Für neue Kunden:
Für bereits registrierte Kunden:
107 Seiten, Note: 1.0
I. List of figures
II. List of tables
III. List of abbreviations
1.2 Relevance of dyslexia research in an economic context
1.3 Particular advantages of dyslexic people in the workforce
2 Origins and mechanisms of dyslexia
2.1 Definition of dyslexia
2.2 Theoretical approaches
2.2.1 Symptoms and mechanisms
2.2.2 Major theoretical approaches
2.3 Genetic influence
2.4 Neuroscientific findings
2.5 Environmental factors
2.6 Global reading comprehension
2.7 Reading mechanics and comprehension in dyslexics
2.8 Coping strategies and mechanisms
2.9 Diagnostic method – IQ-discrepancy criterion
2.10 Stigmatization and psychological aspects
2.11 Dyslexia in the consumer context
3 Decision-making of grocery shopping buying decisions
3.1 Classical decision-making theory
3.2 Factors of grocery shopping decision-making
3.3 Economic factors
3.4 Affection of dyslexics by heuristics and biases
3.5 Development of research question and goal of research
3.5.1 Everyday grocery shopping
3.5.2 Research question
3.6.1 Conceptual model
4 Development of decision for examined product
4.1 Definition of fruit juice
4.2 Multivitamin juice, a multidimensional product
5.1 Explicit product description
5.3.1 Intelligence test
5.3.2 Spelling test
5.3.3 Non-word, Syllable test
5.4 Informational brochure
5.5 Experiment and purchase decision
6.1 Sample description
6.2 Further descriptive sample statistics
6.3 Main results
7.1 Genetics and IQ-discrepancy criterion
7.2 Decision-making strategies and dyslexia
7.2.1 First buying decision
7.2.2 Second buying decision
7.5 Avenues for further research
A1. Survey structure and relations
A2. Logographic reading
A3. Brain areas associated with reading
A4. Gap model reading achievement
A5. Causal model of structures in dyslexia
A6. Hypotheses overview
A7. Conceptual model and results
A8. Product design
This thesis attempts to link dyslexia and serious reading and spelling difficulties to economic decision-making by using a grocery shopping context as stimulus. It is one of the first approaches that tries to analyse the possible effects of this specific learning disability (LD) in an everyday life situation that can have monetary and health consequences by means of three different newly designed multivitamin product variations. It was assumed that dyslexics are particularly challenged by the increasing information volume in nowadays that can lead to an information overflow, especially in a hectic supermarket surrounding. Furthermore, it was assumed that they are not able to filter crucial information from texts as well as controls when presented with a time constraint that is critical for making an optimal buying decision. It was discovered that being affected by this specific LD has some effects on grocery shopping decision-making by clearly increasing the probability of making a sub-optimal decision. Dyslexics’ decision-making in this context is influenced by worse working memory capacity (WMC), information processing skills, and logographic reading leading to worse name recall compared to a control group. Given enough time for making their decision in combination with simplified pictorial access cues can lead to a better decision-making quality.
Abbildung in dieser Leseprobe nicht enthalten
Abbildung in dieser Leseprobe nicht enthalten
Abbildung in dieser Leseprobe nicht enthalten
This thesis should provide deeper insights in the topic of adult dyslexia research in an economic context by looking at the everyday situation of grocery shopping. Aside from research on the connection of math anxiety and price formats (Suri, Monroe, & Koc, 2012) and dyslexic traits in entrepreneurs (Logan et al., 2008) there has not been research concerning possible effects of adult dyslexia on the consumer.
Consumer behaviour is constantly changing and has changed dramatically due to larger amounts of available information in the recent past (Clemons, 2008; Weiss & Timm, 2013). Everyone has to adapt to these changing circumstances equally, whether it is consumers or companies. New channels and information processes require new behavioural patterns and subsequently new strategies on both sides to remain successful (Clemons, 2008).
Furthermore, the consumer’s expectations towards the shopping experience have changed. Rises in total information volume lead to an inconceivable amount of information, which competes with the consumer’s attention (Anderson & de Palma, 2012). This leads to a decreasing time frame to attract the consumer’s attention. Therefore, todays predominant information overflow requires better filtering and selection strategies from the consumer. This change in total information volume can cause more stress (Jayakumar & Sulthan, 2013; Misra & Stokols, 2011). Subsequently, it may lead to problems in managing one’s life effectively, in particular for people with dyslexia or other reading and spelling difficulties. Moreover, one is increasingly tempted to consider other peoples’ opinions when considering a purchase. This can be done by quickly using different types of media including mainly smart-phones and tablet computers, which in turn increases the total information volume even more. Consequently, new circumstantial and technological trends generate even more information and can lead someone to become overwhelmed.
Dyslexia was first described in a case report by Morgan (1896) over 100 years ago. Understanding the neural systems for reading even date back to the year 1891 (S. E. Shaywitz & Shaywitz, 2005). In recent years, dyslexia and other learning disabilities including dyscalculia have become much more prevalent in scientific literature. This is due to an increasing number of publications and a spread of appealing applications of LDs towards other research fields beyond clinical psychology (Bund & Rohwetter, 2013; Logan et al., 2008; Suri et al., 2012).
On an epidemiological level there is a wide range of assumptions about the percentage of dyslexics in a population. The population at risk of developing dyslexia is wide spread and is not limited to the mentally ill and the economically disadvantaged (Hammill, Leigh, McNutt, & Larsen, 1987). Individuals of all ages can be affected by it (Hammill et al., 1987). According to Lyon, Shaywitz and Shaywitz (2003) “reading disabilities affect at least 80 percent of the learning disability population” (p. 2) in the broadest sense. Hence, reading and spelling difficulties are the most prevalent type of LD, which illustrates its importance for doing further research on it, especially for applying it to everyday life situations such as grocery shopping. According to the (European Dyslexia Association, 2013b) there are 30 million European people affected by some form of dyslexia and estimated prevalence rates range from 5% to 25%. Proposed prevalence rates for school children by other authors are similar and range from 5% to 17% (S. E. Shaywitz et al., 2003). But estimates are not always as high as the aforementioned. Yule et al. (1974) reported lower rates for a specific reading disability in children ranging only from 3.1% in the Isle of Wight study to 6.3% in a study done in London. In general, prevalence rates depend on geographical distribution and on the test administered, which leads to variations and inconsistency in the literature (Snowling, 1998).
Approaches that try to differentiate the appearance of dyslexia between gender propose ratios ranging from 1.5:1.3 (boys:girls) S. E. Shaywitz et al. (1990) to 4:1 (Snowling, 1998). The variety in sex ratios is similar to the previously described variety in prevalence rates. Nevertheless, there seems to be a consensus that this disability predominantly appears in boys (S. E. Shaywitz et al., 1990). Snowling (1998) mentions two main reasons why there may be a difference in prevalence rates between studies, which are namely teaching patterns and patterns of referral in school aged children.
Consequences of being affected with some degree of dyslexia range from having to attend alternative schools and, as a result, not completing high school1, to receiving a university degree by means of exhaustive coping mechanisms. In general, about 60% of dyslexic pupils have to repeat a year during their school career (Warnke, 1999). Nevertheless, a variety of degrees of severity make many school careers possible. Stothers and Klein (2010) reported a steady increase in the enrolment of students with LD in postsecondary education. Thus, dyslexia should not be seen as only compromising.
Since reading ability varies greatly in people ranging from slow readers to highly proficient, fluent readers, it deserves to receive more attention. I will illustrate this in the following: Recent evidence shows that reading disabilities represent the lower tail of a normal distribution of reading ability (S. E. Shaywitz & Shaywitz, 2005). Dyslexic people are often slow readers and have difficulties in using the sentence context for identifying printed words and comprehending texts accurately, especially when presented with time constraints or pressure of other kinds (S. E. Shaywitz & Shaywitz, 2005; Warnke, 1999). In daily life, these problems caused by a reading and spelling deficiency can significantly interfere with activities that require reading or spelling skills (Warnke, 1999). More precisely, poor performance in reading and spelling may become disabling on literacy-based activities such as joyful reading, mastering the reading of product labels in a supermarket or academic success (Ferrer, Shaywitz, Holahan, Marchione, & Shaywitz, 2010). The omnipresent information and stimulus overflow in today’s world is likely to lead dyslexics to not reading texts completely and/or avoid reading them precisely. Consequently, I hypothesize dyslexics might be overextended by all the available information they have to deal with throughout a day. This may be an important influencing factor for only reading text material briefly and hastily. Furthermore, job and social requirements can be challenging and are connected to a person’s lifestyle (Heikkilä et al., 2013). It is assumed that these challenges cause high stress levels especially in dyslexics, which might lead to even higher stress levels during a normal grocery shopping experience for them. This may become influential if one tries to find the product that fits one’s current needs best, because one would have to read product descriptions on packages diligently and precisely while being rushed or stressed by at least one exogenous factor such as job demands.
Increasing attention is illustrated well by appearance of dyslexia in articles in reputable German newspapers including “DIE ZEIT”. Articles such as “Wahnsinns-Typen” (Bund & Rohwetter, 2013) make informing about this specific LD easily possible. Those articles try to make clinical research available to a broad mass by combining it with economics and interesting other topics. However, those approaches are not always free of referring to and even using social stigmas such as insanity and madness, which gets illustrated best by means of luring language and generalizations expressed through headings such as “Wahnsinns-Typen” (Bund & Rohwetter, 2013). Therefore, scientific research should attempt to explain effects connected to LDs and to remove stigmas and prejudices by giving facts. On this note, Copalla (2007) advocates for a perception and evaluation of dyslexia as an asset opposed to a handicap, because dyslexics are believed to be able to focus more on really important facts (Bund & Rohwetter, 2013). This gets demonstrated well by a large number of dyslexics in the workforce who hold a position with responsibility as I am going to show adjacently.
