The Role of Socioeconomic Status and Emotion Recognition in Verbal Intelligence Simon R. Poortman
Student ID: 10193820 University of Amsterdam
Supervisors: Nathalie de Vent & Laury van Bedaf Word amount abstract: 118
Table of Contents Abstract………...3 Introduction……….………..4 Method………..7 Results………..11 Discussion………..14 References……….20
Abstract
Recent research has shown a positive relationship between emotion recognition and verbal intelligence among children (De Stasio, Fiorilli & Di Chiacchio, 2014), as well as a possibly positive relationship between socioeconomic status and verbal intelligence (Berg & Berg, 1971; St. Rose, 2009), but no research included adults. Therefore, in this study the predictive role of emotion recognition and socioeconomic status in verbal intelligence was assessed in adults. A total of 40 participants ranging between 18 and 66 years were given the ERT and the PPVT-III. The results showed that neither emotion recognition nor
socioeconomic status significantly predicted verbal intelligence, when controlled for age and gender. In conclusion, the hypothesis that emotion recognition and socioeconomic status positively predict verbal intelligence has not been verified, and more extensive research on this relation is necessary.
Keywords: verbal intelligence, socioeconomic status, emotion recognition, adults, PPVT-III, ERT, SES
Introduction
Socioeconomic status (SES), a measure of the economic and social position of an individual or family, strongly influences the experiences of an individual from childhood through adult life. Growing up in a family from low socioeconomic class is related to substantially worse health and psychological well-being, and impaired cognitive and emotional development throughout life (Adler & Rehkopf, 2008; McLoyd, 1998). Also, not only the lowest level but all strata of SES affect cognitive and emotional development to certain degrees (Duncan, Brooks-Gunn & Klebanov, 1994; Sirin, 2005). Furthermore, socioeconomic status is correlated with academic achievement and intelligence through adolescence (Bradley & Corwyn, 2002; Brooks-Gunn & Duncan, 1997; Duncan, Yeung, Brooks-Gunn & Smith, 1998; Guo & Mullan-Harris, 2000; McLoyd, 1998; Sirin, 2005). The large influence of socioeconomic status on many levels implicates that it is relevant to the entire population. In this study, we investigate deeper by examining its effect on verbal intelligence. People that grow up in better circumstances might have more opportunity to develop their verbal abilities. Reasons for this could be better education or the simple fact that they interact with well-educated, intelligent people more than people with low socioeconomic status do. Before earlier research on this relation is discussed, light is shed on what exactly verbal intelligence is.
Verbal intelligence is an individual’s ability to use language in problem-solving and analysing, remembering, expressing and understanding information that is communicated verbally or in written form. Much is still unclear about what factors effect this component of intelligence, however some studies have been conducted that show greatly varying
relations. For instance, it has been found that verbal intelligence is correlated with socially and economically liberal beliefs (Carl, 2014), and another recent study has shown us that
short-term music training enhances verbal intelligence and executive functioning (Moreno, Bialystok, Barac, Schellenberg, Cepeda & Chau, 2011). Findings from a study behavioural patterns done by Arnold and Doctoroff (2004) revealed that higher verbal intelligence in children correlates with better adaptive skills in behaviour. Altogether, verbal intelligence seems to be linked with various cognitive and behavioural skills.
Research concerning the relation between verbal intelligence and socioeconomic status has shown different results. According to a study by Berg and Berg (1971) children from schools with either low or middle socioeconomic status did not vary from each other in verbal intelligence. However, later research found that social status indicators, such as parents’ level of education, were related to reading related skill development and vocabulary development in children (Carlson, 1998). The results of a more recent study conducted by St. Rose (2009) showed that socioeconomic status is a predictor of critical reading skills after controlling for ethnicity. In short, while several studies have been conducted and there is reason to believe socioeconomic status might have a positive influence on verbal intelligence, it remains unclear how the two are connected with each other.
