Intellectual development from early childhood to early adulthood: The impact of early IQ differences on stability and change over time

Learning and Individual Differences 32 (2014) 156–162

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Intellectual development from early childhood to early adulthood: The impact of early IQ differences on stability and change over time Wolfgang Schneider ⁎, Frank Niklas, Sandra Schmiedeler Department of Psychology, University of Würzburg, Röntgenring 10, 97070 Würzburg, Germany

a r t i c l e

i n f o

Article history: Received 21 February 2013 Received in revised form 26 January 2014 Accepted 5 February 2014 Keywords: LOGIC study Differential stability of general intelligence Intellectual development Different levels of cognitive ability Long-term educational outcomes

a b s t r a c t Despite the fact that intelligence is the best studied psychological construct, there are still only few longitudinal studies analysing intellectual development from early childhood to adulthood. In this paper, data of the Munich Longitudinal Study on the Ontogenesis of Individual Competencies (LOGIC) were used to assess the differential stability of IQ from preschool to early adulthood as a function of level of IQ (i.e., high, average, and low). Intellectual ability of about 200 individuals was first assessed between the ages of four and twelve years, and subsequently at the ages of 17 and 23. Stability of general intelligence was found to be moderately high for the entire study period. Stability was higher for shorter intervals between measurement points and increased with age. Subgroup analyses for initially high-, average-, and low-IQ children revealed that IQ stability over time was higher for the low-IQ than for the high-IQ children. Overall, participants with initially higher IQ scores maintained their advantage throughout the study until the period of early adulthood, and were more likely to attend higher educational tracks. © 2014 Elsevier Inc. All rights reserved.

1. Stability of intelligence over time There is no doubt that intelligence is the psychological construct that has been studied best so far, both in cross-sectional and longitudinal studies. One issue frequently addressed in developmental and educational studies concerns the stability of intelligence over time. There is a broad agreement that the stability of cognitive ability varies as a function of the age of the sample but is rather high from school age onwards (e.g., Watkins & Smith, 2013). For instance, in the Fullerton Longitudinal Study (cf. Gottfried, Gottfried, & Guerin, 2006, 2009) correlations between cognitive measures at the age of 17 and preschool measures varied from r = .16 at the age of 1 year to r = .44 at the age of 3.5 years. In contrast, the average correlation of intelligence measures at the age of school enrolment (6 years of age) with intelligence at age 17 was quite strong (r = .67) and even increased (r = .77) for the time interval between 8 and 17 years (Gottfried et al., 2009). Overall, there is now plenty of evidence that individual differences in general intelligence tend to be stable over time from early school age onwards and that these differences do not seem to change much over the remaining life span (e.g. Deary, Whiteman, Starr, Whalley, & Fox, 2004; Hertzog & Schaie, 1986; Hoekstra, Bartels, & Boomsma, 2007). It is less clear, however, whether individual differences in IQ already assessed during the preschool years already predict subsequent intellectual development. Most available studies did not find much longitudinal ⁎ Corresponding author. Tel.: +49 931 3184822. E-mail addresses: [email protected] (W. Schneider), [email protected] (F. Niklas), [email protected] (S. Schmiedeler).

http://dx.doi.org/10.1016/j.lindif.2014.02.001 1041-6080/© 2014 Elsevier Inc. All rights reserved.

consistency between intelligence test scores at preschool age and intelligence in later development (e.g. Bishop et al., 2003; Gottfried et al., 2009; McCall, Hogarty, & Hurlburt, 1972). The longitudinal study by Bradway and colleagues is an exception in that test–retest correlations were already substantial for the 10-year interval between preschool and adolescence (r = .65), and remained almost identical thereafter, yielding a correlation of .64 between preschool and adult age (Bradway & Thompson, 1962; Kangas & Bradway, 1971). Given that the latter study was carried more than four decades ago and was based on a rather small sample of children (N = 111; N = 48 for the follow-up), a validation of its findings seems in order. Thus, one goal of the present study was to address the stability issue comparing the predictive power of IQ assessed at preschool vs. school age in a longitudinal framework considering relevant aspects like parents' socioeconomic status (SES) or children's age. Theoretically, one possibility for the lower predictive quality of early IQ could be that developmental changes in the prefrontal cortex seem particularly pronounced for the period between 4 and 6 years of age, with executive functions being already comparably robust when children enter school (Bjorklund, 2012). It has been repeatedly shown that executive functions such as updating are significantly correlated with children's general IQ, with correlations varying between .30 and .55 (e.g., Ardila, Pineda, & Rosselli, 2000). 2. Differential stability of intellectual ability over time In this context, another relevant question is whether the reported high IQ stability in random samples can be generalized to subsamples of children with initially high versus low average intelligence. There is evidence that children with initially high average IQ show less IQ