In regards to the evaluation of dyslexia as an asset, it is of particular interest to look at successful and prominent examples in the economy. Logan et al. (2008) found out that every third founder of a company can be included among the group of dyslexics. In the United States, the number is as high as 35% for entrepreneurs. Additionally, this specific LD should occur eight times more frequently in a manager compared to its occurrence in the normal population. Good examples for this phenomenon ranging from the business to the medical field including Steve Jobs (Apple), Ferdinand Piech (Volkswagen), John Chambers (Cisco) and James LeVoy Sorenson (health care entrepreneur) (Coppola, 2007; Bund & Rohwetter, 2013). Furthermore, at least two Nobel laureates, namely Baruj Benacceraf and Niels Bohr, were dyslexic (S. E. Shaywitz & Shaywitz, 2005). As these examples illustrate, dyslexia can have detrimental effects, but does not hinder someone from becoming successful and influential in society. In some cases, it allows for opportunities, due to different ways of approaching problems and looking for efficient solutions (Copalla, 2007).
The aforementioned changing demands in today’s world, in combination with more attention dyslexia has received recently, led to connect dyslexia with economic decision-making. This sparked the development of an experiment that tests for possible effects of dyslexia on decision-making in a particular grocery shopping situation. Therefore, it is crucial to define dyslexia beforehand.
Dyslexia is a specific learning disability that shows a wide range of variability. If one wants to understand the origins and mechanisms of dyslexia it is crucial to start with defining this LD. “The origins of the word ‘dyslexia’ suggest a difficulty in the use of the words rather than just the reading of them. The term ‘dyslexia’ indicates a more complex syndrome than just a problem with reading” (European Dyslexia Association, 2013a). This quote displays the complexity of this specific LD well. Dyslexia is comorbid and is oftentimes accompanied by other but very different deficits in attention like ADD or ADHD (Knivsberg & Andreassen, 2008), spelling, auditory and visual perception or motor domains (Ramus, 2003). For instance, clinical overlap with attention-deficit hyperactivity disorder (ADHD) is estimated to be between 20% and 50% (Duncan et al., 1994). Although, there seems to be a consensus on certain core deficits, it is very difficult to define what constitutes dyslexia exactly and to distinguish between effects of dyslexia due to other occurring and seemingly connected effects, which are caused by other disabilities (Lyon, Shaywitz, & Shaywitz, 2003). The following definition by Lyon et al. (2003) has drawn much attention in recent years. Therefore, the author chose it as a foundation for this study. The definition has been modified2 due to reasons of complexity when trying to capture dyslexia with all its influencing factors entirely.
This definition constitutes the foundation and initial point for this research. Most other definitions put specific emphasis on single parts of the aforementioned definition. They specifically stretch the abnormal neurological component of the disorder for making a sound diagnosis (Hammill, Leigh, McNutt, & Larsen, 1987; Warnke, 1999). For example, according to Hammill et al. (1987) a central nervous system dysfunction (CNS) has to be proven to label somebody as learning disabled. In order to come to an understanding of dyslexia one has to keep neurological abnormalities in regard. This has to be done on a theoretical level at least, because these aspects cannot be directly evaluated in this study’s participants.
Learning disabilities differ from other handicapping conditions in the respect that they do not arise from one factor alone (Hammill et al., 1987). This fact shows why dyslexia can be labelled a specific LD, in which its etiology is a combination of genetic, non-genetic constitutional and environmental factors (Hammill et al., 1987; S. E. Shaywitz & Shaywitz, 2005; Warnke, 1999). Especially these interactions make it difficult to grasp the core deficits of dyslexia distinctively.
Dyslexia in adults is a very specific and more complex part of dyslexia research, because it entails some difficulties that do not complicate dyslexia research with children. The behaviours associated with core deficits of dyslexia change with age, due to developmental interactions and processes that may compensate for certain deficits (Snowling, 1998). According to S. E. Shaywitz et al., over a lifetime, dyslexics are likely to develop coping mechanisms, better word memory and a larger vocabulary, which helps them to master their everyday life successfully, irrespective of the involvement of activities that are connected to literacy. Nevertheless, causes and mechanisms of deficits in adults are comparable to those in children because they stem from the developmental stages of childhood (2003). For this reason, dyslexia is usually studied in children during the acquisition stage of literacy to develop a better understanding of the core deficits and mechanisms, and then findings can be applied to adults (S. E. Shaywitz & Shaywitz, 2005). Problems when trying to develop an all-encompassing definition illustrate issues of complexity that make the proposition of a general statement on what core deficits distinctively constitute a dyslexic phenotype problematic. This complexity is illustrated by the varying characteristics and degrees of severity in patients with this specific LD (Lyon et al., 2003).
Those characteristics and degrees of severity can be related to 5 critical elements identified by the National Reading Panel (2000): phonemic awareness, phonics, fluency, vocabulary and comprehension. According to Warnke (1999), these critical elements are related to a variety of entities like vision, hearing and particular brain areas. Concerning the 5 elements and entities listed above the following symptoms can be found in dyslexics. This list makes no claim to be complete; however, it lists symptoms that are often mentioned in the literature and are directly important to this study.3
Abbildung in dieser Leseprobe nicht enthalten
Table 1: Symptoms
These symptoms can be found in children and adults, although to different extents. A number of studies of young adults with childhood histories of dyslexia indicate that they may develop some accuracy in reading words, albeit very slowly (Snowling, 1998; Stothers & Klein, 2010; Warnke, 1999). A very slow reading rate is assumed to be a manifestation of dyslexia in adulthood (Stothers & Klein, 2010). In some cases, dyslexic adolescents and young adults who show accuracy in reading words may mistakenly be assumed to have “outgrown” their dyslexia; however, the rate of reading as well as spelling ability is the most useful clinical diagnostic instrument in differentiating average from poor readers in students in secondary school, college, and even graduate school (S. E. Shaywitz & Shaywitz, 2005).
In the literature, there are three major theoretical approaches, which try to explain the causes that lead to the aforementioned symptoms and deficits shown in dyslexics, namely the phonological, the cerebral, and the magnocellular theory (Ramus, 2003). These theoretical approaches try to describe dyslexia entirely by not classifying dyslexics into different subtypes. On the contrary, there are smaller approaches that try to differentiate between different subtypes of dyslexics, including the following three phenotypes: decoding, phoneme awareness and single word reading4. This classification is contrary to classifications proposed by Meyer (2000) and Wilcke et al. (2009). Complex interactions of deficits make a clear distinction of dyslexics very difficult. Furthermore, a differentiation between verbal, phonological and non-verbal dyslexia has been made in the literature but is not applicable to this study (Stothers & Klein, 2010). Although trying to simplify appropriately, most classifications so far have not served the purpose of giving a better overview. Thus, despite existing evidence for different phenotypes there is a current lack of evidence in favour of distinct subtypes (Snowling, 1998).
Concerning reasons of complexity of dyslexia every single of the major theories provides reasonable explanations, but up to this point only the phonological theory has gotten the most support (Ramus, 2003; S. E. Shaywitz, Mody, & Shaywitz, 2006; S. E. Shaywitz & Shaywitz, 2005; Snowling, 1998; Stothers & Klein, 2010; Tijms, 2004; Warnke, 1999). It is a theory that builds on the assumption “that speech is natural whereas reading is acquired and must be taught” (S. E. Shaywitz et al., 2006, p. 278). The phonological theory explains dyslexia by stating that the deficits in dyslexics, neurobiological in origin, stem from a specific impairment in the representation, storage, and/or retrieval of speech sounds. The Latin alphabetic system we use consists of graphemes and phonemes, meaning visual letters and sounds of speech. Creating a correspondence between these two different levels is required for being able to read and thus use this system effectively, especially for managing everyday life (Ramus, 2003). Gaining the insight that spoken words can be pulled apart into smaller units of speech, syllables and phonemes for instance, and that the letters in a written word represent these sounds is crucial for the proper use of language (S. E. Shaywitz et al., 2006). Decoding and comprehension are the two main processes that comprise reading. On the basis of this, Ramus (2003) points out the last step in learning to read properly is by making a connection between spoken words, letters and phonemes; this means segmenting spoken words into their underlying components and linking letters to sounds, which is a main impairment in dyslexics (S. E. Shaywitz & Shaywitz, 2005). Further evidence for this argument is provided by the fact that dyslexic subjects performed poorly on tasks requiring phonological awareness (PA)5, which is the main capacity necessary for phonological processing (Ramus, 2003). Hence, their core deficit lies in their phonological and language skills (Duncan et al., 1994). This impaired ability in decoding and building on this in identifying single words is a domain-specific deficit and causes problems in drawing meaning from text. But it does not affect non-phonologic abilities (S. E. Shaywitz & Shaywitz, 2005). Those domain-specific difficulties of poor phonology are independent from global intelligence and are often misleadingly ascribed as a “lack of motivation” (S. E. Shaywitz & Shaywitz, 2005; Snowling, 1998).
