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- • Module 3
- • 1. Discuss in pairs the quotes below and point out the one you agree with.
- • 2. Which of the following aims of education are most important? Rank them in order of importance, then compare with your partner.
- • 4. Discuss together.
- • 5. Listen to three people talking about what they used to like and dislike about school and put the correct letters in the spaces provided.
- • 6. Read the article and comment on the problems of the British teachers and pupils. Are these problems found in your country? Classroom Chaos: How Teachers Lost Control
- • 7. Decide whether the following statements are true or false.
- • 8. Explain the meaning of the underlined phrases in the text above and recall the context in which they have been used.
- • 9. Read the jokes and dwell upon the kind of student you were at school regarding your behaviour. Make use of the topical vocabulary in the table below.
- • 10. Translate into Ukrainian.
- • 11. Match the beginnings of the sentences with their ending and translate them.
- • 12. What do you call …
- • 13. Paraphrase the phrases in italics using your topical vocabulary.
- • 14. Translate the text into English and think up your own continuation of the story using your topical vocabulary.
- • 15. A) Read the messages in an Internet chat room. Which messages are for mixed-sex schools, which are against, and which are neither for nor against?
- • 16. Discuss in pairs.
- • In your opinion, what are the reasons students stay away from school? Think about:
- • Influence of friends
- • 17. Read a part of the interview about truancy. Find out the main reason for truancy from the point of view of Glen Hall.
- • 19. Which of the following things do you think would be most effective in combating truancy? Rank them, then compare your list to your partner’s.
- • 20. Study the vocabulary relating to the problem of truancy and tell you group mate about the worst skiver you have ever known.
- • 21. Fill in the gaps with the missing words.
- • 22. A) You will listen to the discussion concerning the re-drafting of the law aimed at reduction of truancy. Make notes on the following points:
- • 23. Complete sentences 1-11 with a suitable word or expression from the box.
- • 24. Complete the texts with words from the list.
- • In the uk
- • In the us
- • 29. Work in pairs. Discuss the meaning of the words and expressions in bold in the extract.
- • 30. Translate into English.
- • 31. Discuss together.
- • 32. Read the text and find out the advantages and disadvantages of being a prodigy child. Prodigy Children
- • 33. Answer the questions below.
- • 34. Find in the text words and expressions that mean the following.
- • 35. Match the words in the left column with the words in the right one to make the collocations and recall the context in which they have been used.
- • 36. Choose the right word to fill each gap (a, b, c or d).
- • Valuable lessons
- • 37. Complete the following article with the missing words. Use only one word for each space. Talents of gifted children are not recognized
- • 38. Read the text that follows. Use the words in the right column to form words that fit in the same line in the text. There is an example (0). My Child Is a Genius!
- • 39. Translate into Ukrainian.
- • 40. Study the vocabulary.
- • 41. Rewrite the sentences using the phrases from the previous exercise.
- • 42. Translate into English using the topical vocabulary.
- • 43. Read the article and correct the mistakes in the summary.
- • 45. Answer the questions.
- • Postsecondary Education: Admissions
- • 46. Choosing a university
- • 47. Translate into English.
- • 48. Translate into English.
- • 49. How similar is higher education in our country? Answer these questions. Compare your answers with someone else’s in your group.
- • 50. Using the active vocabulary provided below make up a report or presentation about postsecondary education and entrance exams to Ukrainian universities. Active vocabulary
- • 51. Translate into English.
- • 52. Translate into English
- • 53. Study rules for coping with exam stress. Six rules for coping with exam stress.
- • 54. Using a dictionary if necessary, underline the correct word in the sentences. Use the remaining words in sentences of your own.
- • 55. A) Match the pairs of adjectives to the nouns to form collocations.
- • 56. Look at the sentences below and fill in the gaps using the appropriate word from a. B or c
- • One’s work at school
- • University choice
- • 56. Study the rules on the sequence of English tenses.
- • 57. Put the verbs in brackets in correct form.
- • 58. Translate into English observing the rules. Mind the exceptions to the rules of the sequence of tenses.
- • Expressions used with say, tell and ask.
- • 61. Fill in the gaps with say or tell in the correct tense.
- • 62. Read and learn about the reported statements.
- • 63. Finish the sentences using Reported speech. Always change the tense, although it is sometimes not necessary.
- • 64. Finish the sentences using Reported speech. Always change the tense, although it is sometimes not necessary.
- • 65. Rewrite these sentences using direct speech. Complete the sentence.
- • 66. Yesterday you met a friend of yours, Tom. Here are some of the things Tom said to you:
- • 67. Somebody says something to you which is the opposite of what they said before. Write a suitable answer beginning with I thought you said ... .
- • 68. Read the interview, then complete the summary.
- • 69. Choose the correct answer a, b or c.
- • 70. For each of the following sentences, read the direct quote, and then complete the sentence. Student Dilemma
- • 71. Put the following statements into indirect speech.
- • 72. Read some more jokes and study the phrases below connected with students’ work at school. Distribute them into two columns depending on the positive or negative meaning of the phrases.
- • 73. Study the difference between the British English and American English pronunciation.
37. Complete the following article with the missing words. Use only one word for each space. Talents of gifted children are not recognized
A recent report has shown that conventional intelligence tests may not be the best way 0)….of… identifying gifted children. It seems that the tests fail to pick up specific aptitudes and 1)……… important factors, such as motivation. Another problem is that 2)……… it is difficult to test intelligence without relying 3)……… vocabulary knowledge, the results of the tests are inevitably influenced by 4)………a child has already learned at school. The report, a review of international research on 5)………gifted child, suggests that while many child prodigies fail to maintain 6)………success into their adult life, both parents and teachers in England tend to pick the wrong children. Primary teachers in England tended to label children 7)………very able on the basis of their ways of working 8)……… than their cognitive ability. A study 9)………1984 showed that 40 per cent of potential high-achievers 10)……… been underestimated by their teachers. Furthermore, parents and teachers were far 11)……… likely to see boys as gifted. Studies in America, China and England all showed a stable ratio of two boys for 12)………girl identified as highly able. The report’s author, professor Freeman, urges schools to provide extra activities 13)………able pupils. Instead of just teaching gifted children in the same 14)……… as other children, but more quickly, these extra activities would be aimed 15)……… stimulating the child’s special aptitudes and interests.
38. Read the text that follows. Use the words in the right column to form words that fit in the same line in the text. There is an example (0). My Child Is a Genius!
39. translate into ukrainian..
Because she does not go to school, Ruth has not mixed much with other children. 'She enjoys serious conversation with adults,' her father said, 'and I don't think she will feel out of place at Oxford.' He does not think she works harder than other children her age, but concentrates on what she enjoys, principally mathematics. 'She watches television a little but not as a habit', he explained. 'But she plays the piano and has quite a wide range of interests.'
If she does well at St Hugh's, Ruth expects to take a further degree and eventually hopes to become a research professor in mathematics — an ambition she may achieve while still in her teens. The Lawrence family plans to move to Oxford when Ruth takes up her place in October 2006. Before then, she plans to take four A levels to satisfy the college matriculation requirements. Her father hopes she will be exempt from the requirement to pass a foreign language — a 'diversion', he feels, 'from her main interest'.
Miss Rachel Trickett, the principal of St Hugh's, said last night: 'We are all very excited about Ruth. She is obviously quite brilliant and has shown genuine originality.' Ruth's future tutor, Dr Glenys Luke, admits that taking so young a student is a daunting responsibility but says it is one she expects to enjoy. 'I shall tailor the teaching to her requirements', she said. 'Ruth shouldn't have to suffer the same tensions and disappointments that older students face. I hope I shall make it fun for her.'
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- Front Psychol
Identifying Gifted Children: Congruence among Different IQ Measures
1 Faculty of Psychology, Oviedo University, Oviedo, Spain
2 Department of Psychology, Sociology and Philosophy, Faculty of Education, León University, León, Spain
3 Asunción León-Primary and Secondary School, León, Spain
This study has two main aims: (1) analysing the relationship between intellectual capacities and levels of creativity in a sample of Spanish students from the third and sixth grades; and (2) examining the discrimination capacities and degree of congruence among different tests of intellectual ability that are commonly used to identify high-ability students. The study sample comprised 236 primary school students. Participants completed different tests of intellectual ability, which were based on both fluid and crystallized intelligence, as well as creativity. Results indicated that it is advisable to use varying tests in the assessment process, and a complementary measure (i.e., creativity) in order to create a multi-criteria means of detection that can more efficiently distinguish this population of students.
Identifying students with higher abilities has become a subject of great interest for researchers, education administrators, teachers and families alike. However, it is also a controversial issue because there is still no agreement on which variables must be taken into account to determine whether a student has higher abilities, or how these variables should be measured in these cases.
The different conceptualizations of higher intellectual abilities, either from educational, socio-political or psychometric perspectives, have traditionally tried to identify those children who are exceptional ( Pfeiffer , 2015 ). One of the models that has received more attention is the Three-Ring Conception of Giftedness by Renzulli (1978) . This model has helped establish some of the general criteria being used to classify students with higher abilities today. This author defined high intellectual ability as a consistent interaction between three basic human traits that characterize high-ability people: (a) above-average general intelligence; (b) creativity (defined as “that cluster of traits that encompasses curiosity, originality, ingenuity, and a willingness to challenge convention and tradition”; and (c) task commitment (which “represents a non-intellective cluster of traits found consistently in creative and productive individuals, including perseverance, determination, will power or positive energy”) ( Renzulli, 2012 ). This model has been used as a reference in Spanish schools to determine which students are gifted and which students are not gifted. In which the creativity acquiring, at a practical level, great protagonism, above-average commitment. Moreover, some studies show that gifted learners are more creative than average learners, for example, when evaluating divergent thinking or amount of original ideas ( Ferrando et al., 2008 ; Jauk et al., 2013 ).