W. Schneider et al. / Learning and Individual Differences 32 (2014) 156–162

fluctuation over time compared to random samples, keeping up their high mental ability later in life. First support for this assumption came from the Terman Study of Gifted Children (Terman & Oden, 1959), showing test–retest correlation of .87 when intelligence in gifted children (N N 1500; IQs by definition N 130) was reassessed about 6 years after the initial testing. Similar findings were also reported for the Marburg longitudinal study that comprised a German sample of 151 gifted school children (IQ ≥ 130) first tested at the age of 9 years. It could be shown that more than two-thirds of the gifted children still yielded a very high IQ (N 125) after a period of six years (cf. Hanses, 2009). Moreover, Spangler and Sabatino (1995) also showed that most of their gifted elementary school children (N = 66) kept their eligibility status over a period of six years. The situation seems comparable for children with an initially low IQ. Several studies assessing IQ change and stability in low-IQ children (IQ b 80; mostly children with low-birth weight) found stability coefficients varying between .70 and .90, with test–retest intervals ranging between 3 and 10 years (see findings by Mortensen, Andresen, Kruuse, Sanders, & Reinisch, 2003; Schneider, Wolke, Schlagmüller, & Meyer, 2004). For low-ability children, high IQ stabilities were observed from an earlier age onwards than for children of average IQ (with IQ scores ranging between 85 and 115). For instance, Maisto and German (1986) showed that infants with low IQs were likely to continue to have low IQs in their childhood and adult years. However, there is also evidence that IQ scores may change considerably over time, indicating that individuals with initially high IQ scores may score lower at subsequent occasions, and vice versa. From a methodological point of view, more instability may be found in extreme scores of intelligence due to the problem of the regression towards the mean effect, in the absence of perfect reliability (Gottfried et al., 2009; Rost, 2010). For instance, Humphreys (1989) noted that a child with an IQ of 140 at a first assessment would be expected to have an IQ of 125 on a later measurement point. In fact, the mean IQ scores of the children of Terman's study were considerably lower at the second assessment about 6 years after the initial test (148 vs. 139; see Burks, Jensen, & Terman, 1930). In a more recent study, Lohman and Korb (2006) demonstrated that the majority of elementary school children showing intelligence test scores in the upper percentiles in one grade often did not maintain their high levels 1 or 2 years later, despite the use of highly reliable tests. There is still no conclusive answer to the question whether children with initially high IQ scores continue to be gifted when they grow up, and whether children with initially low IQ remain in this problem zone, that is, keep their low IQ scores throughout the school years and beyond, particularly when IQ is assessed very early in life. Thus, the present study aims at examining the differential stability of intelligence as a function of initial IQ level (low, average, and high IQ) from preschool age to adulthood. 3. Intelligence and academic success A final important aspect concerns the relationship between early IQ level and later academic success — an issue already analysed in a serial of studies. In the classic study conducted by Lewis Terman and colleagues a high cognitive ability assessed at the beginning of school predicted high levels of later achievement, although other variables such as motivation or persistence were also of upmost importance (e.g. Keating, 2009; Terman & Oden, 1959). Findings from recent longitudinal studies confirm the meaningful association between intelligence and later academic and professional success (e.g. Hanses, 2009; Judge, Klinger, & Simon, 2010; Sternberg, Grigorenko, & Bundy, 2001). In their impressive longitudinal study based on a very large sample, Deary, Strand, Smith, and Fernandes (2007) found high prospective correlations (r = 0.81) between a general intelligence latent score at the age of 11 years and a general educational latent trait variable at the age of 16 years. In his meta-analysis, Strenze (2007) estimated the