The influence of another issue, a solely auditory impairment, is widely discussed in research on phonology and dyslexia. Ramus outlines that although a single core deficit for the problems shown by dyslexics has not been identified, their appearance can certainly have an impact on phonological skills and their development. However, contrary to what one might expect a phonological deficit can arise even without auditory, visual and motor impairments. Hence, auditory disorders are not necessary for phonological deficits to arise, even though they can influence phonology (2003).
As it has been shown, many factors can be influential to spelling and reading. Since reading speed and word decoding are linked conceptually, both are limitations associated with underlying phonological deficits, additional evidence for this theory is provided by the fact that slow reading and impaired word decoding appears constantly in dyslexics possibly due to poor decoding skills (Stothers & Klein, 2010). Logographic reading6 illustrates this the best. Also, difficulties in verbal memory and phonological processing seem to have a common underlying impairment, according to Tijms (2004) they do not represent a double-deficit, rather manifestations from a common root namely the dysfunction in the encoding of speech sounds. Therefore, “poor verbal short-term memory and slow automatic naming in dyslexics point to a more basic phonological deficit [as well, which perhaps has] to do with [worse] quality of phonological representations, or their access and retrieval” (Ramus, 2003, p. 842). Following this, phonological deficits represent the most robust and specific correlates in Ramus’ (2003) study. Furthermore, the fact that dyslexics of all ages display phonological processing problems underlines the importance of two things: first, the identified core deficits in phonological processing and second, its applicability to dyslexia research in adults, because they both account well for different manifestations shown across a life-span (Snowling, 1998; Stothers & Klein, 2010). In many adults, slow reading is persistent even in well-educated readers (Stothers & Klein, 2010). For instance, Snowling (1998) shows that it is possible for many adults with a childhood history of dyslexia to become fluent readers, but only few overcome their spelling problems. This is one key reason for using a spelling test as main diagnostic instrument.
Although the phonological theory receives the most support by researchers in the field, it shows potential weaknesses. Challengers criticize its lack of accounting for sensory and motor disorders in a significant proportion of dyslexics (Ramus, 2003). Also, an interpretation bias of results and a bias of importance lead to contradictory statements in some publications. For instance, Tijms (2004) suggests “phonemic awareness does not make a unique contribution to the reading and spelling skills…[so that] poor performance on phonemic awareness tasks does not reflect a core deficit…but is secondary to the accuracy of phonological coding in the mental lexicon” (p. 307). These points of critique illustrate quite well why it is difficult to develop a single theory that offers a sound explanation for all core deficits shown by different dyslexic phenotypes, which is mostly due to comorbidity of this specific LD.
Despite a reasonable sounding approach by the cerebellar theory, which is based on the fact that the cerebellum should play a role in the automatization of over-learned tasks such as reading, Ramus (2003) was unable to provide compelling evidence for this theory, because he didn’t find any influence of motor and thus cerebellar performance either on phonology or on literacy. Another approach has been made by the magnocellular theory, which tries to integrate findings from all three major theories in the field. Although this theory manages to take auditory, visual, motor, tactile and phonological manifestations of dyslexia in regard, the main point of criticism are problems with replicating findings of auditory and visual disorders in dyslexics as well as its inability to explain the absence of motor and sensory disorders in many dyslexics. Additionally, it does not allow for making a prediction of phonological deficits based on auditory deficits (Ramus, 2003).
Thus, all three major theoretical approaches contain some accuracy, as they try to propose a theory on core deficits represented in all dyslexics; however, they all show one similar weakness by just mentioning interactions between deficits in different domains as a side note. Only theories that lay most emphasis on these interactions and link them with each other can attempt to capture this specific learning disability in its entire complexity.
Based on the literature in the field, I propose to see dyslexia as the complex learning disability that it is, which might be explained best by the following three-step approach: First, lower level functions in auditory and visual processing as they are found in S. E. Shaywitz and Shaywitz (2005) and Warnke (1999) underlie deficits in phonological awareness and processing. Although these deficits are not always and exclusively found in dyslexics, the importance of making this connection is often underestimated because an impairment in hearing or vision, in the so-called “low-levels” (Snowling, 1998), can make any neurological predisposition worse, trigger the onset of symptoms, or prevent children from developing functional neural connections in these areas during acquisition of language (S. E. Shaywitz & Shaywitz, 2005). This might result in problems normally ascribed to dyslexia. Additionally, it can block access to higher-order processes such as using knowledge about the connotation of a word (S. E. Shaywitz & Shaywitz, 2005). Second, as described previously, deficits in phonetic word decoding can make spelling and reading an exhausting and difficult process when dealing with underlying deficits. This often results in a slower reading rate, less accurate reading, and less accurate recall of memorized content (Warnke, 1999). Finally, problems in step one and two block higher order processes including abstract thinking, because for example perception (and motor) problems can play a crucial role and be very influential and far-reaching in dyslexia (Ramus, 2003). Thus, an impaired building of connections between lower- and higher-levels, should be taken into regard when attempting to provide a theoretical approach that is more likely to cover the basic deficits in connection with their crucial interactions holistically.
To attempt to fully capture dyslexia, heritability must be considered to some degree because it is a contentious and widely discussed topic among researchers. Twin studies by Bishop et al. (1999) suggest that phonological deficits have a largely genetic origin. Also, evidence provided by (Finucci et al., 1976), Meyer (2000) and Wilcke et al. (2009) gives estimates of heritability of dyslexia from 40-70%. Meyer (2000) reports estimates of roughly 40% of dyslexic children who have siblings affected and 40% of dyslexic children have parents who are affected to some extent. Referring to clinical studies from the past three decades Snowling (1998) propose that numbers for heritability can be as high as 50% for siblings of dyslexics, and 50% for children of dyslexic parents. More twin studies even suggest heritability of up to 70% and a greater heritability of phonological than visual aspects of reading and spelling difficulties compared to reading deficits (Snowling, 1998; Warnke, 1999; Wilcke et al., 2009).
Since dyslexia is characterized by a complex phenotype, the involvement of more than one gene is likely (Meng et al., 2005), and at least four prominent candidate genes have been related to dyslexia so far (Fisher & Francks, 2006). More precisely, findings by (Schumacher et al., 2006) indicate some evidence for a genetic variation of loci within the DCDC2 gene, which might be a particular contributor to the development of spelling disorders, because DCDC2 is localized in the brain regions where fluent reading occurs (Meng et al., 2005). In this connection, these identified loci within DCDC2 are related to the aforementioned three reading phenotypes (decoding, phoneme awareness, single word reading) on chromosomes 1, 6, 15 respectively (Grigorenko et al., 1997). Furthermore, since DCDC2 is associated with cortical neural migration, an identified deletion of DCDC2 can lead to alterations in neural migration and maturation caused by down-regulating this gene (Meng et al., 2005). Further evidence for a deletion was also found by Wilcke et al. (2009). Thus, Wilcke et al. (2009) suggests “impaired neural migration may play a role in the etiology of dyslexia” (p. 9). This is supported by Schumacher et al. (2006). Conclusively, evidence from patients affected with severe dyslexia who have shown patterns of abnormal neural migration and maturation and morphological differences underline dyslexia’s heritability (Galaburda, 1993).
Making a connection between heritability and IQ, Wadsworth et al. (2000) found a linear relationship between these variables, but this fact has been challenged by other studies (Deimel, 2002). In the context of high IQ-levels, the appearance of a significant transmission disequilibrium within the DCDC2 gene suggests that a specific effect on reading performance, but not a more global effect on brain function, is likely (Meng et al., 2005). In subjects with relatively high IQ-scores, genetic influences tend to be stronger compared to environmental influence (S. E. Shaywitz et al., 2006).
According to the International Statistical Classification of Diseases and Related Health Problems by the World Health Organization (2010), dyslexia is defined as a developmental disorder of neurological origin that is “associated with abnormal neurobiological development relating to both brain structure and brain function” (Warnke, 1999, p. 4), which shows why taking a closer look at neurobiological structures and pathways involved in this disorder is important.
Although a more global effect of dyslexia on brain function is not likely, many researchers have found abnormalities in the development of neural connections in the left cerebral hemisphere, especially of left-hemisphere posterior regions, which has been associated with relevant reading and linguistic tasks. This neural network is particularly compromised by altered neural development (Meyer, 2000). A range of neuroimaging (Lyon et al., 2003; Meng et al., 2005; S. E. Shaywitz, 1998; S. E. Shaywitz et al., 2003, 2006) and brain potential (Duncan et al., 1994) studies show altered brain activation patterns in dyslexics. This converging data by many researchers indicate a lack of activation of left posterior brain areas during reading as a “footprint” of dyslexia (S. E. Shaywitz et al., 2006). According to Lyon et al. (2003) dyslexic persons not only activate differently, but also use brain regions differently when reading or solving tasks that require an examination of a certain topic including linguistic aspects compared to controls. Even well compensated adults showed different patterns of left-hemisphere brain activation during phonological processing tasks (Snowling, 1998). Simultaneously, the inferior frontal gyrus, also called Broca’s area, shows increased activity in children and adult dyslexics while performing reading tasks. This might cause an overload of processing capacity of anterior brain regions. In non-impaired readers, the anterior, namely inferior frontal gyrus, and posterior, occipito-temporal left-hemisphere, regions are connected well (S. E. Shaywitz et al., 2003).
