How long does it take for the Earth to go around the Sun? ¹	-		66	54

Choice of response is “True,” “False,” or “Don’t know,” except for question 4-14, for which the choices are “Earth around the Sun,” or “Sun around the Earth,” and question 4-15 for which the choices are “One day,” “One month,” “One year,” “Other,” and “Don’t know.”

NISTEP survey data show the ratio of the number of people who answered both questions 4-14 and 4-15 correctly.

From the viewpoint of its personal utilization in a society where science and technology are ubiquitously located, Shen classified “scientific literacy” as constituting three elements (Shen, 1975). The first element is “practical scientific literacy,” knowledge for solving practical problems related to a person’s health and survival. The second element, “civic scientific literacy,” is defined as understanding of the vocabulary, process and impact of science and technology. Shen considered this element of scientific literacy for citizens to participate in the making of public policy. The third element, “cultural scientific literacy,” is the capability to accept scientific information and suchlike as intellectual entertainment; Shen considers this the element that unites science with the arts.

On the basis of Shen’s concept, Miller defined civic scientific literacy in terms of the following three dimensions (Miller, 1998): (1) a vocabulary of basic scientific constructs sufficient to read competing views in a newspaper or magazine; (2) an understanding of the process or nature of scientific inquiry; and (3) some level of understanding of the impact of science and technology on individuals and on society. Of these, (1) and (2) are the dimensions of scientific literacy that are evaluated through the true/false quizzes implemented to make international comparisons. These quizzes are created on the basis of the factor analysis and Item Response Theory (IRT) using the Eurobarometer and data of the Science and Engineering Indicators (Miller, 1998).

In Japan, the National Institute of Science and Technology Policy (NISTEP) has conducted a “Survey of Public Attitudes Toward and Understanding of Science & Technology in Japan” (Okamoto et al., 2002), the object of which was to estimate the level at which Japanese citizens need to be aware of science and technology in order to establish science and technology policies. The survey questions included items respondents could check to indicate their interest in science and technology, questions on their source of information on science and technology, and questions concerning their attitude towards the effects of science, etc. The NISTEP surveys of 1991 and 2001 were designed to be used for international comparison as well. These surveys included questions similar to those of the Eurobarometers corresponding to the first dimension (understanding of terms and concepts of science) and the second dimension (understanding of methodologies and processes of science) of a citizen’s scientific literacy according to the Miller model, so that it was possible to achieve a grasp of Japanese public scientific literacy in these two dimensions and make international comparisons.

It must be noted, however, that the third dimension in Miller’s concept, which is thought to be influenced by culture, was not covered by these surveys. Yet, as has already been noted, the third dimension, “understanding of the effect of science on society,” is considered increasingly important under current science and social conditions. Evaluating scientific literacy with true/false quizzes of scientific knowledge only indicates one axis on the scientific literacy matrix: whether a person has more or less scientific knowledge. Thus, even though we are able to find out how much knowledge a person has, this does not provide us with a solution for improving the person’s scientific literacy.

At this point, it is helpful to examine another survey using different methodology, “Science and the Public: A Review of Science Communication and Public Attitudes to Science in Britain” (2000), conducted by the Office of Science and Technology (OST) and the Wellcome Trust. This is an attitude survey consisting of complex elements and does more than just check the respondent’s attitude to science. It also asks questions on what media and media content the respondent is watching, whether the respondent trusts such media, how the respondent spends his/her spare time, the respondent’s religious beliefs, etc. The respondents are classified into six clusters, including “Technophiles,” “Concerned,” and “Not for Me,” based on nine factors including “intrinsic interest in science,” “concern over the control and direction of science,” and “attitude toward authority.” Other surveys with the same objective and using the same cluster analysis have been carried out in the UK since (OST and Department of Trade and Industry, 2005; Research Councils UK and Department for Innovation, Universities & Skills, 2008). These surveys do not confirm scientific literacy in the narrow sense² by using true/false quizzes to check the respondent’s level of scientific knowledge. Rather, their intent is to “help science communicators think about the information needs of their audiences” (OST and Wellcome Trust, 2000: Foreword) and in this respect they are closer in character to our survey. To our knowledge, no one else has conducted a scientific literacy survey in Japan using cluster analysis. We have therefore used the methodologies of these surveys as reference.