157

association between intelligence and socioeconomic success and reported high correlations between intelligence and education (r = .56); lower correlations were found between intelligence and occupation (r = .43), as well as between intelligence and income (r = .20). Furthermore, Strenze demonstrated that success could be better predicted by IQ scores when individuals were older at the initial IQ test. Whereas most previous studies focused on the development of children from school age onwards, the goal of our study was to explore whether later academic success could be predicted from IQ-level (low, average, and high) already assessed at the age of 4 years, as compared to IQ classifications obtained at the beginning of school entry with about the age of 6 years. 4. The present study This paper presents a secondary analysis based on the Munich Longitudinal Study on the Ontogenesis of Individual Competencies (LOGIC; Weinert & Schneider, 1999; Schneider & Bullock, 2009) which addresses the three major research questions already outlined above. First, we hypothesized that from the age of 6–7 years onwards, there is a remarkable stability with cognitive measures in later childhood and early adulthood, whereas preschool measures of IQ should be only moderately correlated to later IQ measures. Second, we supposed that test–retest correlations indicating within-group stability should be similar for children with initially low, average, and high IQ. Overall, we expected higher correlations for shorter intervals between measurement points and increasing with age. Moreover, we assumed that the initial IQ classifications would be moderately stable over time, with higher concordance of the classifications on the basis of the IQ scores at age 7, as compared to an initial IQ classification at age 4. Third, we hypothesized that initial IQ levels assessed at the beginning of elementary school (at ages 6 or 7 years) should reliably predict further academic development in school and thereafter, whereas IQ levels assessed at preschool age should not reliably predict later educational levels. We assumed that most participants with high average IQs at the age of 7 years subsequently attend higher educational tracks and end up as university students, whereas children with initially low average IQ should typically attend lower educational tracks and end up with less favourable professional careers. 5. Method 5.1. Sample The LOGIC study started in 1984 at the Max Planck Institute for Psychological Research in Munich, Germany. The sample originally consisted of 205 children with an age of almost 4 years, coming from 20 kindergartens in the South of Germany; another 25 children joined the sample 1 year later in Wave 2. Children were tested annually between 1984 and 1993 not only on intelligence but also on numerous other indicators of cognitive and personality development as well as educational achievement. Four years later, in 1997, there was a follow-up (Wave 10) with 174 adolescents aged 17 and 18 years. The last assessment so far (Wave 11) took place from 2004 to 2005, when participants were about 23 years old. At this last measurement point, 151 (73.7%) participants could still be tested. Missing data analysis revealed that individuals who were retained in the sample had on average significantly higher initial IQ scores, indicating systematic dropout. Information about intelligence development was available for the period between preschool age and early adulthood, but could not be obtained for every individual at each wave (see Table 1). For further information on the assessments, see Bullock and Schneider (2009). As our secondary analyses concern longitudinal changes in intellectual ability, all participants with data for only one of the eleven waves were removed from the sample. This left 215 participants for the analyses, 103 (47.9%) of whom were female.

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W. Schneider et al. / Learning and Individual Differences 32 (2014) 156–162

Table 1 Overview of the waves and corresponding ages of the test persons, the number of participants tested, and the IQ tests (English abbreviations in parentheses) assessing verbal and nonverbal intelligence. Wave

1

2

3

4

6

9

10

11

Average age in years

4

5

6

7

9

12

17

23

Number of participants tested with IQ tests

175

208

204

192

190

182

170

147

X

X X

X

X X

X

X

X

Verbal IQ measures HAWIVA (WPPSI) HAWIK-R (WISC) HAWIE-R (vocabulary) (WAIS) Nonverbal IQ measures CMMS HAWIVA (WPPSI) HAWIK-R (WISC) CFT

X

X X X X

Note: WPPSI = Wechsler Preschool and Primary Scale of Intelligence. WISC = Wechsler Intelligence Scale for Children. WAIS = Wechsler Adult Intelligence Scale. CMMS = Columbia Mental Maturity Scale. CFT = Culture Fair Intelligence Test.

