Research on age, cognitive ability and decision-making has made progress over recent years, yet the relative importance of numeric ability has only lately received attention (e.g. Peters, et al., 2007). Currently there is no general agreement on the definition of numeracy, however many researchers have attempted to broadly classify the key processes associated with this construct. For
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example, Peters et al., (2006) define numeracy as “the ability to process basic probability and numerical concepts” (p. 407). Research has shown that having inadequate numeric skills is associated with increased biases in judgement and decision-making (e.g. Reyna, Nelson, Han & Dieckmann, 2009) and lower comprehension and use of numeric information (Peters et al., 2007). Consequently, lower levels of numeracy may place an important barrier to individual’s understanding of financial, health and consumer domains. Furthermore, evidence suggests that poor numeracy skills may be associated with economic and social disadvantage, including poor health, living in deprived neighbourhoods and disadvantaged housing conditions (Jenkins, Ackerman, Frumkin, Salter & Vorhaus 2011).
To date, the literature provides a mixed picture in terms of how numeric ability is related to fluid and crystallised intelligences. For example, Kroesbergen, Luit, Ven Lieshout, Loosbroek and Van de Rijit (2009) found that numeric ability in 5-7 year olds was strongly correlated with both crystallised intelligence (measured by a Dutch verbal task; Taal Voor Kleuters, 1996) and fluid intelligence (measured by Raven’s Coloured Progressive Matrices). However, other research has shown numeracy to be linked with either crystallised intelligence (Schaie and Willis, 1989; 1993) or fluid intelligence, (Kyttala & Lehto, 2008). Furthermore, recent literature has shown numeric ability to be independent of other measures of intelligence (see Banks & Oldfield, 2006; Weller, Diekmann, Tusler, Merty, Burns & Peter’s, 2012; Wood et al., 2011).
In review of these findings, it seems that numeric ability might test relevant knowledge in terms of numeric rules as well as the application of this relevant knowledge for novel problem solving. Therefore it is argued that numeric
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ability reflects aspects related to both fluid intelligence and crystallised
intelligence. In other words, numeric ability is a test of a specific capability within the particular domain of mathematical knowledge. On this basis, this thesis will regard numeric ability as a unique measure of intelligence.
Research suggests that individuals differ substantially in numeric ability (Lipkus, Samsa, & Rimer, 2001) and that many people do not possess even basic numerical skills. The Skills for Life Survey (SfL) commissioned by the Department for Education and Skills (2002/3), consisted of a population sample of adults aged between 16 and 65. Data from this study suggested that 15 million adults in England possessed very low levels of numeracy and that respondents in the oldest age group (55-65) were most likely to be classified at the lowest level. In 2002, the English Longitudinal Study of Ageing (ELSA) sought to provide data about literacy and numeracy proficiency of those beyond the age of 65. ELSA assessed numeracy through five questions, which required successively more complex numerical calculations. Respondents were divided into four groups based on their answers to the numeracy questions. The results showed an overall trend for numeracy to decrease with age: 51% of adults in their fifties were classified as being in one of the bottom two numeracy groups, 62% for people in their sixties, 72% among people in their seventies and 78% for those aged eighty and above. These findings support data from the Seattle Longitudinal Study (Schaie, 1994), which suggested that numeric ability tends to rise until approximately middle age before declining steadily until age 60 whereby another lower plateau is reached. Furthermore, current research investigating the relationship between numeracy and age suggests that as a group, older adults evidence lower levels of numeracy than young adults (Banks & Oldfield, 2007; Peters, et al., 2007).
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Numeric ability has been shown to have important consequences in terms of health and wealth. To date, the health domain has seemingly been the primary focus of much research in regard to numeracy. Indeed, many health-related tasks, such as reading food labels, refilling prescriptions, measuring medications, interpreting blood sugars and understanding health risks require good numeracy abilities (Rothman, et al., 2008). For example, patients often have to make decisions based on information acquired from tables, charts or text. Information about many consumer products (i.e. financial services, nutritional values in food or utility expenses) is acquired in the same manner. Therefore, despite a focus towards reviews of research associated with decision-making and health, it is reasonable to assume that numeracy is as important in other domains (e.g. financial).
With regards to health, Fasolo, Reutskaja, Dixon and Boyce (2010) investigated how people understood information presented on the National Health Service (NHS) website. In their study, participants had to compare and select a hospital they would choose to attend based on a number of indicators such as waiting time, cleanliness and distance. Results indicated that younger and more highly numerate participants engaged in more cognitively demanding strategies and attempted to make tradeoffs. Older (and less numerate) participants found it harder to process different pieces information and tended to rely on summative measures to overcome these difficulties.
Williams, Parker, Baker, Parikh, Pitkin, Coates et al., (1995) found that many patients could not read and understand basic numeric medical information including instructions on medication bottles, standard appointment slips or financial information. In their study 19-33% of patients could not determine the number of pills of a prescription they should take. Apter, Cheng, Small, Bennett,
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Albert, et al., (2006) showed that asthma patients who had been prescribed inhaled steroids and had low levels of numeracy were more likely to have a history of hospitalizations and asthma-related emergency room visits.