The definition of scientific literacy in this study

On the basis of these previous studies, we defined scientific literacy as the “capability of making a social judgment and taking action on issues involving science and technology by linking basic scientific knowledge and methodologies to interests and attitudes, including those related to science” (Saijo and Kawamoto, 2008). This definition of literacy consists of three elements. The first, scientific knowledge and methodologies (scientific literacy in a narrow sense³), and the second, interest in and attitude toward society and science, form the basis for judgment and action on social issues related to science (the third element). This definition incorporates all elements of the Shen–Miller model (see Appendix 1 at http://pus.sagepub.com/).

Correlation among these three elements of scientific literacy (literacy structure) in our definition has a certain tendency, and we thought we would be able to capture this to a degree with a survey using a questionnaire. With this in mind, we tried to clarify the structure inside scientific literacy, i.e., the relationship of scientific literacy in the narrow sense with other elements, and to capture the inter-literacy structure, i.e., what kinds of groups of people (literacy clusters) there are and what literacy structures exist within these groups. Since we wanted to see social trends, the survey was limited to adults.

Our definition does not have as its objective the goal of improving scientific literacy within the science community through formal education at academic institutions. Rather, it is a definition intended to form a basis for designing a strategy for wider science communications. The novelty of our survey is that it is based on cluster models defined by trends grounded in a comprehensive view of scientific literacy that encompasses social attitudes, rather than on a simple linear measure of high or low scientific literacy.

3. Methods

The structure of the questionnaire

We will describe below each question group in the questionnaire,⁴ citing the major scientific literacy surveys we used as reference (see Appendix 1).

(Q1) Field of interest

The first group of questions consists of questions pertaining to the respondent’s fields of interest. Questions on the degree of interest in various fields, including science and technology, were given for a total of 15 fields using “Science and the Public” of the UK (2000) as reference. In our survey, because it was intended only to find the relationship between the respondent’s interests in non-science fields and those in science fields, no questions were asked concerning interest in specific fields of science. Questions in this group as well as in the second group of “Interests and Attitudes,” and the third of “Evaluation of Science and Society” were answered using a 4-point scale of, “Agree,” “Slightly agree,” “Slightly disagree,” and “Disagree.” This was designed to eliminate neutral answers.

(Q2) Interests and attitudes

The second group consists of 35 questions pertaining to the respondent’s interests and attitudes. First were questions on which media the respondent normally uses, because we thought that information about the media the respondent trusts and the kind of presentation the respondent prefers can be useful in science communications. Although in some surveys, including those conducted by NISTEP, the respondents are asked what kind of media they prefer as the source of their scientific and technological information, we went a step further by asking how much information, all kinds of information not just scientific or technological, the respondent gets from newspapers, television and radio, books, the Internet, family members and friends (Appendix 1, Q2A; Q2-1–5). We then asked if the respondents sought answers on their own if they had questions in their daily lives, if they were capable of using electric appliance products, and the like, i.e., questions related to practical scientific literacy (Appendix 1, Q2A; Q2-6–9, 19–22). Additionally, we asked about the respondent’s attitude toward science (Appendix 1, Q2A; Q2-18, 23, 24). Finally, we provided questions concerning the respondent’s social activities, including participation in public office elections, community activities and discussions (Appendix 1, Q2B; Q2-10–17, 25–27). For some of the questions, we used the NISTEP survey as reference (Okamoto et al., 2002). Finally, we asked the respondents to indicate their personal skills in various areas, not just those limited to science and technology (Appendix 1, Q2C; Q2-28–35).

(Q3) Evaluation of science and society

The third group consists of 15 questions concerning the respondent’s evaluation of science and society. These include questions about the values of science in daily life, intelligence value, social value, and national value, and the influence of science (Appendix 1, Q3A; Q3-1–9). Also included are questions on the respondent’s trust and/or confidence in scientists, the media, politicians, and governments (Appendix 1, Q3B; Q3-10–15).

(Q4) Understanding of scientific knowledge

As we considered scientific knowledge to be a key factor for the scientific literacy newly defined for this survey, we provided a quiz of 13 true/false questions (Table 2). Essentially, we adopted the IRT-standard questions used in previous surveys (NSF, 2002; Okamoto et al., 2002; European Commission, 2005), because we decided that, given the very broad range of scientific fields, it would be too difficult to newly establish the parameters for a basic knowledge domain necessary for adult life in contemporary society.