Aside from the aforementioned relations, it is known that verbal intelligence also plays an important role in how well people understand and recognize emotions. Multiple studies provide evidence for this relationship among children. For example, Dunn, Brown and Beardsall (1991) found that the amount of utterances per hour about emotions in three-year-old children correlates with the ability to recognize emotions at the age of six. Furthermore, research conducted by de Rosnay and Harris (2002) has shown that the verbal abilities of three to six year old children predicts the understanding of emotions. Moreover, there is a positive relation between verbal intelligence and the external component of
emotion understanding in children in that same age range, which includes emotion
recognition (De Stasio, Fiorilli & Di Chiacchio, 2014). It is evident that there is a positive link between verbal intelligence and emotion recognition among children. However, in adults, knowledge on this relation is still scarce. Therefore, next to socioeconomic status, the role of emotion recognition in verbal intelligence will be studied.
Despite the research that has been carried out on socioeconomic status as a possible contributing factor in verbal intelligence, conflicting results tell us that its effect on verbal intelligence is still not entirely clear. Also, studies on verbal intelligence and its connection to emotion recognition have been done in the past decades, however these were focused exclusively on children. Research on this relation among adults has yet to be conducted, to see if this relation persists or if verbal intelligence is determined by other factors as age increases.
Therefore, both emotion recognition and socioeconomic status are measured as predictors of verbal intelligence, including adults in this study. The hypothesis is that emotion recognition as well as socioeconomic status positively predict verbal intelligence. The instrument to measure emotion recognition is the Emotion Recognition Task (ERT) (Montagne, Kessels, De Haan & Perrett, 2007). To assess verbal intelligence, The Peabody Picture Vocabulary Test-Third Edition-NL (PPVT-III) is used (Dunn & Dunn, 1997). The goal of this study is to find out if, next to emotion recognition, socioeconomic status predicts verbal intelligence. The expectationthat is tested here is that participants with higher
socioeconomic status and better emotion recognition ability will score higher on verbal intelligence.
Method Participants
A total of 40 participants were tested. They ranged in age between 18 and 66 with a mean of 33 (SD = 15.9). Subjects were excluded if they reported any medical or psychiatric disorder. All participants were Dutch native speakers. Males accounted for 55% of the sample, and females were 45%. Four researchers cooperated in this study and acquired their participants from their personal environment. No reward was given for participating in this study. Both this study and the written informed consent that was given by the
participants were approved by the ethics committee of the University of Amsterdam.
Measures
All participants completed the Emotion Recognition Task and the Peabody Picture Vocabulary Test-Third Edition-NL. A total of two questionnaires was used in this study, which all participants completed. The described tests are part of a test battery. The remaining tests will not be mentioned as they are irrelevant to this particular study and have no influence on the performance on the two tests.
Questionnaires
The two questionnaires used in this study inquired demographic information of the
participants. Data from the questionnaires that was used for this particular study were the highest achieved level of education and estimate of the average modal income of the participant’s household. With the data gathered from these questions, a score of SES was determined.
Peabody Picture Vocabulary Test-Third Edition-NL (PPVT-III)
The PPVT-III aims to measure verbal ability and verbal intelligence, speech problems and intellectual disability (Dunn & Dunn, 1997). The test contains 204 items, which are arranged in 17 sets of 12 items each. For every item, a sheet with four pictures is shown to the participant. While showing the sheet, the experimenter says a word (for example: ‘trapezium’) that corresponds to one of the four pictures and asks the participant to point to the fitting picture or to name the number of the correct picture. The age of the participant determines at which set the participant begins, for example people between the age of 16 and 35 will begin at set 13. The first set where the participant makes a maximum of four mistakes is called the starting set. Should the participant make more than four mistakes in the first set, the previous set is administered. The ending set is the last set that is
administered and it is the set where the participant makes more than nine mistakes. The total score is the number of the last item administered in the last set minus the amount of mistakes made. The test takes approximately 12 minutes to complete.
Emotion Recognition Task (ERT)
The ERT is an untimed computer test designed to measure the recognition of facial emotional expressions. These expressions are shown as morphs slowly changing to express one of the six basic emotions from neutral to one of four intensity levels (40%, 60%, 80% and 100%). Participants have to choose between a six-alternative choice response to label these expressions (Montagne, et al., 2007). The six basic emotions are happiness, disgust, sadness, fear, anger and surprise. The presentation order of the morph video clips is the same for all participants (4 sets of 24 trials), beginning with the lower intensity and
per emotion per intensity is four. A total score for each emotion is computed by adding the number correct for the four intensities, with a maximum of 16 per emotion. Lastly, a total score for the ERT was composed by adding the individual total scores per emotion, with a maximum of 96. The duration of the task is approximately 10 minutes.