However, this is not the only model to be considered. Other authors such as Jeltova and Grigorenko (2005) , Calero et al. (2007) , and Pfeiffer, (2012) consider high-ability children as those who demonstrate a higher likelihood of attaining significant achievements in culturally valued domains. These authors take into account a student’s intellectual abilities, while also emphasizing the relevance of certain personality traits and the role of stimulating social environments that can effectively favor an individual’s learning in specific fields. However, regardless of the theoretical model, there is agreement today that higher intellectual ability is a multi-dimensional construct, and that more human and material resources are needed to identify this often-latent potential in order to provide appropriate educational support to such students ( Tourón et al., 1998 ; Pfeiffer, 2015 ). It is therefore fundamental that schools and professionals are provided with the right tools to identify high-ability students as early as possible ( Reis and Renzulli, 2010 ).
Traditionally, intellectual ability was the central variable used to discriminate high-ability individuals from the average population. Nowadays, however, various authors agree that intellectual quotient (IQ) cannot be used as a single variable in the conceptualization of high abilities ( Calero and García-Martín, 2011 ; Pfeiffer, 2015 ). For example, as discussed by Wellisch and Brown (2012) in their study, some authors suggest that the most reliable information would be based on the perception of teachers and families. Nevertheless, IQ remains an important factor to be assessed and, when used in conjunction with other variables, it can provide essential information concerning the identification of students with exceptional abilities ( Sternberg, 2010 ; Renzulli and Gaesser, 2015 ). Moreover, many educational policies establish that, in order to implement effective identification and intervention processes, a non-negotiable criterion is to evaluate the student’s intellectual capacity by means of standardized tests ( Wet and Gubbins, 2011 ). Although other criteria may be used, there are currently authors who consider that these criteria cannot equal the objectivity and reliability of IQ measurements and tasks, especially for students with learning difficulties ( Lovett and Lewandowski, 2006 ). This broader approach to assessment is important, since the responsibility of detecting high-ability students often falls to schools, which commonly only pay attention to the more traditional signals related to high-ability, such as high levels of academic achievement. Evaluation and intervention recommendations come from teachers in most cases ( Renzulli and Gaesser, 2015 ); however, most teachers do not have a vast knowledge in the identification of high-ability students. This may lead to mistakes during the assessment process ( Tourón et al., 2006 ; Reis and Renzulli, 2010 ) and under-identification of some students, especially those from lower socio-economic backgrounds ( Moon and Brighton, 2008 ; Baker, 2011 ; Freeman, 2011 ; Wellisch and Brown, 2012 ), and/or those who have socio-emotional problems and may appear to have low levels of competence in basic learning processes (emulating students with learning difficulties) ( Silverman, 2009 ; Wellisch and Brown, 2012 ).
Therefore, although the exclusive use of standardized tests to assess intellectual ability has its detractors ( Pfeiffer, 2012 ) and these tests are not the only measures available nowadays, the fact remains that standardized tests have been accepted as reliable measures of identifying students with higher abilities to date ( Lovett and Lewandowski, 2006 ; Lovett and Sparks, 2011 ; Erwin and Worrell, 2012 ) and as Carman (2013) suggests “no matter how often researchers suggest that an IQ score is not the only way of determining giftedness, it is still the most common method of identifying gifted participants for research, either alone or in combination with other criteria.” At a practical level, in Spain the information obtained from standardized tests is the first criterion used to determine if a student may have higher abilities, and is essential for continuation of the evaluation process. This measure is used as a baseline analysis of the students’ capacities and offers a starting point for the detection of higher intellectual abilities ( Renzulli, 2012 ; Wellisch and Brown, 2012 ).
Accepting this condition as necessary, a new problem arises concerning which standardized tests to choose and the degree of congruence required between different measures. This difficulty is associated, in part, with the definition of intelligence itself and with the variables that are considered relevant to measure this construct (e.g., abstract reasoning, vocabulary, numerical knowledge). Standardized tests designed to evaluate the IQ are based on different conceptualizations of intelligence and this is an important aspect to consider when deciding which measure should be used. Some authors recommend the use of non-verbal tests to avoid cultural and linguistic biases ( Naglieri and Ford, 2003 ) such as the Factor “g” test ( Cattell and Cattell, 1994 ) or “Matrices” ( Sánchez-Sánchez et al., 2015 ), both of which are considered good estimators of fluid intelligence and general intellectual ability (or “g” factor). Other authors, in order to provide a more contextual perspective to the conceptualization of the intelligence, give greater weight to the evaluation of psychological variables relevant to the execution of school tasks, thus estimating intellectual ability by focusing on school competences rather than on purely intellectual capacities ( Thurstone and Thurstone, 2005 ). Finally, some authors state that appropriate testing should take the form of batteries of tests that also collect information on a wide range of variables that, in the last decades, have demonstrated they are good indicators of intelligence, such as students’ verbal competence, together with components such as working memory, processing speed, comprehension, analytical capacity, and so forth ( Sternberg, 2010 ; Pierson et al., 2012 ).
At this point it is worth noting the current interest in the research community in hierarchical models of intelligence and their tests, and specifically in the Cattell–Horn–Carroll Theory of Cognitive Abilities (CHC) ( McGrew, 2005 ). This theory establishes three strata in the conceptualization of intelligence: stratum III – general or global intelligence; stratum II (broad) – 10 general intelligence abilities which are the main focus of interest in the assessment of intellectual ability and are fluid and crystallized intelligence, short-term or immediate memory, long-term memory storage and retrieval, processing speed, quantitative reasoning, reacting or decision making speed, visual processing, auditory processing, reading ability, and writing ability; and stratum I (narrow) – made up of more specific components such as inductive processes, vocabulary, visual memory, spatial relations, and general sequential reasoning, and which would conform to the general cognitive factors of stratum II.
Although this theory is gradually having an impact on the evaluation and identification of higher ability students at the international level ( Pfeiffer, 2015 ), and new assessment tools are being designed or adapted based on this model (e.g., WISC-V; Wechsler, 2014 ), at a practical level, at least in Spain, it has not yet become established as a specific assessment protocol adjusted to this perspective. Therefore, both the detection model and the tests used ultimately depend on the experience and knowledge of the professionals in charge of the evaluation, and the assessment measures available in each case.
The present study had two objectives. First, following Renzulli’s (1978) model, it aimed to describe intellectual capacities and creativity levels of a sample of primary school students from northern Spain, with the aim of detecting and analysing potential cases of high ability where IQ is 130 or above – or two typical deviations above the average. Students from grades 3 and 6 were chosen as representative of this stage, and two variables of measures, intellectual capacity and creativity, were measured. Second, taking into account that depending on the tests used the students identified as gifted children may be different, this study aimed to establish the congruence and efficacies of different types of intellectual ability measures in order to determine if they concur, with respect to distinguishing students with higher abilities from average students. In schools it is common to use only a test of intellectual capacity in the processes of identification. Therefore, it is necessary to determine if these results in incorrect identification, either by over- or under-identification, due to inconsistencies between different type tests results.
In this analysis, although they are important variables in Renzulli’s (1978) model, task involvement and academic performance are not included as discriminating criteria because previous literature suggests that many students with high ability fail in the academic environment due to related factors, such as lack of motivation, and poor recognition by teachers of their real educational needs, both of which can also arise due to “teacher-bias” ( Reis and Renzulli, 2004 , 2009 ).
Materials and Methods
A sample of 236 primary school students from northern Spain took part in this study. The students were recruited from the third grade ( n = 117; 49.6%) and the sixth grade ( n = 119; 50.4%). Their ages ranged from 8 to 13 years ( M = 9.96; SD = 1.65). The ratio of males to females in the total sample was not ideal (χ 2 = 4.90; p = 0.027). There were no statistically significant differences in the percentage of students in the different grades ( p = 0.90). The ages of the third grade students ranged from 9 to 10 years ( M = 8.38; SD = 0.51), with 63 (53.8%) of the sample being female, and 54 (46.2%) being male. There were no statistically significant differences regarding gender distribution ( p = 0.405). In the case of the sixth grade students, their ages ranged from 11 to 13 years ( M = 11.50; SD = 0.55), with 47 (39.5%) being female and 72 (60.5%) being male. There were statistically significant differences between the proportion of boys and girls in this group (χ 2 = 5.25; p = 0.022).
The following instruments were administered:
Three measures traditionally used in the assessment of intelligence were used. The Test of Educational Aptitudes (TEA-1) is a test of academic competences based on a selection of the most relevant factors from the “Primary Mental Abilities” by Thurstone (1938) . The Battery of Differential and General Skills (Badyg) is consistent with the Cattell–Horn–Carroll theory (CHC) as the test is based on a hierarchical model of intelligence with three different levels. Lastly, the Factor “g” test is a non-verbal test which provides a measure of fluid intelligence (Gf) and general intellectual ability, or g factor. Due to the age of the students, two different versions of the Badyg were used. Specifically, students in grade 3 completed the Badyg-2, while students in grade 6 completed the Badyg-3. A more detailed description of these tests follows.
Test of Educational Aptitudes (adapted to Spanish by Department I+D of TEA Editions, S.A.) ( Thurstone and Thurstone, 2005 ) test provides an estimation of general intelligence and its factors. It consists of five parts that measure three different components or abilities (i.e., factors): verbal (different words and vocabulary), numerical (calculation), and reasoning (drawing and series). It also offers the possibility to measure verbal and non-verbal abilities separately. It is available in three different versions for different age groups. The TEA-1 version was used in the present study and was administered according to the age range of the sample. Reliability coefficients by mean of Cronbach’s alpha ranged between 0.61 and 0.95 for the different subtests, with an alpha of 0.89 for the full scale. The manual reports adequate internal validity, although correlations between different variables are mostly low to moderate. High correlations are only reported between verbal reasoning and academic aptitude ( r = 0.89), and between academic aptitude and numerical reasoning ( r = 0.85).