Each of these three brain areas is associated with different functions (Figure 5 in the Appendix).7 Most mentionable is the disruption in the left occipito-temporal area (LOT), which is thought to be responsible for problems in reading words and naming pictures of these words, and should be accountable for a lack of accuracy, fluency and speed in reading (S. E. Shaywitz et al., 2006; S. E. Shaywitz & Shaywitz, 2005). Since there seems to be an information overflow in today’s society, impaired readers may be particularly challenged on the text comprehension level by this. S. E. Shaywitz et al. indicate that with maturation there seems to be different ways of compensation by the brain for the lack of functioning of posterior regions. First, the disruption of the anterior-posterior pathway seems to force anterior brain regions to process more information, so that compensation might happen by means of a shift of activation patterns towards anterior brain regions like the inferior frontal gyrus (2006). Second, according to S. E. Shaywitz and Shaywitz, connections between LOT and right prefrontal gyrus have been found in persistently poor readers; the latter region is associated with memory. These readers seem to read real words accurately, primarily by memory, but read unfamiliar words far less accurately (2005). This evidence suggests that they have not learned to use phonological strategies to analyze unfamiliar words, and that they appear to rely more on memory-based strategies and systems (S. E. Shaywitz et al., 2006). It is likely that this makes them more dependent on their vocabulary and requires more effort to perform equally well on tasks. However, differently working brains structures do not automatically imply that they are rigid (S. E. Shaywitz & Shaywitz, 2005).8
Despite all evidence in the present state of knowledge, drawing causal conclusions from genetics and brain activation studies has to be done with caution due to problems with the interpretation of the BOLD response used in fMRI studies and contradicting evidence for the influence of certain genes (Galaburda et al., 2006). Nevertheless, neuroimaging methods can serve as useful in many ways, especially when diagnosing bright, highly-accomplished young adults who can compensate for their lack in phonological processing by using other mechanisms and do not fail normal reading tests (S. E. Shaywitz et al., 2006). Using the fMRI method for diagnosing would be applicable to this study’s sample but cannot be done due to money constraints.
Besides genetic and non-genetic constitutional factors, there is a third group of factors that gets significant attention in the literature. These are so-called external or environmental factors including opportunity for learning, quality of teaching, and language background. They are assumed to have a crucial impact on the degree of severity of dyslexia (Warnke, 1999).
Coming from the perspective of the phonological theory, where reading is a skill that must be acquired, researchers have pointed to the importance of one’s background, pre-reading abilities (Lyon et al., 2003) and adequate reading instruction (Warnke, 1999), especially due to perceived effects on children. Additional evidence for the crucial role of adequate reading instruction points to success possibilities of early interventions concerning children who were able to overcome their difficulties after getting appropriate instruction and training of their semantic knowledge and visual memory (Ferrer et al., 2010). These possible interventions seem to have further positive effects on lowering the degree of severity in adults.
Furthermore, there seems to be a correlation between differences in exposure to language in children who grew up in less educated homes and their reading abilities, which might provide more reasons helping to explain later reading difficulties (S. E. Shaywitz et al., 2006).
One more influential factor according to phonological theory is the strong difference in the regularity of the grapheme-phoneme correspondence of two languages (English and German) of the same language group (Wilcke et al., 2009). Additionally, phonemes are learned from care givers in the infant stage of one’s life (Rasilo, Räsänen, & Laine, 2013). This early development and differences in the regularity are the reason for the necessity of evaluating languages separately in terms of choosing only German native speakers for participation.
Adult reading comprehension has become more important in research in recent years (Jacobson, 2011). As it has been reported that uptake and comprehension of text is influenced by genetic, non-genetic constitutional and environmental factors.
According to Jacobson (2011) reading comprehension can be perceived as a black box that encloses a mostly invisible, individual and intimate process. Nobody knows with certainty what the crucial determinants are. Snowling (1998) describes the ability to read proficiently as a product of both linguistic comprehension skills and decoding. In trying to evaluate the main determinants of reading comprehension, work by Kruidenier (2002) promotes the “four-component” approach, which describes the main components of reading as being, alphabetics, vocabulary, fluency and comprehension. According to Jacobson (2011), this approach should be extended by adding background knowledge and the reader’s socio-cultural profile to the mix of reading comprehension. This on-going debate illustrates that nobody has grasped reading comprehension in its full complexity yet. Nevertheless, compelling evidence suggests that significant differences in reading comprehension in favor of readers without disabilities were found when using timed tests (Stothers & Klein, 2010). The next chapter takes a closer look at the latter fact.
Although an ambiguous picture on whether “statistical differences in reading comprehension between adult readers with LD and those without” (Stothers & Klein, 2010, p. 210) are present in the literature, the picture gets clearer when one includes reading mechanics in the analysis as well. Additionally, some studies have found “significantly lower reading comprehension scores for participants with LD” (Stothers & Klein, 2010, p. 210) and report significantly lower scores in reading mechanics for the group with phonological deficits. Hence, according to expectations, reading mechanics may be identified as one contributing factor to slow reading speed shown by participants with PA deficits who decode words slowly (Stothers & Klein, 2010).
They can result in a variety of symptoms besides slow reading speed. Relating to this study other occurring symptoms with direct consequences on reading can be the following: logographic reading, limitations of verbal short-term memory, working memory, retrieval of phonological information from long-term memory, deficiencies in rapid naming and object naming tasks, establishing and later accessing, adequate phonological representations and reading comprehension (Snowling, 1998).
Furthermore, specific text formatting including font size, text structure, color, size and font of headings, bold words, words written in italics, amount of pictures and graphs can work as access cues relating to perception problems and thus may contribute to text comprehension positively (Meij & Meij, 2013).
One more important factor that has not been emphasized yet, which has been proven to contribute to reading comprehension in dyslexics, is the introduction of a time limit. Its importance is illustrated by Stanovich et al. (1994) who found that when time constraints did not play a role people with phonological deficits were able to compensate for poor decoding and slow reading, whereas differences were apparent in studies that used timed tests. In line with this are more findings by Stothers and Klein (2010) that indicate mean scores for reading comprehension were not statistically different between the group with phonological deficits and the no-deficit group when no time limit was present.
These findings are crucial for the design of this study. On account of sample selection criteria, it was important to consider the latter factors when examining how accurate the participants’ understanding of the informational brochure is, and whether it is possible for a non-LD reader to filter the crucial and important information to make an optimal9 buying decision out of the provided text. Since reading comprehension is a complex process, recall accuracy of product names and reasons for making the optimal purchase decision is the only measure that can be used to evaluate reading comprehension in this study.
Although many findings provide compelling evidence that deficits attributable to dyslexia compromise abilities related to literacy in adulthood, e.g. 49% of those severely disabled as children remained poor readers as adults (Meyer, 2000), there is also compelling evidence for dyslexics who only show slight signs of deficits or no obvious serious impairments and normal reading comprehension, even though they still have basic level decoding difficulties (Snowling, 1998). This can be mostly accounted for by the development of successful compensation mechanisms. Feature characteristics and the degree of severity of dyslexia in adults have often been consolidated and thus are relatively stable over time, which often makes deficits attributable to dyslexia hard to detect (S. E. Shaywitz, 1998), and complicates finding uncompensated dyslexics (Duncan et al., 1994).
Since dyslexia is such a complex specific LD with many different degrees of severity, individual coping mechanisms for managing personal deficits vary to a great extent, so that it is worth to examine them in more detail. Compensatory resources are for instance semantic knowledge, use of context, visual memory, verbal ability (S. E. Shaywitz et al., 2003), larger vocabulary, strong reasoning abilities (S. E. Shaywitz et al., 2006) and years of education (Stothers & Klein, 2010). These resources can be subsumed as greater cognitive abilities, which are believed to provide some degree of compensation for reading difficulties, since they are proposed to help a struggling reader to decipher the meaning of unknown words (S. E. Shaywitz et al., 2006). This has been found particularly in untimed testing because dyslexics may have time to compensate for their difficulties (Stothers & Klein, 2010). Results suggest that especially “strengths in perceptual organization [(PO)10, e.g. grouping,] may serve as one means of the compensation” (Stothers & Klein, 2010, p. 232). Hence, timed measures of reading have to be used to identify those dyslexics, especially young adults at the university level (S. E. Shaywitz, 1998).
Moreover, According to Snowling the fact that dyslexics can achieve normal linguistic comprehension can be seen as advantageous, because they have to apply different strategies compared to readers without phonological deficits to reduce the load on their working memory. They have to analyze texts with main focus on important key points to comprehend them relatively well, because in most cases analyzing texts in detail would overload their working memory. Furthermore, the ability to monitor their comprehension processes more or less consciously is assumed to contribute to good reading comprehension (1998).
Due to simplification, an attempt is made to make groups of compensated dyslexics with similar phenotypes. Only the two groups, whose applicability is most likely to this study, are mentioned here. The first group comprises compensated dyslexics with still persistent problems, so-called false negatives. They “have overcome their primary reading problems and no longer fulfil the criteria for specific reading retardation but may have serious problems with spelling and written work” (Snowling, 1998, p. 5).
The second group consists of high-achieving adults who do not seem to be noticeably affected by their dyslexic abnormalities. In those adults, the quality of coping with their deficits and concealing them depends on the effort invested in decoding. As stress levels increase, the effort invested in decoding has to be kept up, which overloads working memory and impedes processing (Stothers & Klein, 2010).