These knowledge questions were essentially used, not to find out the rate of correct answers or to correlate responses with the other attitudes or interests of the respondents, but rather to measure the degree of scientific knowledge for each extracted cluster using only the total points (knowledge score). As to the total points, although it would be correct to apply the Threshold parameter provided by IRT, for this paper we have simply assigned one point for each question.

(Q5) Understanding of scientific methodology and social judgment

Scientific methodologies, as compared to scientific knowledge, require a greater depth of understanding and are better evaluated with more complex questions and/or descriptive answers. However, given the limitation of a non-face-to-face survey and the difficulty in evaluating descriptive answers, we chose to offer true/false choices for questions each consisting of one to three sentences. Three questions were provided in which the respondent was asked to do calculations, make graphs and read the results (Appendix 1, Q5A; Q5-4, 6, 9). Also provided were six questions for which correct answers are theoretically conceivable, but for which there are different opinions in reality (Appendix 1, Q5A; Q5-1–3, 5, 7, 8). We used PISA’s questions as reference. The PISA questions include those with multiple-choice answers and those requiring free descriptive answers, and are thus intended for evaluating to a degree the respondent’s thinking capabilities within the context of daily life (OECD, 2007). However, with a survey relying on the mail, a questionnaire consisting of a combination of multiple-choice questions and free descriptive questions could end up with a low retrieval rate as well as present difficulty in tabulating the descriptive answers. We therefore decided to use only multiple-choice question/single answer type.

Attributes

In addition to questions asking the respondent’s age, sex, profession, final schooling, and annual income, each respondent was asked if s/he considers her/himself a science type person, liberal arts type person or neither. We also asked about place of residence (Tokyo / 17 government-ordinance cities / other cites / town or village).

Method of collecting answering sheets and analysis

A mail survey was targeted at 4,000 people across Japan selected by means of a two-stage stratified random sampling from the Basic Resident Registers. In the first stage, the whole of Japan was divided into 10 regions, and in the second stage, these regions were further divided into 3 urban groupings (the 23 wards of downtown Tokyo, designated cities with populations exceeding 200,000, etc.). The total of 30 groupings was then divided into 200 areas in proportion to the size of their primary group. Finally we made a random selection of 20 people from each of the 200 areas. The survey was carried out from March 18 to April 7, 2008. A total of 1,286 responses were received (consigned to Central Research Services, Inc.).

First, factors (scientific literacy factors) were extracted by applying a factor analysis to the 65 questions in the first, second and third question groups. We then performed a correlation analysis of the extracted factors and the scores for the scientific knowledge quiz. Factor analysis was carried out only on the 65 questions in the first three question groups because responses to these questions were all 4-point scale choice answers. Responses for the other question groups were either true/false/don’t know or nominal scale choices for which factor analysis cannot be applied. Next, respondents were classified into literacy clusters based on the factor score of extracted scientific literacy factors.

4. Results

Next, we summarize results of the scientific knowledge quiz, and present the factor extraction and cluster formation fields for the 65 questions in the three question groups, i.e., personal interests, attitudes and concerns regarding science and society, and evaluations of science and society.

Extent of scientific knowledge

We included a true/false quiz on scientific knowledge coinciding with those of the previous surveys mentioned. The rate of correct answers for each question was compared with results of previous surveys and was used for analyzing the correlation with the literacy factor. Table 2 shows the comparison with previous surveys of the correct answer rate for each question. The correct answer rate of our survey was lower than those for the NSF and Eurobarometer surveys (NSF, 2002; European Commission, 2005), indicating, as was also indicated by the previous surveys, that the Japanese have a lower level of scientific knowledge than Europeans and Americans.

Factor extraction

The maximum likelihood method and Promax rotation (oblique rotation) was used for the extraction of factors. The estimated value of the Kaiser-Meyer-Olkin measure of sampling adequacy⁵ was 0.89, and the particular sample analysis was confirmed to be effective. After extracting responses to the 65 questions, another extraction was attempted from the answers to the 38 questions remaining after excluding answers with communality of less than 0.22 and factor load of less than 0.3. The number of factors was chosen to be three using the Scree test⁶ (see Appendix 2). The first “scientific” factor is related to interests in and knowledge of science and technology, learning the skills of operating electronic devices, and logical thinking. The second “social” factor is related to interests in social fields such as welfare, local communities, and environment, as well as willingness to participate in public discussions. The third “science-appreciating” factor relates to values of various aspects of science as well as attitude toward science and scientists.