Battery of Differential and General Skills (Badyg) ( Yuste et al., 2005 ) provides an estimation of IQ and presents different versions for different age groups. Students in sixth grade completed the Badyg-E3, which consists of six subtests: (1) analog relations (verbal intelligence), (2) numerical series (inductive reasoning), (3) matrices (fluid intelligence), (4) sentence completion (inductive reasoning), (5) numerical problems (verbal intelligence), and (6) figure matching (visual processing). An overall full-scale IQ index score is also provided. Students in third grade completed the Badyg-E2. It is made up of the same subtests as the Badyg-E3 but varies in difficulty level and application time. Cronbach’s alpha was from 0.77 to 0.84 for the different subtests, and 0.95 for the full scale. The Cronbach’s alpha obtained in the present study, for the full scale, was 0.72.
While there are more powerful assessment tools to evaluate this component and with better psychometric properties, this instrument was chosen for the following reasons: (a) it can be used to predict academic performance in a reliable way; (b) it has been used in previous studies which demonstrated a relationship between intellectual ability and academic performance; and (c) factorial analysis showed high correlations between the different sub-scales that compose the Badyg battery. Criterion validity was moderate to high (Pearson’s r from 0.39 to 0.58). This scale also shows a well-adjusted factorial structure making it possible to carry out additional broad-scoped comparisons (e.g., Sabiston et al., 2013 ).
The Factor “g” test ( Cattell and Cattell, 1994 – adapted to Spanish by Associated Specialized Technicians) evaluates intelligence conceived as a general mental ability. It uses non-verbal tasks to eliminate the influence of those abilities that have been acquired through education, such as vocabulary or numerical knowledge. This test has three versions, each with different difficulty levels. The selection of the level depends upon the age of the participant. Level 2 (suitable for children from 8 to 14 years) was used in the present study. It includes four subtests: series, classification, conditions, and matrices. Individual scores are combined to obtain a global IQ score. The participant is asked to establish logical relationships between abstract figures and forms.
Cronbach’s alpha ranged between 0.76 and 0.85 for the different subtests (alpha = 0.86 for the full scale), with a complementary index adequate stability of 2.59 (typical measurement errors). Criterion validity was high, finding statistically significant correlations between the different sub-scales and the Test of Educational Aptitudes-TEA 1 and 2 (Pearson’s r from 0.53 to 0.81; p < 0.001).
The Creative Intelligence Test (CREA) ( Corbalán et al., 2003 ) presents participants with an image (commonly representing a social scene) and they have a limited time frame to formulate all the questions that the situation evokes in them. Version C, which is aimed at children, was used. In addition to providing a global measure of creativity, it offers the possibility to analyze the results qualitatively. Three levels of creativity can be established based on percentages (low = below the 25th percentile; medium = 26th–74th percentiles; and high = 75th percentile and above).
Students were recruited from different schools in Northern Spain. Once the schools were selected, principals and head teachers of the participating schools were contacted. They were informed about the aims of the study, its voluntary nature and anonymity, and the ethical treatment of the data recorded. The study was conducted in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki), which reflects the ethical principles for research involving humans ( Williams, 2008 ). Informed consent from families was also obtained. Researchers who were trained in psychology administered the above tests, all of which were conducted using counter-balanced methodology over the course of the testing, in three different testing sessions. Students with severe learning difficulties or special educational needs were excluded from the analyses.
A descriptive design was used. Due to the objectives of this study, statistical analyses were performed in different steps. First, the sample was described in terms of age, gender, IQ (based on the three measures of intelligence previously described), and creativity. This analysis was conducted separately for students in grade 3 and 6, as different versions of the Badyg were used. The normality of the dependent variables (i.e., global scores in the CREA, Badyg, TEA-1, and Factor “g” test) was analyzed, paying special attention to skewness and kurtosis values. Following Finney and Di Stefano’s (2006) criterion, the adequacy of these values was demonstrated ( Table Table1 1 ). Secondly, to estimate the correspondence between the different measures of intellectual ability, Pearson correlation between global IQ scores were conducted.
Descriptive statistics for the sample (third and sixth grade students).
Additionally, student’s t -test was also performed to analyze within-subject differences in IQ estimated with the different tests. To analyze the discriminatory capacity of each test in the detection of students with high abilities, the absolute frequency of students with an IQ of 130 or higher (as determined by the different tests) was then calculated. The congruence among the three intelligence tests was estimated by recording the number of students who were found to have an IQ of 130 or above in all the tests. Congruence between pairs of tests in the detection of high-ability students was also established. Although considering an IQ of 130 or above – or two typical deviations above the average – seems to be an arbitrary criterion, in both research and educational practice this criterion is still used, in most cases, as a cut-off point to determine which students have higher intellectual abilities ( Moon and Brighton, 2008 ; Carman, 2013 ; Guignard et al., 2016 ; Peyre et al., 2016 ).
Intellectual IQ Results of the Students and Correspondence between Measures
Table Table1 1 shows descriptive statistics for the sample, while Table Table2 2 presents correlations between IQ scores measured using the different tests of intellectual ability described. Analyses for students in grade 3 and 6 are presented separately.
Bivariate correlations between IQ scores in the different tests (third and sixth grade students).
Third Grade Students
As Table Table1 1 shows, 59% of the students in grade 3 had a medium level of creativity, while only 19% reached high levels of creativity. However, the mean in this variable suggests low levels of creativity in general (values in this variable can range from 0 to 25).
Results from the intelligence tests administered placed the intellectual ability of the group around the average, regardless of the test used. Scores were slightly higher in the case of the Factor “g” test (i.e., fluid intelligence). Standard deviations were high, suggesting the presence of large inter-subject variability. IQ values ranged from 68 to 149 points in the case of the Factor “g” test, between 65 and 135 in the TEA-1, and between 64 and 139 in the Badyg-2. The correlations between the various measures of intellectual ability were positive and statistically significant between all pairs of tests (see Table Table2 2 ). Statistically significant differences between IQ scores estimated with Factor “g” test and Badyg-2 ( t = 5.369; p < 0.001), and between Factor “g” test and TEA-1 ( t = 4.964; p < 0.001) were found, but not between the Badyg-2 and TEA-1 ( p = 0.866). Thus, statistically significant differences were found when the crystallized and fluid intelligence measures were compared, with students’ IQ scores being higher when using the latter measure.
Sixth Grade Students
Results show that students in this group obtained higher scores in CREA than the younger students. However, the scores varied from a minimum of 4 to a maximum of 20 in this variable. Again, the proportion of students with medium creativity was greater than the proportion of students with low and high creativity. However, the percentage of students with high levels of creativity was greater than in the third grade students group (see Table Table1 1 ).
Regarding the variable IQ, sixth grade students showed average levels of intelligence, although a large within-subject variability was observed. IQ scores ranged from 30 to 139 points when the Factor “g” test was used, from 55 to 136 in the case of the Badyg-3, and from 65 to 135 when the TEA-1 was administered. Correlations between the different measures were positive, but only statistically significant when using the Badyg-3 and TEA-1 (see Table Table2 2 ). At a within-subject level, statistically significant differences in IQ scores were observed when the Factor “g” test and Badyg-3 were compared ( t = -2.529; p = 0.013), as well as between the Factor “g” test and TEA-1 ( t = -4.237; p < 0.001), and between the Badyg-3 and TEA-1 ( t = -4.092; p < 0.001). Students in grade 6 obtained better results in the TEA-1 than in the other tests.
Discriminatory Values of the Measures in the Detection of Students with High Abilities, and the Intellectual Measures of the Students Detected
To detect students that could be considered high-ability and determine the congruence between the tests, a selection of cases in which a student scored 130 or above in the different IQ tests was made. Results are presented according to school grade ( Tables Tables3 3 , 4 4 ).
Descriptive statistic of participants with an IQ equal or above 130 in the different tests (third grade students).
Descriptive statistic of participants with an IQ equal or above 130 in the different tests (sixth grade students).
None of the students in this group obtained an IQ score of 130 or above in all three of the tests. However, scores from the Factor “g” test and Badyg-2 converged in two cases. With respect to the other possible paired-comparisons of the tests, there were no instances of converging results ( Table Table3 3 ). These students showed a medium-to-high percentile in the creativity test and a mean IQ of 139 points in the Factor “g” test, with values ranging from 132 to 146 points. They also exhibited a mean of 137.5 points in the Badyg-2, with values between 136 and 139. Regarding IQ assessed by the TEA-1, values were close to 130, ranging from 119 to 128 points.
For students who had an IQ of 130 or greater in only one of the tests, it can be observed that the Factor “g” test identified the highest number of students who met this criterion (13 students), while the TEA-1 was the most restrictive test with only one student identified. The Badyg-2, however, detected six students who met the above-mentioned criterion. It should be noted that, in the majority of cases, students identified as having high-abilities showed a medium level of creativity. Finally, IQ scores ranged between 130 and 149 points when measured by the Factor “g” test, and between 130 and 139 in the case of the Badyg-2. A unique value of 135 was found in the TEA-1.
Again, none of the students met the criteria of having an IQ equal or above 130 points in all three of the tests. Regarding the convergence between pairs of tests, the Factor “g” test and the TEA-1 converged, but only in a single case. The results of this student can be seen in Table Table4 4 . He showed a high level of creativity and his IQ was close to 130 when the Badyg-3 was administered.