Socioeconomic Status (SES)
SES is estimated by asking for the participants’ highest achieved level of education, an estimate of the number of modal incomes earned by their household and the number of earners in their household. Scoring of educational level is based on the
International Standard Classification of Education (ISCED) (CBS, 2009). These levels range from one to eight, with bachelor’s being the sixth ISCED level, for example. The score for the household’s average modal income is equivalent to the number of modal incomes that is earned by the household, divided by the amount of earners in that particular household.
Procedure
The participants were visited at their homes or visited the homes of the researchers. Testing was done on an individual basis in a separate, quiet room. All participants signed an
informed consent at the beginning. Instructions are then given to the participants and they are told that their data will be treated confidentially. Before the start of the tests,
participants were asked to complete the first questionnaire. At the end of the tests, the participants completed the second questionnaire. The two tests relevant to this study were always completed in the same order: first, the ERT and then the PPVT-III. There were several breaks between tests to prevent interference of tests.
Data-analysis
It is expected that socioeconomic status and emotion recognition positively predict verbal intelligence. This is tested using a hierarchical multiple regression analysis. Here, socioeconomic status is a categorical predictor and emotion recognition is a continuous predictor. Verbal intelligence is the dependent, continuous variable. In step 1 of the regression analysis, control variables age and gender will be entered as independent variables. In the second, final step, socioeconomic status and emotion recognition will be added to the model.
To make sure the data is normally distributed, histograms with a fitted normal curve and Normal P-P or Normal Q-Q plot will be checked. The histogram will also be inspected for possible outliers. To check if there is independence of observations (autocorrelation), the Durbin-Watson statistic is used. Furthermore, there must be no multicollinearity in the data. Multicollinearity is checked against four key criteria: (1) Correlation Matrix: Pearson's
Bivariate Correlation among all independent variables needs to be smaller than .8; (2) Tolerance: this is calculated with an initial linear regression analysis, defined as T = 1 – R². When T < 0.02, there might be multicollinearity in the data and when T < 0.01, there
definitely is; (3) Variance Inflation Factor (VIF), defined as VIF = 1/T. With VIF > 10 there is an indication for multicollinearity to be present and (4) Condition Index, calculated using a factor analysis on the independent variables. Values between 10 and 30 indicate a moderate multicollinearity, values over 30 indicate strong multicollinearity. If
multicollinearity is found in the data, the data could be centered by deducting the mean score. Another alternative is to conduct a factor analysis before the regression analysis and
then rotate the factor to make sure factors in the linear regression are independent. Lastly, a scatterplot will be inspected to check for homoscedasticity and linearity.
Results
Of the 41 participants, one was excluded from the study due to missing data. The rest of the participants finished the tests successfully and all their data were included in the process. For the remaining participants, the mean scores on the ERT and the PPVT-III were calculated, including their standard deviations (Table 1).
Table 1
Mean scores on the Emotion Recognition Task, the Peabody Picture Vocabulary Test-III and SES, including the standard deviations
Test Mean SD
PPVT-III 182.25 9.74
SES 6.78 2.04
ERT 64.18 7.33
Note. N = 40
A hierarchical multiple regression analysis was conducted to assess how well emotion recognition and SES predict verbal intelligence, after controlling for age and gender. Preliminary analyses were conducted to assure that there is no violation of the assumptions of normality, independence of observations, multicollinearity, linearity and homoscedasticity. An analysis of standard residuals was carried out, which showed that the
data contained one outlier (Std. Residual Min = -3.79, Std. Residual Max = 1.45). However, after inspection, there was no further reason to exclude this participant from the study. The histogram of standardised residuals indicated that the data was approximately normally distributed, as did the normal P-P, showing points that were almost completely on the line. Also, the data met the assumption of independent errors (Durbin-Watson value = 1.72) and tests to see if the data met the assumption of collinearity indicated that multicollinearity was not a concern (Age, Tolerance = .57, VIF = 1.76; Gender, Tolerance = .93, VIF = 1.07; SES, Tolerance = .60, VIF = 1.66; ERT, Tolerance = .96, VIF = 1.04). Furthermore, the scatterplot of standardised residuals showed that the data met the assumptions of linearity and
homoscedasticity. The correlations of the variables are shown in Table 2. As can be seen, the correlations between the predictor variables were not statistically significant. This was as expected. However, age correlated largely with the performance on the PPVT-III and this was not as expected.