A weak positive correlation was found between the scientific factor and social factor (r = 0.328). There were moderate correlations between the scientific factor and the science-appreciating factor (r = 0.515), and between the scientific factor and the knowledge score (r = 0.432). Although a positive moderate correlation was found between the social factor and the science-appreciating factor (r = 0.507), no correlation was found between the social factor and knowledge score (r = 0.060). Only a very weak positive correlation was found between the science-appreciating factor and knowledge quiz score (r = 0.248).

Establishing a 3-factor/4-cluster model

Using the three factors obtained by the factor analysis, a cluster analysis was conducted based on the K-means method. Four clusters were established on the basis of the distances between the centers of the clusters and the number of samples in each cluster (Table 3). Differences were found when numbers of people of high, medium, and low knowledge scores within each were compared to those in other clusters (χ², P < 0.001), and Clusters 1 and 2 were found to have higher knowledge scores compared to Clusters 3 and 4 (Table 3). Table 3 shows the distribution in each cluster by sex, age group, final schooling, annual income, and region. Significant differences were evident in all of these categories (χ², P < 0.001), except for the category of region which showed no differences (χ², P = 0.716). Differences between clusters in terms of sex and age were also reported in the UK surveys (OST and Wellcome Trust, 2000; OST and Department of Trade and Industry, 2005; Research Councils UK and Department for Innovation, Universities & Skills, 2008). Also reported were differences related to income and educational level (OST and Wellcome Trust, 2000). The UK surveys were based on region rather than city size, but this did not make any difference (Research Councils UK and Department for Innovation, Universities & Skills 2008).

Table 3.

The four clusters.

		Cluster 1		Cluster 2		Cluster 3		Cluster 4
		Inquisitive		Sciencephiles		Life-centered		Low-interest
Median	Scientific factor	0.8216		0.7113		−0.5195		−1.0444
	Social factor	0.9403		−0.5723		0.2205		−1.1212
	Science-app. factor	0.9374		−0.0256		−0.1275		−1.1554
Knowledge score	0–6: 333 (27.9%)	48 (15.2%)		35 (13.9%)		150 (36.7%)		100 (46.3%)
	7–10: 537 (45.1%)	152 (48.3%)		116 (46.0%)		189 (46.2%)		80 (37.0%)
	11–13: 322 (27.0%)	115 (36.5%)		101 (40.1%)		70 (17.1%)		36 (16.7%)
	Mean	8.6		8.9		7.2		6.6
	Standard error	0.13		0.14		0.12		0.20
	Standard deviation	2.22		2.23		2.44		2.91
Population: n = 1192*		315 (26.4%)		252 (21.1%)		409 (34.3%)		216 (18.1%)
Sex	male: 551/female: 637	182	133	177	74	118	289	74	141
	(46.2%)/(53.4%)	(57.8%)	(42.2%)	(70.2%)	(29.4%)	(28.9%)	(70.7%)	(34.3%)	(65.3%)
	NA: 4 (0.3%)	0 (0%)		1 (0.4%)		2 (0.5%)		1 (0.5%)
Age	18–19: 40 (3.4%)	5 (1.6%)		16 (6.3%)		5 (1.2%)		14 (6.5%)
	20–29: 130 (10.9%)	22 (7.0%)		38 (15.1%)		34 (8.3%)		36 (16.7%)
	30–39: 224 (18.8%)	43 (13.7%)		78 (31.0%)		59 (14.4%)		44 (20.4%)
	40–49: 229 (19.2%)	63 (13.7%)		47 (18.7%)		83 (20.3%)		36 (16.7%)
	50–59: 265 (22.2%)	88 (27.9%)		39 (15.5%)		102 (24.9%)		36 (16.7%)
	60–69: 300 (25.2%)	94 (29.8%)		33 (13.1%)		124 (30.3%)		49 (22.7%)
	NA: 4 (0.3%)	0 (0%)		1 (0.4%)		2 (0.5%)		1 (0.5%)
Final schooling	Jr. high school:93 (7.8%)	17 (5.4%)		10 (4.0%)		40 (9.8%)		26 (12.0%)
	High school: 449 (37.7%)	107 (34.0%)		69 (27.4%)		171 (41.8%)		102 (47.2%)
	Vocational school: 283 (23.7%)	64 (20.3%)		59 (23.4%)		110 (26.9%)		50 (23.1%)
	University/College: 330 (27.7%)	110 (34.9%)		101 (40.1%)		81 (19.8%)		38 (17.6%)
	Graduate school: 36 (3.0%)	17 (5.4%)		13 (5.2%)		6 (1.5%)		0 (0%)
	NA: 1 (0.1%)	0 (0%)		0 (0%)		1 (0.2%)		0 (0%)
Annual income (million ¥)	0–2: 100 (8.4%)	12 (3.8%)		16 (6.3%)		38 (9.3%)		34 (15.7%)
	2–4: 301 (25.2%)	72 (22.9%)		48 (19.0%)		115 (28.1%)		66 (30.6%)
	4–6: 314 (26.3%)	83 (26.3%)		72 (28.6%)		109 (26.7%)		50 (23.1%)
	6–8: 204 (17.1%)	62 (17.1%)		53 (21.0%)		60 (14.7%)		29 (13.4%)
	8–10: 123 (10.3%)	36 (10.3%)		27 (10.7%)		39 (9.5%)		21 (9.7%)
	10–: 139 (11.7%)	47 (11.7%)		32 (12.7%)		46 (11.2%)		14 (6.5%)
	NA: 11 (0.9%)	3 (1.0%)		4 (1.6%)		2 (0.5%)		2 (0.9%)
Region	Tokyo: 77 (6.5%)	22 (7.0%)		21 (8.3%)		26 (6.4%)		8 (3.7%)
	Ordinance-designated city: 287 (24.1%)	78 (24.8%)		64 (25.4%)		95 (23.2%)		50 (23.1%)
	Other city: 703 (59.0%)	185 (58.7%)		138 (54.8%)		242 (59.2%)		138 (63.9%)
	Town and village: 119 (10.0%)	29 (9.2%)		27 (10.7%)		43 (10.5%)		20 (9.3%)
	NA: 6 (0.5%)	1 (0.3%)		2 (0.8%)		3 (0.7%)		0 (0%)