5.2. Descriptions of tasks In the LOGIC study general intelligence was subdivided into verbal and nonverbal ability components. An overview of the different psychometric intelligence tests used during the preschool and school years, and of the number of participants for whom intelligence could be assessed at a certain wave is given in Table 1. 5.2.1. Verbal intelligence At the beginning of the study, the Hannover–Wechsler Intelligence Scale for Preschool Children (HAWIVA; Eggert, 1978) was administered twice. This test is quite similar to the Wechsler Preschool and Primary Scale of Intelligence (WPPSI) used in many international studies. Due to time constraints only the verbal part of the HAWIVA was applied in Wave 1. This part consists of three subtests: “General Knowledge”, “Vocabulary”, and “General Comprehension”. Split half reliability for these subtests was above .80 each. One year later, the complete HAWIVA test tapping both verbal and nonverbal intelligence was administered. After school enrolment (Wave 4) the Hamburg–Wechsler Intelligence Scale for Children (HAWIK-R; equivalent to the WISC; Tewes, 1985) replaced the HAWIVA. The HAWIK-R was used between the ages of 6 and 16 years. The verbal part of the HAWIK-R consisted of the subtests “General Knowledge”, “Vocabulary”, “General Comprehension”, “Commonalities”, “Numerical Thinking”, and “Digit Span”. Internal consistency of the subtests varied between .86 (General Knowledge) and.89 (Vocabulary). In the two follow-up assessments (Waves 10 and 11) the subscale “Vocabulary” of the Hamburg–Wechsler Intelligence Scale for Adults (HAWIE-R; Tewes, 1991) was administered as a proxy of verbal intelligence. Due to time constraints, it was not possible to include more subtests of the HAWIE-R in the last waves of the LOGIC study. However, the usage of the vocabulary subscale as a proxy of verbal IQ seems to be justifiable, as it yields the highest overall correlation of the verbal subtests with total verbal IQ. The internal consistency of the vocabulary subtest was .90 for Wave 10 and .84 for Wave 11. 5.2.2. Nonverbal intelligence The Columbia Mental Maturity Scale (CMMS; Burgemeister, Blum, & Lorge, 1972) was used to assess children's nonverbal intellectual skills during Waves 1 and 3. This test requires children to select the one odd item in a group of pictures (e.g., 1 spoon and 4 forks). The split-half reliability of the CMMS is very high (r = .96). Additionally, the nonverbal intelligence component of the HAWIVA was assessed in Wave 2. The three subtests “Maze”, “Figure Drawing”, and “Mosaic Test” were used to construct a total score for nonverbal IQ. Split-half reliability of these subtests varied between .74 (Figure Drawing) and .87 (Mosaic Test).

In Wave 6, children were tested with the nonverbal part of the HAWIK-R (Tewes, 1985). Here, the subtests “Picture Completion”, “Mosaic Test”, “Picture Ordering”, and “Digit symbol Test” were combined to create a sum score for nonverbal IQ. All subtests showed sufficient internal consistency, with alpha coefficients ranging between .67 (Picture Ordering) and .89 (Mosaic Test). As the CMMS measure was no longer appropriate for the children after they had entered Grade 4, it was replaced by the Culture Fair Intelligence Test (CFT; German version; Weiss, 1976), which was subsequently used in Waves 9, 10, and 11. This test assesses children's fluid intelligence with four subtests: “Series”, “Classification”, “Matrices”, and “Topologies”. Subtest reliability was sufficient to good, with internal consistency scores ranging between .80 and .85. For more information about the intelligence tests used in the LOGIC study, see also Schneider, Stefanek, and Niklas (2009). 5.2.3. School and university pathways A questionnaire was used in Wave 10 to obtain information about the participants' progress in school. Information about subsequent professional careers and the pathways into the university system was also obtained by a questionnaire presented in Wave 11. In Germany, children attend elementary school from the age of about 6 until about 10 years. Secondary school is divided into several educational tracks or pathways (i.e. “Hauptschule” as lower, “Realschule” as middle, and “Gymnasium” as higher educational track). Only students graduating from Gymnasium are able to study at a university. 6. Results 6.1. Statistical approach We first calculated total IQ scores consisting of verbal and nonverbal IQ scores. The mean interrelation between nonverbal and verbal IQ obtained at the same wave was relatively high, with a mean correlation of rm = .45. In addition to the total IQ scores at Waves 1, 2, 6, and 9, we also calculated a total IQ score for Wave 4 from verbal IQ obtained in Wave 4 and nonverbal IQ obtained in Wave 3. This was possible because both assessments were less than one year apart, showing a rather high correlation (r = .49). No IQ scores could be calculated for assessments in Waves 10 and 11 due to the fact that only one verbal subtest was used. To obtain total IQ measures, the raw scores for verbal and nonverbal IQ were first z-standardized, and then a composite score was calculated. To answer our first hypothesis, partial correlation coefficients considering SES and age as covariates were calculated for the total sample of the LOGIC study for all waves. In a subsequent step (hypothesis 2),

W. Schneider et al. / Learning and Individual Differences 32 (2014) 156–162

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Table 2 Disattenuated partial correlations (attenuated partial correlations in parentheses) for all measurements of general intelligence (controlled for SES and age), as well as means and standard deviations.