In relation to students scoring 130 or above in each of the tests, results indicated that the TEA-1 was the test that identified the greatest number of students that met this criterion followed by the Factor “g” test. The Badyg-3 was the most restrictive test in this sense, as none of the students showed an IQ score equal to or higher than 130 in this test. Table Table4 4 presents the results corresponding to each group. In this case, 50% of the children identified as high-ability students in the different tests displayed a high level of creativity. This pattern was different from that found in the group of third grade students, where only 2 out of 20 (10%) of the students identified as having high-abilities showed high levels in this variable. IQ values ranged from 130 to 139 in the case of the students identified by the Factor “g” test, whereas all the students identified by the TEA-1 showed an IQ of 135. The student identified by the Badyg-3 had an IQ of 136.
In summary, out of the total of 236 students, 31 students (20 from third grade and 11 from sixth grade) were identified as having an IQ equal to or greater than 130, considering the different tests separately. This corresponds to 13.13% of the sample. There were only three cases in which two tests produced converging results, which equates to only 1.27% of all students evaluated. No convergence of results was found among the three measures of intelligence.
This study has two main objectives: analysing the relationship between intellectual capacities of a group of third and sixth grade students from Northern Spain; and to analyze the discriminatory value and congruence between different tests of intelligence traditionally used in the identification of high-ability students. In general, results point to the need to use different tests in the identification process, as well as to include complementary measures (i.e., creativity) to create a multi-criterial system for the detection of students who fall into this category ( Renzulli, 2012 ).
Intellectual Capacities Results of the Students and Congruence among Measures
In general, results indicated that both third and sixth grade students showed an average intellectual ability (close to 100 in most of the cases). Regarding congruence among the different intelligence measures used, it is important to note that all the tests administered to third grade students showed positive and significant correlations to one another. A moderate to high association between the Factor “g” test and the tests of educational and intellectual aptitudes, more related to academic performance (TEA-1 and Badyg), was found. However, for sixth grade students, significant correlations were only found between the Badyg-3 and TEA-1 (both assess general intelligence through those abilities related to learning and academic performance – or crystallized intelligence). Thus, when the same tests were administered to older students, the correlation between crystallized intelligence measures increased, while the association between crystallized and fluid intelligence measures decreased, or even disappeared. These results are consistent with those reported by Pérez and González (2007) , who noted that the subscales with a greater cultural basis (and containing more elements of the school curriculum) functioned differently according to age, and showed more congruence as children grow up (and presumably as their knowledge increases).
In addition, regarding the accuracy of the tests detecting high-ability students, it should be noted that the congruence among the various measures examined was disturbingly low. In this sense, none of the students met the criterion of showing an IQ equal to or above 130 in all of the three measures that were administered in a concurrently. On the other hand, considering the different tests separately, 13.13% of the total sample corresponds with students who were identified as having an IQ equal to or greater than 130, when theoretical percentage expectation would be around 2%. Differences in the estimations provided by the different tests for a same student were high. This may point to important constraints regarding the validity of the tests that are being currently being used.
It could thus be assumed that, at earlier stages of development, the different types of intelligence tests can converge, with respect to findings. However, this convergence tends to decrease with age, and congruence only stays present in cases in which those abilities have been facilitated and boosted by on-going learning. These findings have some implications for practice. Specifically, the lack of congruence among intelligence measures (such as that identified in this study) may lead to misdiagnosis, preventing some students from receiving adequate support for their exceptional needs. Likewise, it is appropriate to highlight the need to use different tests of fluid and crystallized intelligence in the identification of high-ability students, always taking into consideration the students’ cognitive developmental stages.
Discriminatory Value of the Measures Identifying High-Ability Students and Intellectual Results Differences of the Students Detected
It is necessary to highlight that a reliable evaluation is the basis for an early detection and tailored intervention, and that currently one of the most important concerns regarding higher abilities is that these students often do not receive recognition, and thus appropriate intellectual stimulation, at least in Spain ( Calero and García-Martín, 2014 ). This can lead to a lack of interest, frustration, and failure at school, as well as have a negative effect on the development of self-worth and social acceptance ( Kroesbergen et al., 2016 ) or result in behavioral problems in some cases. On the other hand, a false positive may push students toward overly demanding and frustrating processes that may exceed the limits of their capacity. A total of 31 students in the current study presented an IQ equal or above 130 when the different tests were used separately, which corresponds to 13.13% of the sample. This infers a clear over-estimation of high-ability students, if the acknowledged distribution of IQ in the general population is to be taken into account. When convergence between any two tests was considered, only three students were identified as being high-ability children, which corresponds to only 1.27% of the total sample.
In terms of creativity, students in sixth grade showed higher scores in this variable in comparison to third grade students. This result suggests that creativity may increase as students progress through the different stages of schooling, and draws attention to the need for researchers to conduct more comprehensive studies on what type of teaching methods favor or hinder creativity in the classroom.
It is also worth noting some differences in the functioning of the tests according to grade level. Regarding third grade students, results suggest that the Factor “g” test may be less restrictive than the other tests when it comes to detecting potentially higher-ability students, whereas the TEA-1 may be the most liberal in this sense, identifying the greatest number of higher-ability students. However, the discriminating power of the tests in the case of sixth grade students was different. Specifically, the Factor “g” test and TEA-1 tests were the most and the least restrictive tests, respectively. Again it seems that the tests that measure fluid intelligence and those which measure crystallized intelligence operate differently at different developmental stages (see Figure Figure1 1 ).
Results of the different measurements for groups with IQ higher and lower than 130 (depending on whether the IQ selection was made using g Factor , Badyg or TEA-1).
With respect to the students’ intellectual variables, results indicated that a high IQ is not necessarily accompanied by high creativity, which has already been demonstrated in previous research ( Kim, 2005 ; Marugán et al., 2010 ; Guignard et al., 2016 ). In the case of third grade students, 20 participants were identified as high-ability children by at least one of the tests. However, only two of them demonstrated high levels of creativity. Among the sixth grade students, only six of the 11 who were identified as high-ability students also displayed high levels of creativity. Studies carried out with large samples of Spanish students, such as that of Castejón et al. (2016) , show how in classrooms, although gifted students are equally categorized, not all of them show the same cognitive-motivational profiles. In this way, there are students who exhibit higher scores on creativity and lower scores on general mental ability or self-regulation learning strategies (the group called by these authors as “creative gifted”) and there are student profiles that do not show special ability in this variable; for example, students called “gifted achievers,” who show high scores in self-regulation learning variables and academic achievement, and lower scores in creativity; or students called “cognitive gifted” who get high scores in general mental ability only.
In summary, and as Heller (2004) , Ziegler and Stoeger (2010) , and Wellisch and Brown (2012) have pointed out, the use of different tests of intellectual ability in the identification of high-ability students is necessary. Otherwise, this process may be biased. Furthermore, including additional measures not directly related to intellectual ability, such as creativity, would help establish a more detailed profile of the students and thereby assist in identifying their additional strengths and weaknesses. This is even more important in countries such as Spain, where most of the detection protocols available today, although multidisciplinary, still use a single measure of intellectual ability as a starting point to identify those students at a higher level of ability ( Hernández and Gutiérrez, 2014 ). In this sense, it would be necessary to continue analysing the correspondence between different assessment tests, as well as between different measures of creativity, in order to better delimitate to what extent the tests provide a coherent and comprehensive profile of the students’ intellectual abilities.
Finally, some limitations should be acknowledged in relation to the present study. Firstly, the sample size was somewhat limited and also geographically localized, which may pose some constraints concerning generalization of the results. It would be necessary to expand the study sample to include a large number of gifted children and determine if the results obtained on the lack of congruence between the tests are maintained. In the current study, the percentage of students with scores above 130 IQ points appears biased toward the distribution or congruence between the measures. Secondly, future studies may consider the benefits of including additional variables in research of this kind, such as motivation, personality, learning styles, socio-cultural conditions, and/or students’ affective-emotional states. These additions to the methodology utilized in the present study would undoubtedly enhance the results of any future investigations of the multidimensional construct widely known as “higher ability” ( Reis and Renzulli, 2010 ; Sternberg, 2010 ; Hernández and Gutiérrez, 2014 ). Finally, although through different tests, the same construct (IQ) has been evaluated. Thus, the possibility of an average regression effect or profiles with cluster latent analysis, which is common when evaluating students in a short period of time, has to be considered. It would be interesting to extend the time between evaluations in order to control for this effect in future research.
This study was carried out in accordance with the recommendations of University of Oviedo with written informed consent from the parents of all participants. All parents gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the University of Oviedo.
EF, TG, and CR have participated in the design, analysis and drafting of the paper. OA-G, and AV have participated in the application of the measures and drafting of the paper.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This work has been supported by a project of the Principality of Asturias (FC-15-GRUPIN14-053).
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- News Features
- Brain & Behavior
What Does IQ Really Measure?
A new study argues that the score reflects both motivation and intelligence.
- 25 Apr 2011
- By Michael Balter
Kids who score higher on IQ tests will, on average, go on to do better in conventional measures of success in life: academic achievement, economic success, even greater health, and longevity. Is that because they are more intelligent? Not necessarily. New research concludes that IQ scores are partly a measure of how motivated a child is to do well on the test. And harnessing that motivation might be as important to later success as so-called native intelligence.
Researchers have long debated what IQ tests actually measure, and whether average differences in IQ scores--such as those between different ethnic groups--reflect differences in intelligence, social and economic factors, or both. The debate moved heavily into the public arena with the 1994 publication of The Bell Curve by Richard Herrnstein and Charles Murray, which suggested that the lower average IQ scores of some ethnic groups, such as African-Americans and Hispanics, were due in large part to genetic differences between them and Caucasian groups. That view has been challenged by many scientists. For example, in his 2009 book "Intelligence and How to Get It," Richard Nisbett, a psychologist at the University of Michigan, Ann Arbor, argued that differences in IQ scores largely disappear when researchers control for social and economic factors.