Although 1286 responses were collected the total number of responses to which cluster analysis was applied was 1192 responses.

Cluster 1: Inquisitive

Cluster 1 was high in all factors, i.e., scientific, social, and science-appreciating factors, as well as in knowledge scores (Table 3). The constituents of this group are highly interested in and proud of their understanding of science, value science highly, and have ample knowledge of science. We must be careful, however, not to automatically assume that they have “best literacy.” Although it was eliminated from the targets for factor analysis because its factor loading and communality were low, one of the statements on the survey questionnaire was (Q2-18) “I believe that supernatural phenomena such as psychic power exist.” The ratio of those in Cluster 1 who agreed with this “psychic statement” was 22.2% (70 responses), the highest among the four clusters (χ², P = 0.004). On the other hand, to Q5-2 “I heard that you can make a better wine if it is fermented under an atmosphere where Mozart’s music is played. What do you think of that story?” (hereinafter, the “Mozart question”), the ratio of those in Cluster 1 who answered “I believe it” was 25.1% (79 responses), also higher than that of the other clusters (χ², P =0.003). Tando (2008) reported that there is a weak positive correlation between “fortune telling and magic preference” and “inquisitive mind,” so that a person with a strong inquisitive mind tends to believe in and enjoy fortune telling and magic. The results of our survey may be reflecting this tendency. In any event, it is probably safe to say that Cluster 1 has a strong, positive literacy tendency.

Cluster 2: Sciencephiles

Cluster 2 is a cluster with a high scientific factor, a low social factor, and a medium science-appreciating factor. Its knowledge score is approximately the same as Cluster 1. It has a demographically large proportion of males (70.2%, 177 responses), and a high percentage of young people. Compared to Clusters 3 and 4, Clusters 2 and 1 are relatively better educated and have higher incomes (χ², P < 0.001). There was also a tendency for individuals with a high science factor to be frequent users of the Internet (Appendix 2, Q2-4). A high science factor is another point that Clusters 1 and 2 have in common. But although Cluster 2 showed a similar tendency to Cluster 1 in the responses for the scientific factor, the ratio of people who responded negatively to the “psychic question” (Q2-18) was 22.2% (56 responses), which was the largest after Cluster 4. Likewise, the ratio of negative responses to the “Mozart question” was the largest after Cluster 4 at 18.3% (46 responses). It would seem that constituents of Cluster 2 are more skeptical than those of Cluster 1. Moreover, although there was similarity between Clusters 1 and 2 in terms of the scientific factor and knowledge score, they differed in terms of the science-appreciating factor. In the UK survey “Science and the Public,” in which 6 clusters were extracted from 9 factors (OST and Wellcome Trust, 2000), “confident believers” and “technophiles” had high levels of education and income and shared a strong interest in science, but differed in their degree of trust in science and politics. While a simple comparison cannot be made, these characteristics resemble those of the Cluster 1 “Inquisitive” and the Cluster 2 “Sciencephiles.”