(1) IQ_4 df (2) IQ_5 df (3) IQ_7 df (4) IQ_9 df (5) IQ_12 df (6) Intelligence_17 df (7) Intelligence_23

1

2

3

4

5

6

7

M (SD)

.40 (.37) 149 1

.69 (.59) 141 .64 (.53) 176 1

.66 (53) 145 .66 (.54) 177 .78 (.65) 176 1

.58 (.49) 137 .60 (.48) 169 .75 (.64) 169 .84 (.69) 169 1

.40 (.36) 129 .54 (.43) 158 .64 (.55) 158 .72 (.58) 158 .89 (.75) 158 1

.46 (.40) 107 .48 (.38) 127 .58 (.52) 129 .66 (.55) 129 .88 (.73) 129 .95 (.79) 129 1

107.7 (10.7)

we subdivided children in three groups according to their average IQ level (i.e. low, high, and average), comparing the lowest tercile to the highest tercile and also to the remaining tercile, depending on children's initial IQ at Wave 1. When no score was available for a child at Wave 1, his or her IQ score at Wave 2 was used for classification. In addition, we compared group stabilities obtained for initial IQ level classifications at the ages of 4 versus 7 years by calculating z-scores. Here, the percentages of participants reclassified in the same group at a later wave, and Cramer's V as a measure of contingency are presented (values b.20 indicate weak contingencies, values between .20 and .50 indicate pronounced contingencies, and values N .50 very strong contingencies; cf. Eckstein, 2004). To answer our third hypothesis, we explored whether children allocated to the three IQ-level groups followed different school career pathways and also differed according to their academic success. Moreover, we used logistic regressions to predict school and university outcomes using early IQ scores, controlling for the impact of potentially relevant variables such as SES, age, and sex.

6.2. Stability of intelligence over time In a first step of analysis, partial correlations among intelligence measures were calculated across the entire study period, as shown in Table 2. Moreover, the means and standard deviations of the overall IQ scores are presented.

106.2 (10.2) 104.5 (9.9) 102.7 (8.0) 111.5 (11.2) 0 (.83) 0 (.86)

Correlations between two adjacent waves were substantial, suggesting high stability of intelligence. Findings also confirmed the simplex structure of the data, at least from Wave 2 onwards, in that the longer the time period between two measurement points, the lower the correlations. Although IQ scores obtained at the age of 4 years correlated reliably with IQ scores assessed in adolescence and young adulthood, coefficients were of only moderate size. From age 7 onwards, interrelations turned out to be significantly higher (for waves 6 to 10, all p's b .05, using the Fisher r-to-z transformation). Although interrelations were even higher for the later years, from age 7 onwards all correlations exceeded .55. Thus, fairly high stability scores were obtained from that age onwards. In the next step, the sample was divided into three subgroups with initially low, average, and high average IQ. The mean IQ of those children belonging to the lowest tercile was M = 93 (SD = 6.0), and that of the highest tercile subgroup was M = 121 (SD = 4.3), not reaching the conventional level of giftedness (IQ N 130). The mean IQ obtained for the average-IQ group was M = 108 (SD = 3.6). Table 3 shows the partial correlations controlling for SES and age between intelligence measures for the whole study period, separately for each group. Unexpectedly, the pattern of stability scores differed among groups. Whereas stability coefficients found for the low-IQ group were moderate to high from the early beginning until the last wave and increased continuously with increasing age, this was not true for the other two

Table 3 Partial disattenuated correlations (partial attenuated correlations in parentheses) for all measurements of total intelligence for the subgroups with low, average, and high IQ, respectively (first, second, and third column; controlled for SES and age).

(1) IQ_4 df (2) IQ_5 df (3) IQ_7 df (4) IQ_9 df (5) IQ_12 df (6) Intelligence_17 df

2

3(4)

4(6)

5(9)

6(10)

7(11)

.41⁎/.12/.−.01 (.37⁎/.10/.−.01) 32/74/34

.28/.17/.34 (.25/.14/.30) 30/69/34 .54⁎⁎/.46⁎⁎/.18 (.48⁎⁎/.42⁎⁎/.16) 43/82/41

.35/−.11/.35 (.28/−.08/.28) 31/72/34 .55⁎⁎/.66⁎⁎/.06 (.47⁎⁎/.55⁎⁎/.05) 44/83/40 .66⁎⁎/.78⁎⁎/.30 (.56⁎⁎/.65⁎⁎/.25) 44/82/41

.41⁎/.13/.08 (.37⁎/.11/.07) 28/68/33 .53⁎⁎/.53⁎⁎/.18 (.44⁎⁎/.44⁎⁎/.15) 41/79/40 .57⁎⁎/.68⁎⁎/.63⁎⁎ (.50⁎⁎/.59⁎⁎/.55⁎⁎) 42/79/40 .70⁎⁎/.80⁎⁎/.70⁎⁎ (.58⁎⁎/.66⁎⁎/.58⁎⁎)