In some cases, affected, but well-compensated, individuals become really successful but were diagnosed with a LD only later in life, e.g. Alan Meckler, CEO of online imagery hub Jupitermedia, was diagnosed at the age of 58 (Coppola, 2007). This points to the importance and impact of successful coping mechanisms developed throughout life. Better cognitive abilities build the foundation for successful development of those mechanisms that help concerned people to become successful and high-achieving either in business or other fields. Nevertheless, in everyday life one is often exposed to stressful situations. Such stressful conditions can compromise resources available for compensation, such as memory (Wolf, 2009). Therefore, generating a stress level that is comparable to one experienced every day was necessary to produce somewhat realistic conditions. As well, since there is a relatively low prevalence of uncompensated dyslexia in adults, a large sample size is just as crucial as stressful conditions to find significant effects (B. A. Shaywitz, Fletcher, Holahan, & Shaywitz, 1992).
In today’s literature there is an intense discourse about the discrepancy criterion described adjacently. Since there is a variety of possible conditions that can accompany dyslexia, it is difficult to determine any clear and appropriate cut-off value for diagnosing dyslexia (Deimel, 2002). Nevertheless, Rutter and Yule (1975) set standards by establishing the use and inclusion of IQ in the diagnosis of spelling disability in 1975. Since then, there has been a widely accepted consensus on this classification criterion of dyslexia.
From a statistical perspective, the IQ-discrepancy criterion is the correlation between ability (IQ) and proficiency (spelling performance). This correlation is not 1, because there are exceptions to this norm, for example people who write better than their IQ-score indicates. In Germany there is a consistent positive linear relationship between spelling performance and IQ at about 0.4 (Deimel, 2002), which illustrates the variety of different levels occurring on both measures that seem to be linked, and therefore can be used for diagnosing someone with a learning disability. Furthermore, to ensure that standardization works for such measures, it is crucial to choose an appropriate allocation base. For example, the inclusion of special needs students has to be decided on (Deimel, 2002).
Since dyslexia is a learning disability that basically affects the reading and spelling parts of cognitive functions, a valid dyslexia diagnosis requires a severe discrepancy between a person’s ability and performance (Meyer, 2000; S. E. Shaywitz et al., 1990). First, it can only be given if an affected person achieves a global IQ-score above 85 points (Wilcke et al., 2009). Second, given that the IQ-criterion is fulfilled, the achieved t-score in the spelling test has to be at least 1.5 standard deviations below the score expected according to the achieved IQ-score. Meaning, if a person achieves a global IQ-Score of 10011 he has to have an actual t-score below 3512 in spelling performance (Deimel, 2002). Proposed numbers for a necessary standard deviation for giving a sound diagnosis range from 1 to 2.5 (Deimel, 2002; Ferrer et al., 2010; Warnke, 1999). Therefore, the use of 1.5 standard deviations seems to be a reasonable number for this study, as supported by Ferrer et al. (2010) and Warnke (1999).
According to this criterion, if a person either achieves only an IQ-score below 85 points, any problems with reading and spelling can be considered an expected performance deficit. He would be viewed as an “under-achiever”13 (Thorndike, 1963) who is generally impaired beyond the area of reading, spelling and learning (Deimel, 2002). Therefore, he cannot be labelled as learning disabled.
On the contrary, a lack of difference in skills of discrepant and non-discrepant readers has led to an evolving body of critique. Exemplarily, B. A. Shaywitz et al. (1992) found no differences between discrepant and non-discrepant readers on variables such as visual perception or language skills. This is supported by findings by Fletcher et al. (1994) who did not find differences on underlying abilities needed for the acquisition of reading skills either including phonological awareness, vocabulary knowledge, or verbal and nonverbal short-term memory. Similarly, Stanovich et al. (1994) found no differences between the two groups on word recognition, phonological or orthographic measures.
Researchers including Badian (1997) and Meyer (2000) argue against this criterion by showing that discrepant readers are far more likely to have multiple deficits, which can result in an overload of deficits that makes proper reading extremely difficult, but are not clearly attributable to dyslexia. Additionally, the aforementioned findings explain why oftentimes people with reading and spelling problems do not get diagnosed with dyslexia, even though phonological deficits are often present. Therefore, Wadsworth et al. (2000) among others cast doubts on the validity of global IQ as a valid measure for identifying dyslexics due to a variety of possibly influential factors not directly related to dyslexia, which can alter a person’s IQ-score as well as his reading and spelling performance, and thus lead to an inconsistent picture. For example, a person can have enormous strengths in one cognitive domain that does not get appropriately portrayed in his global IQ-score. Hence, Meyer (2000) proposes three alternative models to diagnose dyslexia: “(1) listening comprehension versus reading comprehension, (2) assessing only word recognition and decoding with no reference to discrepancy, and (3) failure to respond to treatment protocols” (p. 328). All of her alternative proposals try to approach the assessment of a specific aspect of dyslexia in more detail compared to the single use of global IQ such as phonological awareness. However, by focusing only on one specific aspect in diagnosing developmental dyslexia in children, they ignore other just as important criteria and do not test for other causes including perceptual problems. Additionally, the measurement of verbal IQ was proposed because it assesses verbal abilities that can affect a person’s reading ability, due to an indirect influence of phonological factors on reading, which in turn makes it a direct predictor of reading accuracy in adults, (Ferrer et al., 2010; S. E. Shaywitz et al., 2003).14
The applicability of using the IQ-criterion for this study to classify a subject either as dyslexic, severely affected or control is underlined by the existing correlation between reading, spelling and IQ, because typical readers showed a substantial concentration of high scores for both reading and IQ (Ferrer et al., 2010). Further findings by Ferrer et al. (2010) indicate mutual influential effects. Therefore, according to a high IQ-score, dyslexics should be able to learn to read adequately or even quite well. However, they seem to lack a mechanism for the close coupling. Therefore, a spelling score below expectations makes it possible for them getting diagnosed with this specific learning disability.
Finally, one has to keep in mind that this research field is characterized by many different methodological approaches that have been used in the studies mentioned in this discussion. Hence, this leads to different and often conflicting results and conclusions as shown in this section (Deimel, 2002).
It is possible that people affected with dyslexia can find out about their disability later in life, because their coping mechanisms have been strong enough to prevent compromising difficulties to be noticed, and they can become exceptional in business and academics (Bund & Rohwetter, 2013; Logan et al., 2008). Although success stories are known some infamous misconceptions about dyslexics including labelling them as stupid and retarded are still persistent and present today. This is illustrated by reported additional emotional, behavioural (Knivsberg & Andreassen, 2008) and psychiatric problems (Warnke, 1999) of students affected with severe dyslexia, even though those problems are mostly secondary to the impairment in reading and spelling. These problems are expressed in terms of depressive traits and significantly lower self-esteem as well as an increased feeling of anxiety and less competency in their academic work (European Dyslexia Association, 2013b; Knivsberg & Andreassen, 2008). According to Alexander-Passe, the large extent to which this can influence individuals is illustrated by the report of life- long mental illness issues, which can result in self-harm reaching as far as to suicide attempts, due to many factors including outside pressure by peers and at work. Those attempts are considerably higher in adults with dyslexia as in the same age group. Trauma and anxiety can be caused by exposure to stigmatization that can be detrimental to ones reading and spelling performance (2012). Throwing dyslexics in the same pot with narcissists, and psychopaths as it has been done by newspapers including DIE ZEIT (Bund & Rohwetter, 2013), does not contribute to a removal of the aforementioned stigmatization. Subject’s participation during this study might be affected by experienced stigmatization and trauma throughout one’s life.
The last chapter provides an introduction to the topic of dyslexia, its origins, mechanisms, prejudices and the state of the art. This disability’s roots lie in distinct brain processes, but can be managed quite well by the development and use of coping mechanisms. These directly apply to an economic context by influencing the management of everyday tasks. It should have become clear that dyslexia is a specific, heritable learning disability that can have strong effects on affected people (S. E. Shaywitz & Shaywitz, 2005; Snowling, 1998; Stothers & Klein, 2010). In many cases, these people show distinct weaknesses in respect to reading and spelling, but have average or even high global IQ-levels. This does not make them less intelligent than unaffected people overall (Fletcher et al., 1994; B. A. Shaywitz et al., 1992). Moreover, it has been shown that dyslexia can have considerable effects on actions related to literacy. Many decisions in nowadays require the gathering and filtering of loads of information in a short period of time to come to a sound conclusion. Therefore, a connection to everyday life can be assumed, for example during grocery shopping, which could be affected by dyslexia.
Classical cognitive psychological decision-making theories, for example by Stanovich and West (2000), propose a dual process model, which differentiates between two types of cognitive processes. According to Kahneman, one is believed to be responsible for intuitive judgments based on percepts that come to mind quickly and effortlessly, called System 1, and the other is believed to be responsible for reasoning and consciously monitored judgments that are more effortful and come to mind slower, potentially governed by rules, called System 2. With the help of different decision-making tasks, it can be evaluated whether System 1 is likely to be responsible for making an intuitive decision or whether System 2 is more likely to be responsible for making a more conscious decision. A decision outcome that seems to be accounted for by the latter system can also be due to the detection of an error, intuitively made by System 1, that gets consciously overridden by System 2. On the contrary, being under cognitive load normally leads to an expression of manifestations of intuitive thought that are normally inhibited. Inconsistencies in participants’ decisions have been linked to accessibility of criteria influential to a particular decision. Accessibility is mainly influenced by motivational relevance and level of arousal of the information provided. Low accessibility and habitualization can lead to the subconscious use of heuristics that are able to produce biases (2003).