Table 2
Zero-order correlations between predictor variables and dependent variable
PPVT-III Age Gender SES ERT
PPVT-III 1.000 Age .783* 1.000 Gender .071 .241 1.000 SES .518* .625 .076 1.000 ERT -.351* -.178 -.029 -.172 1.000 Note. * p ≤ .05
Age and gender were entered at Step 1, significantly explaining 62.8 % of the
variance in the performance on the PPVT-III, F(2, 37) = 31.18, p < .001, R² = .628, Adjusted R² = .607. After entry of ERT and SES at Step 2 the variance accounted for by the model was significant and 67.3%, F(4, 35) = 18.00, p < .001, R² = .673, Adjusted R² = .636. ERT and SES explained an additional 4.5% of the variance in the performance on PPVT-III after controlling for age and gender, but this was not significant, R squared change = .045, F change (2, 35) = 2.428, p > .05. This was not as expected. In the final model, contrary to expectations, ERT significantly predicted the performance on the PPVTIII, but this relation was negative, ß = -2.16, t(35) = -2.19, p < .05. SES was not significant in predicting the performance on the PPVT-III, ß = .012, t(35) = -.092, p > .05. The raw and standardized coefficients of both models, together with the squared semi-partial correlations with the performance on the PPVT-III can be seen in table 3. Most of the total variance was uniquely explained by age, and to a lesser extent by ERT. Gender and SES received the lowest of the four weights in the model.
Table 3
Regression model to predict the performance on the Peabody Picture Vocabulary Test-III
Model 1 Model 2 B SE (B) ß B SE (B) ß sr² Constant 169.406* 3.278 188.233* 9.842 Age .497* .063 .813 .469* .078 .766 .333 Gender -2.416 1.998 -.125 -2.336 1.934 -1.21 .014 SES .055 .594 .012 .000 ERT -.286* .131 -2.16 .045
Note. sr² is the squared semi-partial correlation. * p ≤ .05
Discussion
The aim of this study was to evaluate how emotion recognition and socioeconomic status predict verbal intelligence. We expected that emotion recognition and socioeconomic status positively predict verbal intelligence. In this study, socioeconomic status did not significantly predict verbal intelligence when controlled for age and gender. Emotion recognition did, but negatively. This is not in line with the pre-set hypothesis. Instead, age primarily predicted verbal intelligence and this was significant.
The results of this study differ from the results of previous research. In the study conducted by Rosnay and Harris (2002) verbal abilities of three to six year old children predicted their understanding of emotions, and the more recent study by De Stasio, Fiorilli and Di Chiacchio (2014) showed that verbal intelligence and emotion recognition are
positively correlated. A similar relation was expected to be found in this study, but in fact a negative correlation was found and emotion recognition negatively predicted verbal intelligence. The results of earlier research on socioeconomic status and verbal intelligence was inconsistent, but still gave us reason to believe that socioeconomic status could predict verbal intelligence. Berg and Berg (1971) showed us that there was no variation in verbal intelligence in children from schools with low and middle socioeconomic status, but Carlson (1998) and St. Rose (2009) found that socioeconomic status was related to vocabulary development and predicted reading related skills. We expected to find that socioeconomic status was a predictor of verbal intelligence, but this relation was not found in this study.
First, it should be noted that age had a great effect on verbal intelligence. The most probable explanation for age to largely predict the performance on the PPVT-III, is
vocabulary development. Older people generally have a more extensive vocabulary than younger adults (Bowles, Grimm, & McArdle, 2005; Bowles & Salthouse, 2008; Kavé & Yafé, 2014; Kemper & Sumner, 2001; Verhaeghen, 2003). It is assumed that age is related to a growth of vocabulary due to incremental reading or life experience (Uttl, 2002). Improved performance on tests that measure vocabulary may also reflect a cohort effect that favour people that were born earlier, either because they have read more or because the tests contain obsolete items (Verhaeghen, 2003). Using the fourth edition of the PPVT might work to control the latter. This improved version of the test came out ten years later than the third edition and includes less old fashioned words (Dunn & Dunn, 2007).Also, tests other than the PPVT editions could be used to focus less on vocabulary and more on other
dimensions of verbal intelligence. An example of a test that includes more aspects of verbal intelligence is the fourth edition of the Wechsler Adult Intelligence Scale (WAIS-IV)
Index, consists of subtests that measures other abilities next to vocabulary, such as fluency with abstract verbal reasoning. A sample item of abstract verbal reasoning is “In what way are an apple and a pear alike?” to which the answer would be ‘fruit’. Other abilities
measured by the WAIS-IV are the degree of general information acquired from culture and the understanding of social conventions, rules and expressions. In future studies, the PPVT-IV and the WAIS-PPVT-IV can be considered as possible solutions to the significant effect of age in this study.