.35/.12/.23 (.31/.11/.21) 24/64/33 .66⁎⁎/.34⁎/.23 (.57⁎⁎/.28⁎/.20) 34/75/40 .62⁎⁎/.58⁎⁎/.35⁎ (.54⁎⁎/.52⁎⁎/.31⁎) 35/75/40 .79⁎⁎/.60⁎⁎/.55⁎⁎ (.65⁎⁎/.48⁎⁎/.45⁎⁎)

.68⁎⁎/.14/.47⁎ (.59⁎⁎/.12/.42⁎) 17/53/29 .57⁎/.26/.28 (.48⁎/.23/.24) 24/62/33 .52⁎/.57⁎⁎/.46⁎ (.46⁎/.50⁎⁎/.40⁎) 25/61/33 .72⁎⁎/.44⁎/.72⁎⁎ (.59⁎⁎/.37⁎/.59⁎⁎)

42/79/40

35/75/40 .94⁎⁎/.83⁎⁎/.86⁎⁎ (.81⁎⁎/.71⁎⁎/.73⁎⁎) 35/75/40

25/63/33 .98⁎⁎/.75⁎⁎/.89⁎⁎ (.84⁎⁎/.63⁎⁎/.76⁎⁎) 25/63/33 .99⁎⁎/.94⁎⁎/.80⁎⁎ (.94⁎⁎/.79⁎⁎/.68⁎⁎) 24/63/33

(7) Intelligence_23 ⁎ p b .05. ⁎⁎ p b .01.

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W. Schneider et al. / Learning and Individual Differences 32 (2014) 156–162

groups. Here, moderately high stability of the average-IQ group was only found from Wave 4 onwards, when children were about 7 years old. Substantial stability in the high-IQ group was not observed before Wave 6, when children were about 9 years old. The findings thus indicate that within-group stability over time tended to be highest for the low-IQ children, and lowest for the high-IQ subgroup. Next, the stability of group classification was analysed using Cramer's V and the percentage of participants reclassified in the same subgroup (see Table 4). Only about 50% of the children classified as high-, average-, and lowIQ at age 4 and about 60% of the children classified as high-, average-, and low-IQ at age 7 were reclassified in the same subgroups later on. The highest reclassification rate was found for the group with average initial IQs. Altogether, only weak to moderate contingency coefficients were found for the initial classification at age 4 (Cramer's V about .25), and moderate contingency scores were obtained for the classification at age 7 (Cramer's V about .40), pointing to considerable fluctuation. 6.3. Intelligence and academic success Finally, we compared the three subgroups with regard to their educational track. The attendance of the German “Gymnasium” as the high educational track (typically starting at the age of about 10 or 11 years) was compared to that of all other lower educational tracks. Moreover, we assessed the number of participants eventually enrolling in and graduating from university. Finally, we used logistic regression analyses to predict academic success while controlling for age, SES, and sex. The three IQ subgroups classified at the age of 4 did not differ significantly regarding the percentage of individuals still attending university or already having graduated from university at the last measurement point. Nonetheless, this early IQ classification significantly predicted the educational track at school (Cramer's V = .35; p b .001; see Fig. 1). Comparable percentages of individuals with initially average IQ either attended a low or a high educational track. In comparison, most participants with initially low IQ attended the low educational track, whereas the majority of those with initially high IQ attended the high educational track (Gymnasium). Using the classification based on IQ scores at school entry at age 7, the subgroups not only differed regarding their educational school pathways (Cramer's V = .45; p b .001) but also regarding the percentage of individuals enrolled at or graduating from university (Cramer's V = .38; p b .001, in both cases, see Fig. 1). Almost 50% of the total sample graduated from university, with about 80% of the high-IQ subgroup children graduating. Moreover, the low-IQ and average-IQ subgroups did not differ much regarding the percentage of graduated individuals (about 33% each). An interesting finding concerned the fact that 12 participants with initially low IQ eventually attended the high educational track, and that 9 participants with low IQ at the age of 7 did graduate from university. Surprisingly, 12 participants with initially high IQ did not attend the high educational track. Table 5 shows the IQ development for these subsamples as well as the mean IQ development for all children who attended the higher educational track, as well as for those graduating from university. As can be seen, the sample of academically successful

Fig. 1. Percentage of individuals attending higher educational tracks as a function of their initial IQ classification at Wave 1 or 2 (age 4), and percentage of individuals graduating from university as a function of their IQ classification at Wave 4 (age 7).