New work, led by Angela Lee Duckworth, a psychologist at the University of Pennsylvania, and reported online today in the Proceedings of the National Academy of Sciences explores the effect of motivation on how well people perform on IQ tests. While subjects taking such tests are usually instructed to try as hard as they can, previous research has shown that not everyone makes the maximum effort. A number of studies have found that subjects who are promised monetary rewards for doing well on IQ and other cognitive tests score significantly higher.
To further examine the role of motivation on both IQ test scores and the ability of IQ tests to predict life success, Duckworth and her team carried out two studies, both reported in today's paper. First, they conducted a "meta-analysis" that combined the results of 46 previous studies of the effect of monetary incentives on IQ scores, representing a total of more than 2000 test-taking subjects. The financial rewards ranged from less than $1 to $10 or more. The team calculated a statistical parameter called Hedge's g to indicate how big an effect the incentives had on IQ scores; g values of less than 0.2 are considered small, 0.5 are moderate, and 0.7 or higher are large.
Duckworth's team found that the average effect was 0.64 (which is equivalent to nearly 10 points on the IQ scale of 100), and remained higher than 0.5 even when three studies with unusually high g values were thrown out. Moreover, the effect of financial rewards on IQ scores increased dramatically the higher the reward: Thus rewards higher than $10 produced g values of more than 1.6 (roughly equivalent to more than 20 IQ points), whereas rewards of less than $1 were only one-tenth as effective.
In the second study, Duckworth and her colleagues analyzed data from an earlier study of more than 500 boys from Pittsburgh, Pennsylvania, whose IQs were tested in the late 1980s by a team from the University of Wisconsin, Madison. During the IQ test, the boys, whose average age was 12.5 years, were videotaped; then observers trained to detect signs of boredom and lack of motivation (such as yawning, laying their heads on the table, or looking often around the room) viewed the videos and assigned motivation scores.
Researchers followed the boys over time, and when the boys reached early adulthood (average age 24), 251 of them agreed to a series of interviews about their educational and job achievements (there were no differences in IQ or other key factors between those boys who participated and those who didn't.)
Duckworth's team analyzed the results of these earlier studies to see what they said about the relationship between motivation, IQ scores, and life success. By constructing a series of computer models of the data, the team found that higher motivation accounted for a significant amount of the differences in IQ scores and also in how well IQ predicted later success in life. For example, differences in motivation levels accounted for up to 84% of the differences between the boys in how many years of school they had completed or whether they had been able to find a job. On the other hand, motivation differences accounted for about only 25% of the differences in how well they had done in school as teenagers. According to the researchers, that suggests that native intelligence does still play an important role in both IQ scores and academic achievement.
Nevertheless, the Duckworth team concludes that IQ tests are measuring much more than just raw intelligence--they also measure how badly subjects want to succeed both on the test and later in life. Yet Duckworth and her colleagues caution that motivation isn't everything: The lower role for motivation in academic achievement, they write, suggests that "earning a high IQ score requires high intelligence in addition to high motivation."
The study has important social policy implications, Duckworth says. "I hope that social scientists, educators, and policy makers turn a more critical eye to any kind of measure, intelligence or otherwise," she says, adding that how hard people try "could be as important to success in life as intellectual ability itself." Duckworth suggests that admissions to programs for "gifted and talented" children should not be based on IQ scores alone, but also on "who wants to do the work."
Nisbett agrees that the study is "tremendously important in its implications." Motivation, along with self-discipline, "are crucial," Nisbett says. "A high IQ and a subway token will only get you into town."
Lex Borghans, an economist at the Maastricht University in the Netherlands, who has also studied the relationship between intelligence tests and economic success, says the new report shows that "both intelligence and personality matter." Even if native intelligence cannot be increased, Borghans says, "there might be other routes to success."
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Why Marketers Are Returning to Traditional Advertising
- Christine Moorman,
- Megan Ryan,
- Nader Tavassoli
Seven reasons companies are reinvesting in TV, radio, and print ads.
Pundits have long predicted the demise of traditional advertising. However, it is alive and well and headed for growth for the first time in a decade. The authors explain seven factors driving this trend, including the ability of traditional ads to break through digital clutter, the decline in third-party cookies, and more.
Digital marketing technologies and their ecosystems have dominated growth in marketing budgets for over a decade. As consumers have shifted their attention from stationary media to perpetual media on the go, traditional advertising lost some of its appeal. In turn, marketers pivoted investments from television, radio, newspaper, events, and outdoor advertising to digital channels, from TikTok to TechTarget.
For the last decade, marketers have consistently predicted that their traditional advertising spending would decline. According to data from the 28th Edition of The CMO Survey , on average, marketers reported an annual decrease in traditional advertising spending of -1.4% between February 2012 and 2022, compared to an annual increase of 7.8% for overall marketing budgets during this same period.
However, recent evidence suggests that a shift is underway. In contrast to the historical trend, in August 2021 and February 2022, marketers predicted that traditional advertising spending would increase by 1.4% and 2.9%, respectively.
Consumer-facing companies are leading the shift, with B2C service companies predicting the largest increase in traditional advertising spending (+10.2%), followed by B2C product companies (+4.9%). Further, and somewhat ironically, companies that earn 100% of their sales through the internet are leading this inflection — predicting an 11.7% increase in traditional advertising spending over the next 12 months.
So, why is traditional advertising on the rise, and will the trend continue? We see seven drivers behind the shift.
1. Breaking through the digital clutter .
As consumers are spending most of their waking hours online, it seems they are becoming increasingly numb to conventional digital advertising and engagement. They report frustration and negative brand association with digital advertising clutter that prevents them from reading an article, watching a video, or browsing a website. For example, a HubSpot survey found that 57% of participants disliked ads that played before a video and 43% didn’t even watch them. As a result, marketers are looking for a way to cut through the noise.
Traditional ads, on the other hand, are experiencing increased engagement. MarketingSherpa reports that more than half of consumers often or always watch traditional television advertisements and read print advertisements that they receive in the mail from companies they are satisfied with. Indeed, research by Ebiquity suggests that traditional media channels — led by TV, radio, and print — outperform digital channels in terms of reach, attention, and engagement relative to costs. This performance differential is amplified as costs of online advertising have increased, especially when accounting for impression, click, and conversion fraud — whereas the costs of traditional media have fallen. It simply makes good economic sense to rebalance spending away from digital clutter.
2. Capitalizing on consumers’ trust in traditional advertising .
The same MarketingSherpa survey found that the top five most trusted advertising formats are all traditional, with customers trusting most print advertising (82%), television advertising (80%), direct mail advertising (76%), and radio advertising (71%) to make purchasing decisions. Similarly, it found that British and American consumers trust traditional advertising such as television, radio, and print more than social media advertising. As a result, marketers can use traditional advertising to build brand credibility and trust with jaded buyers.
3. Preparing for the decline of third-party cookies .
For years, marketers have relied on third-party cookies to track website visitors, using detailed data on their search preferences to improve the user experience and target consumers with personalized ad experiences. However, with Google phasing out the third-party cookie on Chrome browsers by late 2023 and Apple implementing changes to its iOS14 operating system, the death of third-party cookies is imminent. The CMO Survey found that 19.8% of companies invested more in traditional advertising (outside of online approaches) as a result.
Because of this inevitable change to the advertising landscape, marketers will be forced to rely on segmentation methods that hew closer to traditional advertising models. Without advanced data-driven targeting, marketers will need to refocus on extending their reach.
4. Tapping the growing medium of podcasting .
Podcasts are a form of digital media. However, unlike banner, display, and other social advertisements that often appear within consumers’ everyday browsing, podcasts use an on-demand approach that is more similar to traditional radio. And this is one reason advertising succeeds. According to Ads Wizz , “Podcasts saw a 51% increase in available inventory, a 53% increase in new podcasts, and an 81% increase in podcast ad impressions.”
In addition to reaching over 100 million monthly listeners, podcast ads are effective because listeners trust their podcast hosts and are genuinely influenced by their endorsements. In fact, Edison Research’s Super Listeners 2020 study found that 45% of podcast listeners believe the hosts of their favorite podcasts actually use the brands mentioned on their shows. According to the same study, almost half of podcast listeners pay more attention to podcast advertisements than those of any other format. Given the match of target market to podcast content, podcasting has proven to be an effective way to get a company’s brand in front of a well-suited and attentive audience.
5. Exploiting the digital lift of traditional media .
Digital technology can leverage traditional tools in powerful and surprising ways. For example, who would have thought that direct mail would be resurrected? That’s exactly what happened when mailers are paired with a QR code that consumers can scan to learn more. Furthermore, as Madison Taylor Marketing shares, unique URLs and QR codes allow marketers to gather extremely granular data, permitting them to develop robust marketing analytics regarding ROI and attribution, and eroding the advantage of digital channels.
6. Fine-tuning brand and market fit .
Marketing is an art and a science of contingencies and context. This means that sometimes traditional advertising is a perfect fit for some brands, markets, and messages. For example, broadcast TV continues to offer an ideal platform for emotional storytelling ads, such as the clever “Welcome Back” Guinness ad that marked the reopening of pubs and restaurants following the Covid-19 lockdown. New addressable TV solutions, such as by Finecast , now enable advertisers to precision target viewer segments across on-demand and live-streamed TV, thereby eroding the targeting advantage of online channels.
7. Revisiting digital effectiveness.
The CMO Survey showed that 54.8% of marketers track digital marketing performance in real time, with an additional 35.2% doing so quarterly or weekly. At the same time, marketers are also becoming skeptical of the hyped returns of digital media, because the platforms control both the advertising inventory and its effectiveness measurement. This has raised credibility concerns related to ad fraud and the worry that digital advertising may be far less effective than reported.