According to Verplanken and Aarts (1999), habituated behavior is understood as behavior that, once repeatedly and satisfactorily executed, leads to an automatic response to specific cues. The following are heuristics and biases that may play a role in this study: affect heuristic, prototype heuristic, framing effects (Kahneman, 2003). Affect heuristic means every stimulus causes a, not always conscious, affective evaluation, the emotional core of an attitude, which biases a decision. The prototype heuristic is described as the process of ad hoc substituting a prototype attribute for an extensional target attribute, because the former comes more readily to mind (Kahneman, 2003). Subsequently, the target attribute is no longer factored in the judgment, and this may lead to a suboptimal decision. Therefore, completely unknown products had to be designed. According to Kahneman this substitution seems to be a general characteristic of System 1, whereas System 2 has to be consciously aware of this bias to increase the likeliness of detecting it and overriding the decision that would have been made based on factoring in false attributes by System 1. The corrective operations of System 2 can be compromised by time pressure and by performing tasks at different day times counteracting a person’s bio-rhythm among others (2003). Framing effects are alternative formulations of the same situation, which make different aspects of it accessible and might alter the perception of outcomes and consequences, which might result in an altered decision by shifting normal preferences or time inconsistent preferences. Evaluation of outcomes and consequences is done, according to prospect theory, through individual gains and losses (Tversky & Kahneman, 1981). A correction or even an overcorrection15 may appear in the case of becoming aware of using a heuristic for one’s decision (Kahneman, 2003). One has to be conscious that these effects can be caused by the phrasing and design of the brochure. Additionally, all of the aforementioned effects might be elicited by using different phrasings of the buying decision question in the interview during the decision-making process.
These heuristics demonstrate well that cognitive systems involved in the decision-making process are dependent on perception. To include the latter in decision-making theory, Kahneman (2003) and Kahneman and Tversky (1979) proposed a new theory, building on Bernoulli’s (1954) theory, called prospect theory, which links decision-making to utility. The researchers indicate that decisions are evaluated by the decision-maker in terms of gains and losses by relating outcome to a reference point, meaning a decision’s utility is evaluated relative to this reference point, because perceived utility is individual and driven by emotion. When it comes to making a decision on whether to buy a product or not, consumers tend to search less when processing costs are lower and thus utility would increase (Moorman, 1996).
Aside from the aforementioned systems, according to Soederberg Miller et al. another cognitive process called conceptual integration plays a role when reading is involved in the decision-making process. This process describes the organization and integration of concepts in text with prior knowledge, and therefore is a key component of the acquisition of new information from text. It is affected by idea density of text and therefore reflects workload of the text. Reading times increase with increasing idea density. Furthermore, a mediating influence of conceptual integration on the relationship between prior knowledge, working memory capacity (WMC) and acquisition of new information is assumed. WMC in turn is assumed to be responsible for cognitive efficiency by means of inhibiting distracting information and thoughts, and through appropriate time allocation. It can be measured in reading time. Additionally, it closes the circle by fuelling attention to conceptual integration, which in turn promotes greater acquisition of information from text (2011). Further factors facilitating information acquisition, comprehension, and elaboration are: motivation, ability, and willingness to process and information about negative consequences. All of these factors may lead to improved decision quality (Moorman, 1990; Suri & Monroe, 2003). Willingness to process information is determined by the relation between attributes and consequences, for instance information on negatively perceived nutrients that lead to higher levels of attention (Moorman, 1990). But there is one factor that plays a controversial role: time pressure. Suri and Monroe found that the consumer’s ability to process information is not necessarily limited by time pressure, but an increase in time pressure is likely to result in a reduction in the extent of systematic information processing, because under high motivation to process information, systematic processing decreased with increasing time pressure (2003).
Since time pressure and confusion about text content both affect people’s acquisition of new, evaluative, and comparative information, this is the most crucial information that can be used in the decision-making process (Wendler, 1983). In connection to this, Alba (1983) indicates that people learn new information although they are confused about the content. The latter types of information should help participants to acquire some information from the informational brochure provided in this study’s experiment, even though they might not understand it all in detail.
The following illustration shows a general concept for making a buying decision by Kotler, Bliemel and Keller (2007) referred to later on. Only steps two through five are relevant for this study.
Abbildung in dieser Leseprobe nicht enthalten
Figure 1: Conceptual steps of buying decision (Kotler, Bliemel, & Keller, 2007)
The previous chapter describes processes and factors involved in general decision-making. It is clear that making a decision is a complex process that is affected and facilitated by many cognitive factors including WMC and prior knowledge, which have to be applied to decision-making in a grocery shopping context to be useful for this study. They are linked to this study through having a high likeliness of being central to health behaviors and health information processing by being related to accurate perceptions of food choices and food health (Soederberg Miller et al., 2011). Higher levels of education are proposed to lead to higher levels of nutrition knowledge, which in turn supports dietary choice (Soederberg Miller et al., 2011). Furthermore, high nutrition knowledge increases the purchase likelihood of products with high nutritional value and is related to higher levels of accuracy of product label understanding (Burton, Biswas, & Netemeyer, 1994). Nutrition value itself is indicated to have strong effects on nutrition beliefs, attitudes, and reported purchase likelihood (Burton et al., 1994). Therefore, both WMC and nutrition knowledge in relation with nutrition value are seen as crucial for understanding dietary choice and promotion of information processing (Soederberg Miller et al., 2011). Furthermore, a particular influence of diet-disease knowledge on comprehension of nutrition information has been found (Moorman, 1996). This is important, because comprehension is the activity found to be most susceptible to the influence of prior knowledge levels (Moorman, 1990). Therefore, a lack of adequate nutrition knowledge can lead to high comprehension costs (Russo, Staelin, Nolan, Russell, & Metcalf, 1986). These costs are assumed to be even higher in dyslexics, due to deficits in phonology, WMC and long-term memory.
Other factors including age play a role in dietary choice as well. Specifically, age-related decreases in WMC can cause difficulties in information processing (Soederberg Miller et al., 2011), correct recall of previously provided nutrition information (Jacoby, Chestnut, & Silberman, 1977) and performance accuracy (Cole & Gaeth, 1990). For example, participants over 50 years had greater difficulty to recall and use previously provided nutrition information correctly (Jacoby et al., 1977). Recall accuracy is dependent on activation of prior knowledge at the time of learning (Alba, 1983). However, not only age is assumed to be responsible for decreased accuracy on choice tasks, also the length of a shopping trip (Jacoby et al., 1977), changes in basic perceptual ability (Cole & Gaeth, 1990), and the reason for a lack of accuracy in describing nutrient’s meaning (Jacoby et al., 1977) might exert effects. Acquisition of nutrition information decreased by 50% on a 12-item shopping trip for example. During a long shopping trip, the acquisition rate is estimated to be only at 10% (Jacoby et al., 1977). According to Soederberg Miller et al. (2011), these difficulties can be avoided by increasing nutrition knowledge through higher activation of prior knowledge, since higher knowledge levels reduce age effects on memory and comprehension tasks.
Although it has to be mentioned that recall does not necessarily equal memory and memory does not necessarily equal comprehension, testing recall as a result of memory and comprehension is a first assessment of those factors (Alba, 1983). Furthermore, Jacoby et al. (1977) show that few consumers actually acquire information and have a proper understanding of what they have acquired, which makes decision-making, primarily based on newly acquired information, more difficult. This fact might lead to even more difficulties in terms of lower understanding levels in dyslexia. Thus, the costs of acquisition in terms of collecting, comprehending and computing nutrition product information is likely to be even higher for them.
According to a study conducted by Jacoby et al. (1977), the majority of consumers (55-57%) base their decision-making of a grocery purchase on the nutrition labels available. They are reported to be the source most often used (Burton et al., 1994). Therefore, if consumers understand product labels better they might increase their acquisition and comprehension of information. According to Moorman (1996), the Nutrition Labeling and Education Act (NLEA) implemented in 1990 has led to greater understanding. However, the changing of eating habits and preferences often outweighs lower costs of information processing (Russo et al., 1986), so that it might be easier for some people to use supplements to make up for bad habits than changing them. A multivitamin juice can function as such a supplement. In this context, framing effects might play a role because consumers view positive and negative nutrients equally important. They emphasize the negative more (Russo et al., 1986).16 Furthermore, in this context vitamins are an important factor in food buying decisions, they were mentioned by 76% in a survey (Russo et al., 1986). Following this, the thesis’ author assumes that dyslexics still have problems with understanding conventional product labels due to reasons of small font size, lack of making required connections between declarations, and getting stressed out by the demands of information processing in a supermarket surrounding.
This analysis demonstrates why cognitive processes can have a tremendous impact on purchase decisions made during a grocery shopping trip. The author argues that these impacts are even more increased for dyslexics. Nevertheless, economic factors including consumer scepticism towards product and nutrition information, product involvement, corporate identity, brand loyalty and familiarity have not been mentioned so far, but they should not be undermined in this context. Often consumer scepticism towards product information is strong and stems from stored knowledge and prejudices, which build the basis for implicit and mostly negative believes about marketer activities held by consumers (Moorman, 1996). In the case of brand loyalty, there might be a tendency towards avoidance of engaging in package search due to a familiarity bias (Jacoby et al., 1977). This bias is supported by Moorman (1990) who did not find significant improvements in decision-making due to familiarity. These findings illustrate the necessity for designing a trio of completely new product variations of the same multivitamin juice that does not exist in stores in an attempt to avoid any biases.