participants with an initially low IQ showed a steady increase in IQ as a function of time, ending up with IQ scores above the mean of the total sample. While this finding may explain the unexpected success of this subgroup, it is difficult to understand why a small subsample of children with initially high IQ did not attend the higher educational track given that their IQ level only decreased slightly over time, with IQ scores obtained at the end of elementary school still above the overall mean. To explore this issue, we analysed children's grades in spelling and math obtained at the end of elementary school. It turned out that this small subsample of high IQ children constituted a group of underachievers. That is, despite their high IQ, their grades in spelling and math were below the mean of the total sample, and significantly worse than those of the low IQ children who later attended the higher educational track and university. Finally, logistic regression analyses were carried out to predict academic success of the total sample while controlling for potentially relevant variables. Results were similar to those for the subgroup analyses. While initial IQ at age 4 only predicted the educational track reliably, early IQ assessed at age 7 predicted both educational track and graduation from university (model fit of these analyses was acceptable to good). Age and sex did not contribute to the prediction of academic success. However, participants from families with a higher SES were more likely to attend Gymnasium and graduate from university (Table 6).

7. Discussion One major goal of the secondary analyses of the LOGIC study presented in this paper was to explore the stability of individual differences in general intelligence as a function of level of IQ (i.e., initially high, average, and low IQ) with a sample of initially 4-year-old children who were followed up until the age of 23 years. Regarding the stability of intelligence from preschool to early adulthood, our findings are consistent with other longitudinal studies (e.g. Deary et al., 2004; Gottfried et al., 2009) indicating rather high stability of intelligence from childhood to early adulthood, with higher

Table 4 Stability of IQ-group classification, as a function of initial assessment. Classification at age 4

Classification at age 7

Classification at age

Cramer's V

Percentage of correct classifications for initially low, average, and high average IQ-groups

Cramer's V

Percentage of correct classifications for initially low, average, and high average IQ-groups

9 12 17 23

.36⁎⁎ .26⁎⁎ .21⁎ .23⁎

53.2/57.3/53.2 43.5/52.2/45.5 41.3/50.6/37.2 38.9/56.5/45.5

.48⁎⁎ .40⁎⁎ .37⁎⁎ .39⁎⁎

63.8/63.0/62.2 48.9/62.5/61.4 55.0/58.8/53.7 55.9/63.1/52.9

⁎ p b .05. ⁎⁎ p b .01.

W. Schneider et al. / Learning and Individual Differences 32 (2014) 156–162

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Table 5 Development of the mean IQ and z-scores (Waves 10 and 11) at various ages (standard deviations in parentheses). Age

4

7

9

12

17

23

Low initial IQ & Gymnasium (N = 12) High initial IQ & no Gymnasium (N = 12) Low early IQ & graduation from university (N = 9) High early IQ & no graduation from university (N = 9) Higher educational track (N = 94) Graduation from university (N = 70)

95 (6.5) 119 (2.9) 98 (6.0) 117 (7.4) 111 (9.8) 110 (10.2)

102 (6.0) 110 (6.5) 92 (4.9) 116 (3.9) 109 (8.5) 109 (9.9)

102 (7.7) 104 (6.4) 96 (4.9) 110 (6.6) 106 (7.2) 106 (7.5)

114 (8.7) 111 (7.7) 107 (5.6) 116 (8.7) 117 (9.8) 118 (10.6)

.25 (.44) −.33 (.54) −.37 (.57) .00 (.64) .45 (.61) .43 (.72)

.23 (.64) −.19 (.58) .10 (.56) .07 (.62) .36 (.71) .45 (.64)

Note: The term “initial IQ” refers to the IQ classification at age 4, whereas the term “early IQ” refers to the IQ classification at age 7.

stabilities for shorter intervals between measurement points and increasing with age of the participants. Contrary to expectation, IQ scores obtained at age 4 were already significantly related to IQ scores assessed at subsequent time points, with correlations ranging between .36 and .59. Nonetheless, IQ scores obtained at the age of 7 years predicted further IQ development more strongly. Correlations went up to r = .79 for short intervals, in particular for the period from late elementary school age onwards. Thus, although it is possible to predict further development from IQ assessments in the preschool years to some extent, these predictions are not as reliable as those based on IQ scores assessed at the beginning of elementary school. A second major issue concerned the stability of IQ in subgroups initially showing high, average, and low levels of intellectual ability. As most children belonging to the lower quartile of the initial IQ distribution were still in the normal range (M = 94), the term “low IQ” has to be used with caution in the present context. Similarly, those children belonging to the high-IQ group had a mean IQ of about 120 and were not gifted in the traditional sense (IQ N 130). However, their IQ level corresponded with that typically found in special classrooms for gifted children established in several states of Germany (see Stumpf & Schneider, 2009). Interestingly, IQ scores obtained for those children with an initially low IQ turned out to be more stable over time than those found for the two other subgroups from preschool age onwards, thus replicating earlier findings for low-IQ children (e.g., Mortensen et al., 2003; Schneider et al., 2004). For the subgroup with initially high IQ, sufficient within-group stability was not found before the early elementary school years, indicating considerable IQ fluctuation in this subgroup for the time between preschool and the beginning of elementary school. Concerning the reclassification of the three subgroups, we confirmed findings of other studies that most children with high cognitive ability maintain their advantage in intellectual ability until early adulthood (e.g. Hanses, 2009; Spangler & Sabatino, 1995). However, although about 50–60% of the participants remained in their IQsubgroups over a period of almost 20 years, there was also considerable