The digital promise of hyper-targeting and personalization is also under scrutiny. For example, recent academic research by Jing Li and colleagues published in the Journal of Marketing shows that retargeting can actually backfire if done too early. And research in computer science has shown that personalization can lead to consumer reactance, especially when consumers are unfamiliar with the brand. In short, marketers are learning that the advantages of digital media can be a double-edged sword and are becoming more cautious about blindly embracing it.
Pundits have long predicted the demise of traditional advertising. However, it is alive and well and headed for growth for the first time in a decade. When used together, traditional and digital marketing can reach more audiences, build and keep trust, and motivate buying from consumers who otherwise might tune out marketing messages.
- Christine Moorman is the T. Austin Finch, Sr. Professor of Business Administration, Fuqua School of Business, Duke University. She is founder and director of The CMO Survey .
- Megan Ryan is an MBA candidate at Duke University’s Fuqua School of Business (Class of 2022). She is a research fellow for The CMO Survey and also represents Fuqua as a McGowan and Forté Fellow.
- Nader Tavassoli is a professor of marketing at London Business School and academic director of the Leadership Institute at London Business School. He is the UK director of The CMO Survey .
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The Flynn effect refers to the consistent upward drift in IQ test scores across generations which has been documented to be approximately 3 points per decade.
From: WISC-V (Second Edition) , 2019
- Intelligence Testing
- Working Memory
- Raven's Progressive Matrices
- Intellectual Disabilities
- Bayley Scales of Infant Development
- Cognitive Ability
- Fluid Intelligence
- Processing Speed
- Wechsler Intelligence Scale for Children
The Flynn Effect and Its Clinical Implications
Jacques Grégoire , Lawrence G. Weiss , in WISC-V (Second Edition) , 2019
The FE refers to a consistent upward drift in intelligence test scores across generations of about three points per decade. This occurs when a contemporary examinee’s test performance is compared to norms derived from a previous generational cohort when the average performance was lower than it is today. The effect is widely accepted as real, and typically thought to be due to various societal improvements that enhance development of the brain and nervous system such as nutrition, healthcare, and education. The effect is corrected when the test is revised and the norms reanchored to reflect the performance of the current generation.
The size of the FE varies for different broad cognitive abilities and is lowest for crystallized knowledge and highest for fluid reasoning tasks. This makes it difficult to measure the true size of FE when comparing the FSIQ scores from different editions of a test, such as the WISC-IV and WISC-V, because the FSIQ is comprised of different blends of the broad cognitive abilities across editions of the test. When comparing WISC-IV and WISC-V scores, the FE is much smaller than the three points per decade predicted by the FE theory, which is believed to be due to changes made in the composition of the FSIQ between these two editions of the test. When holding test edition constant by comparing WISC-IV scores of a contemporary sample to matched controls from the original standardization sample, the FE is again found to be about three points per decade. For these reasons, it is not yet possible to measure the FE for WISC-V unconfounded by content changes between test editions, until enough time has passed since the standardization of the WISC-V.
We recommend that FE adjustments be reserved for high-stakes legal evaluations with potential life-changing consequences. In these situations, the empirically determined FE for the specific test administered should be applied. If empirical data is not yet available, such as is currently the case for the WISC-V, then the theoretical estimate of three points per decade can be applied until such time as empirical data are available.
FE adjustments are not recommended for most routine clinical evaluations. Rather, it is recommended that practitioners always use the most recent version of a test, and report the confidence interval surrounding the obtained test score. Routine use of confidence intervals when reporting test scores is widely viewed as best practice. In most situations, the 90% confidence interval will be wider than the FE adjustment, making an FE adjustment unnecessary.
Jacques Grégoire , ... Lawrence G. Weiss , in WISC-V Assessment and Interpretation , 2016
Variation of the Flynn Effect Over Time
The FE does not occur abruptly when new norms are published. It appears gradually, beginning upon publication of new norms. As the characteristics of the population change inexorably, while norms remain fixed, the gap between the two continues to widen between each standardization and the next one. Norms become more and more lenient across time, until the day new norms are set up. Such an evolution of norm validity is a problem for clinical practice, since the value of the cut scores lessens over time and the number of misidentified individuals increases. To solve this problem, Flynn (1998b) suggested correcting the old norms on the basis of an annual change of the average IQ level of the population of .25 points. This adjustment value is a rough estimate based on past observations of the FE, postulating a linear evolution of the population average IQ across time and into the future.
But several recent findings seem to indicate that the shape of the FE could no longer be linear. The FE could have reached a ceiling with the consequence that the future evolution of the population average IQ would be flat. In Table 6.1 , reporting Flynn’s data collected in Norway, we have already seen that the rate of the FE was slower from 1968 to 1980, compared to the trend from 1954 to 1968. Sundet, Barlaug, and Torjussen (2004) reported more recent data from Norwegian conscripts. They observed that the mean scores of the conscripts on a Raven’s-like test stopped increasing in the mid-1990s, and even slightly decreased. Similar observations were made in Denmark ( Teasdale & Owen, 2007 ), Sweden ( Rönnlund, Carlstedt, Blomstedt, Nilsson, & Weinehall, 2013 ) and Finland ( Dutton & Lynn, 2013 ). Two explanations were proposed for the slowdown of the FE in the Nordic countries of Europe. It could be partly attributable to the non-European immigrants with lower education who settled in the north of Europe from the end of the 1960s ( te Nijenhuis, de Jong, Evers, & van der Flier, 2004 ). It could also be the consequence of dysgenic fertility ( Nyborg, 2012 ), i.e., the negative association between fertility and IQ, the families with low IQ having more children than the families with high IQ. The first explanation does not apply to Finland ( Dutton & Lynn, 2013 ) and, consequently, cannot be generalized. The second one is more speculative and needs more empirical data to be supported. Another approach to explaining the FE curve is to consider it as a consequence of several interlinked factors (see “Causes of the Flynn effect ,” below). These factors are stimulating the development of individual intellectual potential, but their positive influence is likely not infinite and is, therefore, gradually coming to an end. According to a traditional proverb in the stock market: “Trees don’t grow to the sky.” As a consequence, the shape of the FE is likely to be curvilinear.
The Flynn Effect and the Wechsler Scales
Xiaobin Zhou , ... Jianjun Zhu , in WAIS-IV Clinical Use and Interpretation , 2010
What causes the Flynn effect?
The variability of the Flynn effect suggests that either the population IQ change is not a simple phenomenon that can be explained by a single factor, or, if it is the result of a single factor, the influence of this factor is different in different subgroups of the population or in different domains of ability. Researchers have studied some factors (for example, is it a real IQ gain due to social, population, or genetic factors, or is it simply a psychometric artifact?) that may have played a role inside the “black box” behind the Flynn effect. As significant genetic modifications of a population only occur over very extended periods, the IQ gain observed during a 50-year period cannot be explained by modification of the genetic characteristics of Western populations. Several other hypotheses have been proposed to explain this effect, but, considered separately, none of them provided a sufficient explanation. The Flynn effect seems rather to be a consequence of several interrelated factors.
Educational progress during the twentieth century seems to be a strong factor underlying the Flynn effect. Several studies have shown the impact of schooling on intelligence ( Ceci & Williams, 1997 ). During the last century, in industrialized countries, schooling became compulsory. The percentage of children attending secondary and higher education programs strongly increased. In the same period, the percentage of illiterate people dropped sharply. Moreover, a growing number of children attended nursery school regularly, with a clear impact on their intellectual development ( Fernandez-Ballesteros & Juan-Espinosa, 2001 ). Barber (2005) analyzed the relationship between schooling and IQ using data collected in 81 countries. He observed that the intellectual differences between countries were mainly related to literacy rate, attendance at secondary school, and agricultural population percentage. In more agricultural societies working procedures are often traditional, changing slowly. Individuals can therefore rely on what they learn in their younger years for their whole occupational life. On the other hand, in industrialized countries lifelong learning is necessary. Because of the growing impact of technology in everyday life, people continually have to learn how to use new appliances and procedures. In industrialized countries, working requires fewer and fewer physical aptitudes, but more and more intellectual ones ( Fernandez-Ballesteros & Juan-Espinosa, 2001 ).
In the past 50 years, education has also changed within families. Increase in family incomes, elevation of parents' educational level, and diminution of the average number of children per family have profoundly modified children's education. Parents now have more time and money to spend on their children's education. They also have more information about the normal development of a child and how to stimulate it. Espy, Molfese, and DiLalla (2001) conducted a longitudinal study on 105 children aged from 3 to 6. They confirmed that positive educational conditions within the family support the development of the components of intelligence.
Although, overall, education levels have improved over time, the rate of this development is not equal in all countries. Some developing countries, such as China, are catching up with developed countries at a fast pace. At the same time, in developed countries a high percentage of the population has already reached a high degree of educational attainment, and thus the rate of educational development is slowing down. Thus, if education is a key underlying factor in the Flynn effect, we would expect the IQ increase to be more prominent in developing countries than in developed countries. In fact, this pattern is supported by a comparison of the recent standardization data for the WISC-IV in the US, mainland China, and Hong Kong. Among these three samples, the US population has the highest education level (i.e., about 85 percent of the 6- to 16-year-olds have parents with at least a high school education), and the Flynn effect is 0.25 points per year. In mainland China, only 54 percent of parents of children within this age band have a high school education, and the Flynn effect using the WISC (China) is 0.59 per year ( Wechsler, 2008a ). The parental education level in Hong Kong is between that in the US and that in China, but is more similar to that in the US (that is, about 85 percent have a high school education, although a smaller percentage attended college compared to the US). The Flynn effect there is 0.30 per year ( Wechsler, 2010 ). Thus, not only may the improvement in education have caused an increase in the population's intelligence, but the changing pace of this improvement may also have caused variations in the magnitude of the Flynn effect in different countries and at different periods in a country's development.