In the financial domain, numeric ability has been shown to be associated with prosperity. For example Lusardi and Mitchell (2011) found that American citizens who were less numerate were less likely to accumulate wealth. And furthermore, individuals who could not perform a 2% interest rate calculation were much less likely to plan for retirement. Similar patterns of results have been found in other countries. For example, in the Netherlands, those who could perform a 2% interest calculation were found to be much more likely to plan for retirement, and in Italy, those able to do this calculation were more likely to participate in private pension plans (Van Rooij, Lusardi & Alessie, 2011).
In terms of ageing, numeric ability and wealth, surveys suggest that older adults with high numeric ability tend to be wealthier. For example, the Survey of Health, Ageing and Retirement in Europe (SHARE) assessed people aged 50 and above in 11 European countries. The survey included questions on demographics, physical and mental health, employment, income, assets, social activities, and expectations. Results indicated that numeracy was generally low amongst the older population however, older participants with higher numeric ability were found to be more likely to own stocks and general investments (Christelis, Jappelli, & Padula 2010). In addition, Banks (2006) found a positive correlation between numeracy and prosperity. Among 50-59 year old men, those with the highest numerical ability were also in the highest wealth quintile, while those with the lowest numeric ability were over six times more likely to be in the poorest wealth quintile. Similar results were established for women aged 50-59 and for both men and women aged 60-74.
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Numeric ability is found to be linked with many important decisions associated with health and finances. Studies and surveys have shown that numeric ability among populations across in America and Europe tends to be low. In addition, lack of numeracy is not only widespread but is particularly severe among some demographic groups, specifically the elderly.
Many important decisions require the individual to be proficient with numbers. How age impacts decision-making in the real world has received surprisingly little attention given the significant consequences of making poor choices. Furthermore, research investigating the association between age, decision-making and numeric ability has provided mixed findings.
Numeracy has been found to be a significant predictor of comprehension and decision-making performance by Wood et al., (2011). Wood et al., (2011) examined the impact of increasing choice options for prescription drug plans in younger and older adults. Measures of executive functioning, speed of processing, working memory, crystallised intelligence and numeric ability were also taken. Their results revealed that numeracy was a robust independent predictor of decision-making ability, regardless of age.
Other research has not always found age-related declines in tasks requiring numeric comprehension. Tanius, Wood, Hanoch and Rice (2009) found no significant effects of age on performance when participants were asked to compare and select a prescription drug plan. Although, numeric ability and speed of processing were found to be significant predictors with participants who scored highly on these measures making better choices.
Further research suggests that older adults can be as adept as younger adults in decision-making involving numbers. Specifically, previous research
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reports that adults use at least two types of strategies (exhaustive and non- exhaustive) to solve numeric problems, and they adjust their strategy to the problem and situation characteristics. Exhaustive strategies require that cognitively demanding verification processes be implemented before making a choice (e.g. encoding numbers and calculating the correct solution). Non- exhaustive strategies do not require all verification processes to be completed before a decision is made; an individual may simply retrieve a solution directly from memory. Geary and Wiley (1991) demonstrated that both older and younger adults engaged in exhaustive and non-exhaustive verification strategies and were equally adaptive in their strategy selection. In their study, a simple addition task was presented to older and younger adults and verbal reports of their strategy selection were recorded. Both age groups reported using non-exhaustive strategies (i.e. solving 4 + 9 by directly retrieving 13 from memory) and exhaustive strategies (i.e. solving 4 + 9 by calculating 10 + 4 – 1). Young adults used exhaustive strategies on easier problems more often than older adults (7% vs. 2%) and non- exhaustive strategies less often than older adults (88% vs. 98%).
Further evidence in support of adept strategy selection in older adulthood was presented by Geary, Frensch and Wiley (1993). In their study, participants were presented with simple and complex subtraction problems. Results indicated that the subtraction skills of the older adults were better developed than those of younger adults. In explaining these findings, the authors proposed that older adults’ early education in basic mathematics was superior to that of young adults, however the effect of practice throughout adulthood may also have contributed to the advantage of the older group. On this basis, it was thought that increased practice may have lead older adults to rely more upon non-exhaustive, retrieval
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based processes or be more adept at selecting an appropriate strategy for particular problems.
So far, the literature reviewed suggests that age-related changes in cognitive abilities cannot be characterized by mere declines, but are in fact complicated and potentially malleable. Arguably, even more interesting than the weaknesses are the relative strengths that older adults display. Findings demonstrate age-related gains in emotional functioning (e.g. Carstensen & Mickels, 2005). This pattern stands in contrast to the substantial body of literature documenting age-related declines in processes that are effortful, deliberative and resource intensive. Next, age-related changes in emotional functioning are outlined. Then emotion-regulation strategies used by older and younger adults are considered, followed by an evaluation of these strategies within a model of limited resources.