fluctuation. Not all children showing high test scores at an early assessment maintained this level one or two years later (see also Lohman & Korb, 2006). Interestingly, we also observed trends in the other direction, indicating that some participants with initially low intellectual abilities (IQ ≤ 85) subsequently managed to catch up enormously, reaching IQ scores above 120 at the age of 17. The substantial IQ change in this subgroup may at least partially explain the low correlation between IQ scores in preschool years and scores in later childhood. Regarding the association between early IQ-level and later academic success, we found that children with high intelligence at the age of 4 years were more likely to attend higher educational tracks in later childhood than the two other subgroups. Moreover, children with early high IQs were more likely to attend university than the remaining children. Interestingly, however, about 25% of the children with initially high IQ did not succeed in school. Our reanalysis revealed that these children can be conceived of as underachievers, showing poor achievement in math and spelling from early elementary school onwards. The fairly high percentage of underachievers in the high-IQ sample is surprising, and may be at least partially due to an undemanding instruction. In comparison, more than 20% of the children with an initially low IQ improved substantially over time, showing IQ levels above the overall mean at the end of elementary school, and also above average grades in math and spelling. It does not come as a surprise, then, that these children performed subsequently well, mastering the high educational track and also the German university system. Please note that the overall IQ level in the sample was slightly above the norm (M = 107) with comparatively little variance (SD = 10.4), pointing to a generalization problem. Given that the intelligence tests were standardized only a few years before they were used for the LOGIC sample, the relatively high mean IQ scores do not seem to indicate reliability problems but indicate sample characteristics (e.g., a mainly urban and comparably homogeneous sample, middle to high socio-economic status). More than half of the individuals in our sample attended university, compared to a normal percentage of 28% in Germany (OECD, 2013). This difference points to the overall high

Table 6 Overview of the various logistic regression analyses. Regression-coefficient B

SE

Wald-statistic

Sign. 2

Exp(B) 2

(a) Prediction of higher educational track (Gymnasium = 1) vs. lower educational track (=0) attendance using IQ_4 (Nagelkerke-R = .29) and IQ_7 (Nagelkerke-R = .38; in parentheses) as predictor variables (together with sex, age, and SES) Sex −.06 (−.07) .35 (.37) .03 (.04) .86 (.85) 0.94 (0.93) Age .00 (.00) .05 (.05) .00 (.00) .99 (.95) 1.00 (1.00) SES .03 (.02) .01 (.01) 13.89 (10.94) .00 (.00) 1.02 (1.03) IQ .07 (.12) .02 (.02) 14.30 (22.52) .00 (.00) 1.08 (1.13) Constant −9.82 (−14.08) 2.13 (2.65) 21.28 (28.24) .00 (.00) 0 (0) Regression-coefficient B

SE

Wald-statistic

Sig.

Exp(B)

(b) Prediction of graduation from university (=1) vs. no graduation (=0) using IQ_4 (Nagelkerke-R2 = .13) and IQ_7 (Nagelkerke-R2 = .26: in parentheses) as predictor variables (together with sex, age, and SES) Sex .43 (.52) .37 (.40) 1.34 (1.66) .25 (.20) 1.54 (1.68) Age .05 (.04) .05 (.06) .84 (.53) .36 (.46) 1.05 (1.04) SES .02 (.02) .01 (.01) 10.48 (7.50) .00 (.01) 1.02 (1.03) IQ .01 (.09) .02 (.02) .39 (12.99) .53 (.00) 1.01 (1.09) Constant −3.71 (−11.29) 2.05 (2.63) 3.27 (18.38) .07 (.00) 0.02 (0) Note: The interpretation of the Wald statistic is analogous to that of a t-test in linear regression. Exp(B) gives the exponentiation of the B coefficient (odds ratio for the predictors).

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