Cluster 3: Life-centered

In Cluster 3 the scientific factor is low, the social factor is medium high, and the science-appreciating factor is medium. The knowledge quiz score is low compared with Clusters 1 and 2. The population ratio is 34.3% (409 responses) and is the largest of all the clusters. The peculiarity of Cluster 3 is its low scientific factor. However, a closer look at individual responses reveals there were people who responded negatively to the statement (Appendix 2, Q2-23) “I believe I am knowledgeable of science and technology” but positively to the statement (Q2-24) “I wish to know more about science and technology.” Also, looking at results for the whole cluster, even though the factor contribution ratio is low, those with a relatively high social factor did show a tendency to visit science museums, as evidenced by the high ratio of positive responses to the statement “I often visit science museums and attend adult education classes” (Q2-9). Therefore, we cannot conclude that Cluster 3 is simply a group with science phobia. This group has a large population of women (70.7%, 289 responses). Quin and Brown (2007) reported that in theirsurvey conducted in the US, they found a gender gap in the attitude toward science, and that women are generally less tolerant of genetically modified foods, expressing greater concern over their effects on the human body, and have a greater trust of environmental groups. In our survey, we asked if respondents would be willing to eat food only minutely contaminated with chemical substances (Q5-3) and the respondents in Cluster 3 showed a very cautious tendency with 62.3% (255 responses) saying they would “Try not to eat as much as possible” and 24.0% (98 responses) saying “Don’t eat at all.” This was the most cautious trend among all four clusters (χ², P < 0.001). Likewise, to the question as to whether a mother should be stopped from giving contaminated breast milk to an infant (Q5-8), the respondents of Cluster 3 expressed the highest rate of reservation with 31.5% (129 responses) selecting “agree” and 29.6% (121 responses) selecting “slightly agree” (χ², P = 0.037).

Cluster 4: Low-interest

Cluster 4 has a high percentage of women and slightly high percentage of young people, while the population was approximately half of Cluster 3, with a ratio of 18.1% (216 responses), the smallest among all the clusters. Cluster 4 is low in scientific factor, social factor, and science-appreciating factor as well as in knowledge score. Low age, education, income, interest and trust in science, and the large number of women all combine to make Cluster 4 resemble in its tendencies those who responded “Not sure” in the UK survey (OST and Wellcome Trust, 2000).

Still, we cannot assert that Cluster 4 is a group of people who have no interest in anything. We believe that the three factors of scientific literacy in our questionnaire simply failed to capture the interests and concerns of the people who belong to Cluster 4. From the standpoint of improving science education for everyone, it is important for us to capture the interests of the people who belong to this cluster, and encourage their participation in the communication program to improve scientific literacy.

5. Discussion

Using a postal survey based on a random sampling of subjects, we succeeded in clarifying the relationship between various elements, not just those between the scientific factor, science-appreciating factor, and knowledge quiz score, but also including the social factor, for the scientific literacy of the Japanese people. The relationship of these factors is of material importance in designing effective platforms for science communication.

The first finding of our study is that there is a relationship between knowledge and attitude. The total score of the 13 quiz questions concerning scientific knowledge correlates positively with the scientific factor, but has only a very weak correlation with the science-appreciating factor, and no correlation with the social factor. This result suggests that Japanese citizens may have good knowledge of science but are not necessarily supportive of science and technology matters as they affect society. In contrast, it has been reported that in developing countries, the more knowledgeable citizens are, the more supportive they are of science and technology (Shukla and Bauer, 2009). Sturgis and Allum (2004) have also reported that scientific knowledge is not a primary factor for an affirmative attitude toward science in the UK, but that it does have a positive influence. However, the Eurobarometer survey indicated that there is no strong correlation between the two (Shukla and Bauer, 2009). Our result which indicates the scientific knowledge has only a very weak correlation with the science-appreciating factor come close to the results of the latter.