On the contrary, effects of a corporate Identity’s perceived understandability on confidence in purchase decisions has been found by Wendler (1983). This might influence the willingness to buy the study’s products in a supermarket, because the brand of the three products is unknown. Thus, it does not embody any brand image. Finally, an influence of product involvement on information comprehension, awareness and risk has been found (Wendler, 1983). The aforementioned factors, related to economics, are believed to have an impact on decision-making in a realistic grocery shopping setting. But with exception of consumer scepticism and confidence in the purchase decision, all other factors had to be excluded from this study to avoid a mixture of effects contributing to each other.
Throughout the previous chapters, mechanisms, effects and factors have been described that lead to the hypotheses listed in the following chapter. As previously described, dyslexics and people with serious reading and spelling difficulties sometimes lack the skills to examine reference materials for determining which food contains a particular ingredient (Viswanathan, Hastak, & Gau, 2009). Also, they often read slower, which causes problems in reading a given text in a certain time period due to phonological deficits in coding and decoding among others (Stothers & Klein, 2010). This in turn can lead to lower text comprehension and a lower amount of newly acquired information compared to a control group (Stothers & Klein, 2010). Furthermore, dyslexics might not be able to recall product names accurately due to deficits in WMC and the use of the logographic reading strategy. Hence, when asked to make a buying decision for one product an intuitive decision made by System 1, which can be affected by framing effects and the affect heuristic is likely (Kahneman, 2003; Tversky & Kahneman, 1981). Also, a preferred use of pictographic thinking is possible (Viswanathan et al., 2009). These effects might lead subjects to make a sub optimal or no decision at all. In the case of being affected by sub conscious effects including heuristics and framing effects, the word vitamin or parts of it could serve as an access cue for accidentally deciding for the right product due to a partly name overlap. This word is mentioned many times in the brochure, which provides participants with all necessary information needed for the purchase decision.
Furthermore, whether participants make an intuitive decision might become apparent in terms of not having a closer look at beverage containers and not considering all information given on these containers. Beverage container design cannot play a role in the decision between products, because it is held constant. In addition, being presented with completely new products can allow consumer scepticism to play a role in the decision-making process and lead to making an unmotivated or no decision. Moreover, emphasizing vitamins with possible negative consequences is meant to direct attention towards them, thus providing subjects with supporting information in their decision-making process.
Acquisition of information about products in order to make a sound decision has to be done in many areas of one’s life besides grocery shopping including finance, health care and insurances. Relating to this, a sub optimal buying decision can have harmful effects in many respects including worse health conditions or a loss of money.
The field and task of grocery shopping was selected for this study, because it is a task most people have to master at some point in their life, even though shopping frequency varies largely (Kim & Park, 1997). Therefore, it is applicable to a wide variety of people. All are presented with the following questions referring to decision-making on food purchases: where they want to buy their food from (Recker & Kostyniuk, 1978), how much time they want and can devote to grocery shopping (Hui, Bradlow, & Fader, 2009), how much money they want and can afford to spend on groceries, what food they want to buy and how healthy that food should be. By making a person think about these questions, it is a task that adds to a person’s daily workload besides demands introduced by his job and social environment. Thus, it can lead to stress (Jayakumar & Sulthan, 2013). This is a reason for the importance of efficient information processing in today’s world to hold workload as minimal as possible. It is a stressful activity for all people due to time pressure and environmental stimuli (Aylott & Mitchell, 1998). However, it seems to be obvious that this task can be particularly challenging for those who are already challenged by demands of other daily tasks such as people affected by dyslexia. Of course, not everyone is exposed to time constraints and time pressure when going grocery shopping, but this basic assumption seems to be applicable to a wide variety of people in the workforce.
The aforementioned theoretical background of research done on dyslexia in adults and decision-making led to the development of the following research question:
Hence, the goal of this study is to show that dyslexia and serious reading and spelling difficulties can have an effect on decision-making in daily grocery shopping because according to S. E. Shaywitz and Shaywitz (2005) dyslexics have problems to filter, comprehend and retain all crucial information given in a text on an unknown product adequately, which would be necessary for making an optimal and sound buying decision.
This model illustrates which effects might have indirect and direct influences on the two buying decisions participants had to make throughout the experiment, based on the aforementioned literature. For example, IQ-levels and prior knowledge are characteristics that had already been present in dyslexic participants prior to this study. Based on this account it is assumed that their presence in the groups exert direct influences on the processes that lead to the buying decisions, but exert only indirect influences on the buying decisions themselves.
Abbildung in dieser Leseprobe nicht enthalten
Figure 2: Conceptual model and relationships of hypotheses
Based on the aforementioned theoretical construct shown in Figure 2 the following hypotheses have been developed:
H0: There is no difference between dyslexics and controls.
H1: Dyslexics and people with serious reading and spelling difficulties will not make an optimal17 buying decision when presented with a new product that requires acquisition of specific product information by means of reading an extensive text compared to a control group, when presented with time constraints.
H1a: Higher levels of prior knowledge about vitamins and nutrients make an optimal decision more likely in dyslexics18.
H1b: Making an optimal buying decision gets more likely for dyslexics with higher global IQ-scores (>115).
H2: Product name recall is less accurate in dyslexics only after getting the chance of reading the informational brochure.
H2a: Dyslexics overestimate their informational processing skills.
H2b: Dyslexics have more difficulties differentiating between the three product names.
H3: Being presented with the actual product’s beverage container, having the possibility of comparing the available product variations, and having no time constraint allows for an equal likelihood for dyslexics to make an optimal buying decision compared to controls.
H4: Dyslexics perceive daily grocery shopping as more stressful.
H5: Dyslexics cannot filter crucial information out of the informational brochure as well as controls.
H6: Dyslexics read less frequently in general.
The German Association for Nutrition (DGE; 2013) has focussed on assessing which nutrients are required for appropriate nourishment through a balanced diet. Effects of nutrients including vitamins19, sugar and others have been widely discussed. Particularly, the way of intake is an important issue in this regard. Following this, the role supplements play has been emphasized and assessed in terms of whether it is necessary to take up nutrients by eating fresh fruit or whether supplements like smoothies, juices, and pills can have an equal effect on health (Bach & Lewis, 2012; Belz, 2008). Hence, the product of choice was a multivitamin juice in this study. These juices claim to be particularly healthy and delicious at the same time, due to one of its main ingredients, vitamins. Vitamins have drawn much attention by researchers recently, especially in discussions about components of a healthy lifestyle and healthy nutrition. Finally, fruit juices have gained importance illustrated by the fact that almost the same amount of juice compared to milk is consumed, at least in the US (Storey, Forshee, & Anderson, 2006). Following this, it is assumed that most people can relate somehow to multivitamin juices when asked for making a buying decision.
According to Village, the Food and Drug Administration (FDA) states that a fruit juice has to contain 100% fruit juice to acquire the right of getting labeled as such. Any beverage that contains less than 100% fruit juice has to display that it is reconstituted from concentrate and list the exact percentage of fruit juice contained. Furthermore, a descriptive term such as “beverage”, “drink” or “cocktail” must be included in the product description or shown on the container. In most juice drinks, flavours, sweeteners and fortifiers are added and ingredients must be listed on the label (2001).
There is a trend in today’s society towards living a healthy life style that one wants to emphasize, show, and openly admit to (Weeden & Sabini, 2005). Living and looking healthy is perceived as attractive among a certain group of people and gains more and more importance (Anderson, Adams, & Plaut, 2008). To achieve this, a well-balanced diet that incorporates necessary vitamins, antioxidants and many more healthy nutrients is assumed to be particularly healthy (Belz, 2008). For this reason, a high consumption of vegetables and fruits is important, as it is associated with a low risk of developing cancer and cardiovascular disease (Bub et al., 2003). Despite this, many people do not eat the recommended amount of 400 grams of vegetables and 200-250 grams of fruit a day (Deutsche Gesellschaft für Ernährung e.V., 2013). There are options for making up for this. Since drinks and beverages take up a central position20 in a person’s diet it can serve as a supplier of nutrients (Village, 2001). However, not all drinks people consume can be considered healthy or can serve as some supplement for a lack of vitamin uptake through fruit. In this regard, only the intake of 100% fruit juice is associated with positive effects (Village, 2001) because fruit drinks mostly contain only 10% real fruit juice and add unhealthy components including sweeteners and fortifiers among other things to the mix (Rampersaud, Bailey, & Kauwell, 2003).
Although a healthy diet plays a vital role in our lives today, many people did not want to change their eating habits in the past (Russo et al., 1986). Hence, taking a closer look at the supplementary effects of the intake of vitamins through 100% fruit juice seems to be a suitable way for building a connection with the study’s investigated product, as juice consumption may make up for a lack of intake of necessary nutrients through vegetables and fruits to some extent.
Following the aforementioned fruit juice definition given by the FDA, this study used three imaginary multivitamin juice products that contain 100% fruit juice. These products had to be newly designed, so that subjects could have never heard of them before, to avoid possible confounding influences by side effects including habituated decision-making (Verplanken & Aarts, 1999), biased opinions based on past experiences with package form, package color, product pictures and other product attributes.
According to Meffert, Burmann and Kirchgeorg (2012), a product consists of two main characteristics or benefits: Its fundamental benefit and its additional benefit. On the one hand, the three different multivitamin juices by the same brand “Vitaminolatibos” have the same foundational benefit in terms of being a drink that supplies one with fluid. On the other hand, their additional health benefits differed due to amounts of vitamin levels they each contained.