Another important factor underlying the Flynn effect is the considerable improvement in bioenvironmental conditions of life since the end of World War II. Bioenvironmental conditions refer to the interactions between the environment and individual biophysical characteristics. For more than half a century, in developed countries, improvements in health and nutrition had a considerable impact on physical development. In these countries, infant mortality was falling rapidly, while the average individual height was growing regularly. Schmidt, Jorgensen, and Michaelsen (1995) observed such an increase in average height, between 1960 and 1990, among conscripts in 11 European countries. During this period, the average height of Dutch conscripts increased from 1.72 m to 1.77 m (i.e., from 5'6′′ to 5'8′′). They also observed a close relationship between this increase in height and the reduction in infant mortality during the first month of life. This reduction in mortality is itself related to nutritional improvement and a decrease in infectious diseases. It is plausible that factors related to height increase also had an impact on intellectual development. Tuvemo, Jonsson, and Persson (1999) conducted a study on 32,887 conscripts, and observed a significant relationship between their height and their intellectual performances. Several studies regarding children's malnutrition have clearly shown the impact of insufficient nutrition on both physical and intellectual development ( Pollit, 2000 ). When nutrition and health care improve, an increase in intellectual abilities is also observed in the population ( Daley et al. , 2003 ). However, the importance of nutrition and health on intelligence is still under debate ( Flynn, 2009 ), and the relationship between these variables is probably not linear.
Based on Bronfenbrenner's bioecological model, Dickens and Flynn (2001) proposed an integrative interpretation of the Flynn effect. Innate cognitive abilities would benefit from positive environmental conditions, which could stimulate their development. Individuals could then look for the most favorable environment that could be positive for their intellectual development. According to this process, small modifications in biological and social environments could have important multiplier effects. During the twentieth century, in industrialized countries, several positive bioenvironmental factors appeared, providing an opportunity for genetic intellectual potential to develop. But will this intellectual potential continue to develop? According to Bronfenbrenner and Ceci (1994) , the proportion of the genetic potential not already actualized is unknown, and even unknowable. As trees do not grow to the sky, human intelligence has likely some developmental limits that will be reached sooner or later. Regarding height potential, a ceiling seems to have been reached in some countries where positive bioenvironmental conditions appeared earlier – in Norway and Sweden, for example, the height of conscripts is no longer increasing ( Schmidt et al. , 1995 ). At the same time, in countries where positive bioenvironmental conditions appeared later (for example, Southern European countries), height is continuing to increase. A similar phenomenon is now being observed for intelligence. In Norway and Denmark the Flynn effect has not been observed since the 1990s, and intelligence seems to have reached a plateau.
Susan Engi Raiford , Alan S. Kaufman , in WISC-V Assessment and Interpretation , 2016
A special issue encountered in intellectual ability testing, the Flynn effect (i.e., a 3-point rise in Full Scale IQ points per decade), is approached in Chapter 6 . Grégoire et al. provide a review of the Flynn effect across editions of the WPPSI and WISC, demonstrating that the effect is more complex and varied than originally thought. They provide a well-reasoned explanation for the varied impact of the Flynn effect across domains, particularly the Processing Speed subtests, as well as guidelines for clinical practice that will be of use to clinicians attempting to compare results across the WISC-IV and WISC-V.
Justification of Neuropsychological Batteries
Elbert W. Russell , in The Scientific Foundation of Neuropsychological Assessment , 2012
Perhaps an even greater problem for norming is what might be called the obsolescence effect on assessment. The Flynn effect brings this issue into prominence. The concept that obsolescence or being out-of-date makes a test or procedure invalid (“inaccurate,” “inappropriate,” “not useful,” “creating wrong interpretations,” etc.) has been widely accepted in psychology and neuropsychology ( Russell, 2010 ). Such obsolescence, which is produced by merely publishing a new version of a test, has been accepted by the American Psychological Association as indicated by statements in the Standards for Educational and Psychological Testing ( AERA et al., 1999, p. 59, 4.18 ) and the Ethical Principles of Psychologists and Code of Conduct of the American Psychological Association ( APA, 2002, p. 1072 ).
This change resulting from the concept of obsolescence has produced a great amount of damage in the field of psychology. For instance, it has produced an extensive nullification of research effort. Each new test means that the research done on the previous versions is no longer applicable to the newer versions. Examination of the literature ( Russell, 2010 ) indicates that, up to the present, the number of psychological research studies that have been obliterated are probably about 10,000.
The arguments attempting to justify this concept of obsolescence, generally referring to the Flynn effect, mean that the creation of a new version of a test or simply time makes tests obsolete. However, the Flynn effect appears to have plateaued. In psychometric theory, validated tests do not lose their validity because of the creation of newer versions. In addition, time does not invalidate tests because of the improvement of neurological methodology such as the MRI. This assumption is unscientific, unproven, and, if true, would discredit all older neuropsychological and neurological knowledge, including the work by Broca, Wernicke, William James, Luria, Head, Hebb, and Teuber. In science, no method, theory, or information, once validated, loses that validation merely because of time or the creation of another test or procedure. Once validated, a procedure is only disproved or replaced by means of new research that demonstrates the procedure’s lack of validity.
Robert J. Sternberg , James C. Kaufman , in Reference Module in Neuroscience and Biobehavioral Psychology , 2017
An Empirical Curiosity: The Flynn Effect
We know that the environment has powerful effects on cognitive abilities. Perhaps the simplest and most potent demonstration of this effect is the Flynn effect , named after its discoverer, James Flynn. The basic phenomenon is that IQ has increased over successive generations throughout the world during most of the past century—at least since 1930. The effect must be environmental because, obviously, a successive stream of genetic mutations could not have taken hold and exerted such an effect over such a short period of time. The effect is powerful—about 15 points of IQ per generation for tests of fluid intelligence. Also, it occurs throughout the world. The effect has been greater for tests of fluid intelligence than for tests of crystallized intelligence. The difference, if linearly extrapolated (a hazardous procedure, obviously), suggests that a person who in 1892 was at the 90th percentile on the Raven Progressive Matrices, a test of fluid intelligence, would in 1992 score at the 5th percentile.
There have been many potential explanations of the Flynn effect, and in 1996 a conference was organized by Ulric Neisser and held at Emory University to try to explain the effect. Some of the possible explanations include increased schooling, greater educational attainment of parents, better nutrition, and less childhood disease. A particularly interesting explanation is that of more and better parental attention to children. Whatever the answer, the Flynn effect suggests we need to think carefully about the view that IQ is fixed. It probably is not fixed within individuals, and it is certainly not fixed across generations.
Creative competence and age
Kenneth J. Gilhooly , Mary L.M. Gilhooly , in Aging and Creativity , 2021
Cohort effects on divergent thinking?
As we will discuss later in ( Chapter 9 ), the results of cross-sectional studies may be affected by cohort effects . In particular, the Flynn effect ( Flynn, 1984, 2012 Flynn, 1984 Flynn, 2012 ) whereby earlier born cohorts scored lower than more recent born cohorts could give a misleading impression in cross-sectional studies of declining convergent intelligence with age, when the effective factor is birth cohort rather than age itself. Since most of the studies outlined above on age effects on divergent thinking have been cross-sectional, it is worth considering the possibility of cohort effects in this area.
Cohort effects in creativity test performance have been examined by Kim (2011) in a dramatically titled paper (“The Creativity Crisis …”) which reports a large study of normative data from the Torrance Tests of Creative Thinking (TTCT-Figural) over five time periods, viz., 1974, 1984, 1990, 1998, and 2008. A total of 272,599 participants were involved in this study.
The TTCT-Figural is timed at 30 min, so speed is a factor. It involves three activities with 10 min allowed for each. In Activity 1, the participant constructs a picture using a pear or jelly-bean shape provided. In Activity 2, 10 incomplete figures must be used to make an object or picture. With Activity 3, there are three pages of lines or circles which the testee uses in creating a picture or pictures.
The age ranges used in this study extend from 6 to 18–20 years, and so there are no results on older individuals in this study. However, the interesting result is that since 1990, even as IQ scores have increased with successive cohorts (the usual Flynn effect), TTCT creativity scores have significantly declined for more recent cohorts. These results imply that age-related declines in creativity score in cross-sectional studies cannot be explained by cohort effects (which are going in the opposite direction) and so support the view that there are real longitudinal declines within individuals that underlie the cross-sectional findings.
A related study by Weinstein et al. (2014) investigated whether the apparent trend to reduced creativity over birth cohorts found by Kim (2011) might replicate, using real-world creative products, rather than responses to rather artificial tests. In the Weinstein et al. study, 354 visual artworks and 50 creative writing works, produced by adolescents between 1990–95 and 2006–11, were assessed using a structured method based on technical criteria and content elements. Results showed strong domain differences, in that performance in visual arts increased on a variety of indices of complexity and technical proficiency, while performance in writing decreased on indices related to originality and technical proficiency. The authors suggest an environmental change explanation in that changes in technology have made much more visual artwork examples and visual artwork tools available digitally which have benefitted creative productivity in that domain; while more rigid structuring of written work has been emphasized in schools which may reduce novelty of structuring in that domain.
Aging effects on cognitive and noncognitive factors in creativity
Age effects on intelligence tests.
In examining age effects on intelligence, three broad methodological approaches are possible (1) cross-sectional; (2) longitudinal; and (3) cross-sequential. Let us now look at these in turn.
In the cross-sectional method, one compares different age groups at same time. As a simple example, separate groups of 20, 40, and 60 year olds could be given the same intelligence test, and the resulting distributions of scores compared to look for differences in averages or variabilities. There are advantages of cross-sectional methods—they are quick to administer, inexpensive to run and avoid test repetition or practice effects.