Another thing to keep in mind is the difference between the tests used to assess emotion recognition. The tests used in earlier studies vary, but what they have in common is that all of their items contain a verbal component and require a level of verbal
comprehension to be completed successfully. They are administered verbally and answers are to be given verbally as well. Tests like the Dog-Rabbit Test, the Mother-Infant Separation Test (MIST) and the Test of Comprehension (TEC) consist of long stories that precede each question about emotion, either told by the experimenter or included to the video that accompanied the item question. For example, in the MIST, the participant gets told a story that takes approximately five minutes before the final question is asked. A sample question is “When Sally is alone she hears someone knocking at the door. How does she feel before she sees who it is?”. The positive relationship between emotion recognition and verbal intelligence that was found in these studies, could be explained by the fact that it simply reflects the linguistic complexity of these tests. This could also be the reason that in this study, where the ERT was used, emotion recognition was not positively related to verbal intelligence, as this test does not consist of any verbal component. When administering the ERT, the participant simply gets presented a face that expresses a certain emotion. With one click, one out of six emotions is chosen and a non-verbal answer is given. Further research
regarding the difference in verbal relevance of these tests should be conducted. For example, a study on verbal intelligence including both the ERT and the MIST might show that there is a difference in performance on these tests. This way, the significant influence of verbal complexity can be assessed.
There are some aspects of socioeconomic status that should be considered when it comes to its level of significance in this study. What should be mentioned first, is which individual factors SES consists of here. In this study, SES was composed of the highest
achieved educational level and modal income of participants. The fact that it consists merely of these two components, is a possible explanation for it not to contribute much to the variance in verbal intelligence. To add more dimension and form a better determination of SES, additional elements should be assessed. There are multiple ways to do this, such as by adding the number of years spent in education or assessing occupational prestige, though the latter is a difficult factor to measure because so many occupations exist and there are many competing scales. Inquiring the number of years spent in education helps to account for the participants that are still in their student days, but have not graduated yet. For example, the highest achieved educational level of university students in their final year of bachelor’s studies is still (often) secondary school. So while some participants almost have their bachelor’s degree, these years in education were not included in the composition of SES because only the highest achieved level of education was included. Future studies should consider adding these years for a better representation of the educational level of participants.
Something else that should be kept in mind is the way the participants for this study have been gathered. The four experimenters in this study are university students from
middle to high socioeconomic class and the acquired participants are all from their own social circles. These consist mainly of family, friends, fellow students and colleagues, that were mainly from the same social class. Hence few participants with low socioeconomic status were included in this study, resulting in a relatively smaller range in SES between subjects. If this range were to be increased by also including people from a low
socioeconomic class, SES between participants would vary more. This way SES could possibly account more for the variance in verbal intelligence. In future research, the aim should be to acquire participants from all three levels of socioeconomic class.
Lastly, to assess modal income, participants were asked to give an estimation of the modal income of their household, which was then divided by its amount of earners. A possible complication is that participants that do not directly contribute to the modal income of their household might find it difficult to give a good estimate due to their lack of knowledge in this. For example, some of the younger participants that live with their parents reported having a hard time estimating the modal income. This may result in an inaccurate assessment of average modal income. A way to avoid this is by inquiring participants for a more exact valuation of the modal income of their household. However, the problem that arises here is that people often do not want to give away such detailed information due to private reasons.
In conclusion, the hypothesis that emotion recognition and socioeconomic status positively predict verbal intelligence has not been verified by this study. However, this study gave insight in what else could determine verbal intelligence later in life, and brought up new questions to be answered. Both the strengths and limitations of this study need to be
considered in future research, and more research is necessary for more clarification on this relationship.
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