Our second finding was to clarify the differences and similarities of scientific literacy between groups classified into clusters. For example, the social factor for Cluster 2 is low while that for Cluster 3 is medium high, yet both clusters have a medium science-appreciating factor. We noted that the science-appreciating factor has positive correlations not only with the scientific factor but also with the social factor. Also noted was the fact that the social factor has a higher positive correlation with the science-appreciating factor. These results seem to indicate that people with higher social consciousness and desire for participation are also likely to place a relatively high value on science and technology.

What these findings indicate is that the four clusters are not completely isolated from each other but share tendencies that should make communication possible. For example, Cluster 2 may recognize the tendency of its own social factor when it cooperates with Cluster 3. In the same way, the scientific interests of those in Cluster 3 may be stimulated by communication with those in Cluster 2. And this is the final finding of our study. Thus, we see possibilities for learning from each other through communication designed to stimulate the interaction of the literacy characteristics of each cluster. There is a report that many of the scientific interests and understandings of adults are affected by free-choice learning (Falk et al., 2007). Free-choice learning and the communication program that we envision for the improvement of scientific literacy have some things in common. Both place importance on inherent motivation (scientific factors in the case of Clusters 1 and 2 and social factors in the case of Clusters 1 and 3), and both construct learning programs on this premise.

The purpose of clustering is neither to divide into certain stereotypes, nor to show an ideal cluster. The objective is not for Clusters 2, 3, and 4 to become like Cluster 1. What is important, we believe, is to draft a proposal for a communication program based on the internal motivation, theme setting and communication designs for the different clusters so that we can guide people to more fruitful activities that will eventually lead to the kind of scientific literacy desirable for the whole of society.

6. Conclusion and future directions

The present study was intended to provide the foundation for the design of practical science communication activities by defining scientific literacy in terms of its importance in society, and by providing a 3-factor/4-cluster model. While simple comparison cannot be made with the UK cluster survey because of different questions and factors, it would be interesting to compare the scientific literacy of our two countries on the basis of a more refined analysis. However, comparing scientific literacy between countries is not our primary objective. We are currently working on a field survey of a number of areas in Japan focusing on actual science communication activities and education activities (Saijo and Kawamoto, 2008). We have also prepared a simplified questionnaire consisting of 10 questions to identify scientific literacy that will be used in small-scale surveys at science cafes.

The final objective of our research is to develop a science communication program to improve the scientific literacy of all of Japanese society. School education plays a major role in improving scientific literacy. Because our survey was limited to adults older than 18, we cannot discusses the correlation of scientific literacy among adults and students. We are currently conducting a supplementary survey of junior and senior high school students using a simplified questionnaire. A future topic to be addressed is how adult and student scientific literacy relate to each other and what the implications are for science education in schools.

Through these surveys, we intend to analyze the relation between the scientific literacy cluster of participants in scientific events and education, to find out what kind of themes and structures are most effective for each cluster, as well as how to design platforms for effective science communications.

Footnotes

Acknowledgements

The present study was conducted under the research titled “Survey of current status of scientific literacy and development of educational program by tendencies in social activities” (Principal researcher: Miki Saijo), funded by the research development program titled “Scientific Literacy of the 21st Century” (2006–09) under the auspices of the Research Institute of Science and Technology for Society of the Japan Science and Technology Agency. Our heartfelt gratitude goes to all the people who assisted us in this research and survey

Notes

Author biographies

Shishin Kawamoto is a research associate at the Tokyo Institute of Technology. Since completing his Ph.D. in developmental biology at Hokkaido University, he has focused on scientific literacy and education in science communication.

Minoru Nakayama is a professor at the Tokyo Institute of Technology. He is using engineering methodology to analyze the relationship between the characteristics of human learning behavior and the environment in which a learner is placed. He is also studying a method of statistically evaluating learning methods and learning systems.

Miki Saijo is a professor at the Tokyo Institute of Technology. Her professional field of study is applied linguistics, and the central theme of her study is communication design in contact situations as it relates to formal communication between people having different knowledge and experience. She is also studying how scientific literacy enables public engagement.

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