Typical cross-sectional findings for verbal meaning, reasoning, space, word fluency, and number are shown in Fig. 9.1 . These results suggest maintenance of abilities until mid-50s then declines, with verbal meaning (a crystallized measure) holding up best over the age groups ( Schaie, 1994 ).
Figure 9.1 . Typical cross-sectional findings for verbal meaning, reasoning, space, word fluency and number.
Rabbitt (2015 , p.19) reports cross-sectional results from a study of 6504 volunteers which indicate drops in performance on the AH4 (fluid) intelligence test of around 10% between ages 55 to 65, 13% between 65 and 75 and 20% between 80 and 89. Thus, in this study, the rate of decline in fluid ability tends to increase with age.
However, cross-sectional methods have some disadvantages. They assume representative (comparable) samples that differ in just one way (age); they cannot measure individual changes with age and are open to cohort effects which we discuss next.
Flynn (cohort) effects
Test score differences between groups of different ages may reflect long-term effects that apply to particular birth cohorts. For example, earlier birth cohorts (i.e., groups born at the same time) may have had more exposure to environmental lead pollution or have had fewer years of formal education than later cohorts.
See Fig. 9.2 , for example, cohort effects ( Schaie, 1994 ). This Fig. shows marked cohort effects—three out of the five abilities show increasing scores over cohorts. Similar have been trends found in many studies.
Figure 9.2 . Cohort effects in mental ability measures.
The typical trend of increasing raw scores on IQ tests by cohorts over the years is known as the “ Flynn effect ” ( Flynn, 1984 ) and is a current puzzle in the area. If the effect is taken to mean that actual intelligence or ability has increased by six points per decade, it would imply that average people 100 years ago were functioning at the level of learning-impaired people today—which seems unlikely. After all, no striking increases have been noticed in rates of production of patents, scientific discoveries, and so on, which might be expected if raw cognitive ability had truly increased so markedly.
So, what is going on? Possible explanations of the Flynn effect include (a) more familiarity with test-like tasks over cohorts; (b) better nutrition leading to healthier brains; (c) better antenatal/perinatal care leading to fewer brain damaged babies; (d) more stimulating environments; and (e) spread of computer and video games ( Rindermann et al., 2017 ). However, none of these possibilities have been established definitively as causes and as Deary (2001 , p.112) wrote, “it is officially mysterious,” and remains so over 20 years later. To complicate matters even further, there is some evidence now that the effect is reducing or even reversing! Teasdale and Owen (2005) in a study of Danish military conscripts found diminishing gains over the period 1959–98 and declines between 1998 and 2004. Similarly, Bratsberg and Rogeberg (2018) found evidence of a reversing Flynn effect in Norway following an earlier typical Flynn effect. These fluctuations suggest a role for environmental factors as against genetic changes.
ASSESSMENT OF INTELLIGENCE
Helen Tager-Flusberg , Daniela Plesa-Skwerer , in Developmental-Behavioral Pediatrics (Fourth Edition) , 2009
Changes in Intelligence Scores Across Generations
A recurring theme in this chapter has been the relative weight given to conventional intelligence tests in conceptions, theoretical models, and empiric research on intelligence. Despite the controversies that remain regarding the overreliance on intelligence tests, the fact remains that these tests are reliable and valid measures. Yet it is still unclear what these tests actually measure. One of most intriguing findings in studies of IQ is the gradual increase in test scores that has occurred over the past several decades, not only in the United States, but also in many nations around the world ( Flynn, 1987 ). This increase, amounting to about 3 IQ points per decade, has been found on verbally loaded tests, but is especially apparent on nonverbal tests that tap primarily fluid intelligence, such as the Raven’s Progressive Matrices and Vocabulary Scales ( Raven et al, 1998 ). The so-called Flynn effect remains poorly understood, although many hypotheses have been proposed to explain why IQ scores are on the increase ( Neisser et al, 1996 ), including increases in the complexity of our culture or increases in nutritional status, which also has led to significant gains in height in many populations.
Perhaps the most significant lesson to be learned from the Flynn effect is that intelligence, as measured by standard tests, does change over time—that IQ scores are not immutable either within an individual or across populations. Standard tests of intelligence are important tools for predicting academic performance in children and identifying learning or intellectual disabilities. At the same time, there is a clear need to develop further instruments that would measure intelligence in a broader way that can capture the dynamic developmental processes that underlie cognitive performance across many domains of functioning. If IQ test scores predict individual differences in school achievement moderately well, and indirectly adult occupational status (to the extent that it is strongly related to academic success), the predictive correlations between IQ and actual job performance seem to be substantially reduced over time. Research by Dweck and colleagues ( Dweck et al, 2004; Mangels et al, 2006 ) has shown that, besides actual ability, individuals’ own beliefs about the nature of intelligence (e.g., whether it is believed to be a fixed quantity or to be acquirable) may influence their performance in a learning situation through top-down biasing of attention and strategic processing.
More importantly, intelligence is only one way to assess children. There are so many other dimensions that affect a child’s daily life at home and in school that go beyond intelligence, and may not even be associated with it, such as quality of life and positive emotional experience ( Watten et al, 1995 ).
Dysgenics and Eugenics
James R. Flynn , in Intelligence and Human Progress , 2013
If you want to abolish dysgenic reproduction, advanced nations should follow Sweden and Norway: abolish poverty so the lower classes will have middle class aspirations and knowledge of contraception. Even where this is not done, as in the United States and United Kingdom, the rate among the native population is slow enough to tolerate for a century, particularly if the Flynn Effect persists. Even if it does not, those nations may raise their average genetic IQ by using the new eugenic techniques. Immigration is a long-term problem only if you believe that black and Hispanic genes limit their potential. Even if you have a mild leaning in that direction, industrial society creates unskilled work the native population is unwilling to do. The rationale for many immigrants is that they will do it and thereby promote economic growth.
China, assuming its stability and ascendancy, is not going to meddle with the internal politics of other states more than the United States has done—probably it will do less. Eugenics/dysgenics does not supply a theory of history that decides whether the souls of America and Europe are to be saved or damned. It is an analysis of one trend, probably not a very important trend, among all those that will determine the course of the next century. As the next chapter will show, I am far more concerned about things like climate change and whether the world can be pacified without being merged into one state.
Changing feelings can boost creativity for conventional thinkers
Even people who tend to think conventionally, such as accountants or insurance adjusters, can be creative, a recent study suggests, if they can look at emotional situations in a different light.
In a set of experiments, researchers found that conventional thinkers, those who rank low on openness to new ideas and experiences, came up with more creative ideas than peers after they practiced "emotional reappraisal." This means viewing a situation through another emotional lens, such as trying to see an anger-inducing event as one that is neutral or hopeful.
The study, published in the journal Organizational Behavior and Human Decision Processes , indicates that that creativity is something that can be trained.
"One of the study's implications is that creativity is not something that's only accessible to people we think of as 'creatives'," said lead author Lily Zhu, an assistant professor in Washington State University's Carson College of Business. "Whenever we break away from our existing perspective and try to think about something that's different from our initial reaction, there's a creative element to it. If we can practice or train that flexible-thinking muscle, it may help us be more creative over time."
For the study, Zhu and co-authors Chris Bauman and Maia Young from University of California, Irvine, conducted a survey and two similar experiments with three different sets of people. The first survey of 279 college students revealed that people who tended to be more creative, ranking high on openness to new ideas, also tended to practice emotional reappraisal regularly.
In an experiment with 335 people recruited through a crowdsourcing platform, the participants were first ranked on their openness levels and then shown a film scene designed to elicit anger. While viewing, they were given different instructions: to suppress their emotions, think about something else to distract themselves or to try emotional appraisal -- looking at the scene through another lens. A portion were also given no instruction on how to regulate their feelings.
After viewing the film, the participants were asked to come up with an idea to use a space in their building being vacated by a cafeteria that was going out of business. Those ideas were then evaluated by a panel of experts who did not know anything about the participants. Ideas such as using the space for "napping pods" or opening a childcare facility were considered highly creative whereas opening a similar cafeteria or a food franchise were considered low in creativity.
The next experiment had a different group of 177 participants write about an experience that made them angry, rather than viewing a film. They were then tasked with either writing about it again from a different emotional perspective or writing about something else as a distraction.
In both experiments, conventional thinking participants who tried emotional reappraisal came up with more creative ideas than other conventional thinkers who used suppression, distraction or no emotional regulation strategy at all.
Notably, for participants who were considered creative thinkers to begin with, emotional reappraisal did not seem to have much effect on their creativity. The authors suggest that since creative people already tend to practice emotional reappraisal regularly, doing more of it doesn't have as much of an impact, like adding more gas to a car that already has fuel.
The findings have implications for improving business productivity, the researchers contend, since it appears possible to tap the knowledge and experience of more employees by encouraging their creativity, even those in conventional occupations like accounting, insurance adjustment or data analytics.
Zhu suggested that supervisors could develop trainings to cultivate creative thinking skills in employees. Individuals can also practice emotional reappraisal when confronted with a crisis or challenge instead of suppressing negative emotions.
"Negative emotions are inevitable in the workplace," Zhu said. "The question is not do we want negative emotions, or not? The question is: how can we better deal with them in a productive, healthy way? Part of the implications of this study is that we can use negative emotions in our everyday life as opportunities to practice flexible thinking."
- Anger Management
- Disorders and Syndromes
- Child Psychology
- Child Development
- Emotional detachment
- Anger management
- Limbic system
- Illusion of control
Materials provided by Washington State University . Original written by Sara Zaske. Note: Content may be edited for style and length.
Journal Reference :
- Lily Yuxuan Zhu, Christopher W. Bauman, Maia J Young. Unlocking creative potential: Reappraising emotional events facilitates creativity for conventional thinkers . Organizational Behavior and Human Decision Processes , 2023; 174: 104209 DOI: 10.1016/j.obhdp.2022.104209
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