Different product names were meant to indicate this difference. All other possibly distracting information regarding ingredients other than vitamins and fruit juice percentage was removed from the beverage container, and thus held constant, so that subjects’ attention could be directed towards the difference between names and tables showing the vitamin levels. A normal multivitamin juice, named “Multivitaminsaft” by the brand “K Classic”, as sold by the supermarket Kaufland, built the core of the product used in this study. The used containers contained original drinkable juice and hadn’t been opened before. These containers got a new outside design.
Vitamin levels on the outside of the beverage container were geared towards recommended daily values (DV) and recommended dietary intake (RDA) by the German Association for Nutrition (2013), and are declared in RDA units because this is a variable comparable across nutrients and people (Russo et al., 1986). Vitamin levels were as follows:
Abbildung in dieser Leseprobe nicht enthalten
Table 2: Products
For the beverage container, a positive but atypical dark green colour was chosen, which was assumed to look appealing to the subjects. It built the background for all three different products. Furthermore, a picture of fruits was chosen that is not used by any other multivitamin juice product available on the German market, so that the aforementioned possible biases may get excluded. The table containing vitamin levels was adapted in size and content from those typically used on beverage containers. The described product design is illustrated in Figures 10 - 12 in the Appendix.
Since many different effects and deficits can play a role in dyslexia, subjects were asked and partly checked for the following exclusion criteria including mental retardation assessed by their IQ-score, lack of education opportunity (Warnke, 1999), and willingness to make a decision. Subjects have been recruited through newspapers, posters and personal contacts. The study was conducted in Bonn and Friedrichshafen (Germany) in the fall of 2013 with 46 adult participants living in these areas.
The hypotheses are evaluated empirically by administering a small battery of tests, conducting an experiment on a consumer’s buying decision on one of the aforementioned three different multivitamin juice products and the completion of a short survey. The author decided to use a between-subjects design, which classifies subjects and applies the same conditions to either some or all participants at one point in time, because “intuitive judgments and preferences are…best studied in between-participants designs and in short experiments that provide little information about the experimenter’s aims” (Kahneman, 2003, p. 712). This study uses an altered form of the classical between-subjects design because all participants have been presented with the same stimuli and got classified, in a second step, after they had already participated in the study. The tests were administered in the following order to simulate stress comparable to stress levels at the end of a workday. Participants were asked to take an intelligence test before taking part in a spelling test.
It was essential to this study to test subjects for their IQ-level. In accordance to the IQ-discrepancy criterion used in the literature for classifying a subject as a dyslexic participant, one had to take a standardized IQ-test first. Since dyslexia is a LD that shows many different phenotypes and degrees of severity, I chose to use the IBF-S test for this study due to its variety of task formats. For this study, it seemed to increase the likelihood of getting a more realistic overview and broad picture of the subjects’ cognitive abilities. This test consists of 7 tasks that use different types of task formulation and give every subject a fair opportunity to show his skills across all cognitive areas identified by Thurstone (1941), namely fluency/verbal comprehension, quantitative reasoning, memory, and space/spatial cognition. This range may lead to more realistic results because some dyslexics could have been disadvantaged by being exposed to an intelligence test, which only consists of visual and spatial tasks like the Figure Reasoning Test for instance (Daniels, 2006) or a language-specific IQ-test. In the latter format, better readers normally achieve higher scores due to bidirectional influences of reading and IQ (Meyer, 2000). Therefore, choosing an intelligence test that only uses one type of task might have distorted the results. Moreover, it is one of the most time efficient, but still valid, tests that can be conducted in groups (Ibrahimovic, Bulheller, & Horn, 2006). The latest version, updated in 2006, was used to avoid any kind of “Flynn–effect”, which indicates the necessity for using updated IQ and spelling tests, since people perform better on the same IQ tests than people did ten or twenty years ago (Flynn, 1987).
Subsequently, subjects were asked to take a standardized German spelling test for adults called “Rechtschreibungstest-Nichtraucher”. This test is appropriate for adults and widely used as a valid and reliable instrument in evaluating a person’s spelling abilities according to his schooling (Kersting & Althoff, 2004).
At the end of the testing session participants had to take a syllable test, meaning they had to repeat non-words that the test administrator read out loud. A non-word or pseudo-word test is an often-used test for identifying dyslexics because subjects must use phonological analysis to perform the task well. This method is called “Mottier-Test” and is part of the “Züricher Lesetest” by Linder and Grissemann (2000). It is a syllable test, which tests for WMC by requiring participants to keep as many syllables as possible in their memory. It was administered to see whether participants have the ability to decode the product names consisting of 7 syllables correctly, and to uncover possible symptoms of decreased WMC in dyslexics. It was decided against applying a non-word reading test because there is no universal manual on how to pronounce a non-word correctly (McDougall, Borowsky, MacKinnon, & Hymel, 2005).
After a five-minute break, during the second part of the study, participants were given five minutes to read an informational brochure on vitamins and the aforementioned three multivitamin juice products. This brochure offers information about particular benefits and risks of the vitamins and dosages that are included in the products as well as clear advice as to which product one should buy and which one is likely to have harmful effects on the consumer by providing particular reasons. To permit some help, they were given the opportunity to mark in the text, since asking people to circle relevant information was found to improve their decision-making accuracy (Cole & Gaeth, 1990). After reading the text, participants were asked to select one of the three multivitamin juice products. Since studies have shown that effects of dyslexia only become evident when affected persons are exposed to time constraints (Stothers & Klein, 2010), a five-minute constraint was chosen and confirmed to be appropriate by pre-tests. The time constraint was adapted from standardized reading speed of non-dyslexic persons, which ranges from 186 to 377 words per minute (Just & Carpenter, 1980). This is in line with a proposed average of 280 words per minute by Taylor (1965). Additional time was given because the brochure idea density is comparable to a scientific text that is more difficult to read and comprehend and represents a higher workload. The three product names are written in italic font and the brand name in bold letters to make detecting and decoding them more likely. These font types can function as access cues (Meij & Meij, 2013). In order to provide an even stronger access-cue, the word brand is also written in bold letters and the text was structured more visually due to pre-test feedback. Furthermore, the brochure contains only evidence-based facts of vitamins and their possible effects on the human body and on the composition of different types of multivitamin juices.
Participants had to choose one out of three offered self-designed and newly created multivitamin juices by the same brand named “Vitaminolatibos”. The three aforementioned product names are similar fantasy names: Vitaminolatipo, Viwamenolatipo and Vitamenoladipo. Attention has to be paid to the fact that these names consist of 7 syllables. Usual syllable memory capacity of non-words is 6, but the chosen names contain parts of or the complete word “Vitamin”, which can be perceived as slight access-cue (Meij & Meij, 2013). Every participant was asked individually to make a decision on buying one of the three offered products. It was assumed that there would be a preference for the healthiest juice, which benefits the buyer most. Assuming subjects want to maximize their health by minimizing the purchase and consumption of possibly harmful products. Subjects could only make a conscious sound and intentional decision if they identified and comprehended the two key paragraphs including crucial buying suggestions by reading the provided brochure precisely.
Following the time limit, subjects had to turn over the brochure and were not able to take in more information or finish reading accordingly. They were guided in a second room individually and were asked in two separate steps of “joint evaluation” (Kahneman, 2003), which multivitamin juice product out of the three described they would buy and what are the reasons for the buying-decision they just made. Immediately after the first buying decision was made, subjects were shown the three real products in the three self-designed packages. These packages could be taken in hand, looked at from every perspective and compared much like in a real-life situation. Subjects were not given any time constraint for making their decision, however, they were asked to apply the same decision-making mechanisms as they would usually use in a non-artificial purchasing situation in a local supermarket. Subsequently to the second buying decision, every participant was asked whether he/she wants to drink some multivitamin juice out of the package he/she just decided for.
Following these decisions, subjects were asked to complete a five-page survey (Figures 13-17 in the Appendix). This survey contained 39 questions, excluding demographics and the PANAS Mood Scale, which were partly adapted from the Handbook of Marketing Scales (2011)21, and partly tailored to this particular study in order to detect further indirect influences on participants’ buying decision in connection with dyslexia. Many Items had to be particularly tailored to this study due to novelty of this research question in the economic field. This survey includes openly, binary and unipolar phrased items. A unipolar phrased item design seemed to be best suited for most items, because they focus the respondent’s attention on the particular object or attitude evaluated (Bradburn, Sudman, & Wansink, 2004). Furthermore, five-point scales are used because according to Krosnick (1999) those produce the most reliable results particularly for unipolar scales. Verbal anchors served as indicators for subjects to assess every point on a scale (Bradburn et al., 2004). A variety of scales were chosen to make the question more understandable for dyslexic subjects, even though this might lead to problems when evaluating the collected data. The general survey structure, as illustrated in Table 4 in the Appendix, was adapted from Moosburger and Kelava (2012).
The specific structure and the phrasing of items have been developed according to feedback of three pre-tests, which had been conducted before the study had commenced. The number of Items was reduced from roughly 60 to 39, excluding demographic questions and the PANAS Mood Scale, due to the additional collection of qualitative data during the interview situation of each buying decision. The latter data was assumed to be able to make up for this decrease in items.
At the end of the 2.5-hour testing procedure, according to the “no deception norm” (Rampl & Wobker, 2012, p. 47), all participants were told about idea and purpose of the study and the individual test methods. As well, detailed questions were answered after the respective session had finished.
For this study, a number of 46 people were recruited for participation. This is an average sample size comparable to other studies on dyslexia. The selection of the sample was assumed to have possible effects on the outcome. Therefore, the goal was to recruit adult dyslexics who cover a wide range of degrees of severity and global IQ-levels to increase the likelihood of getting results that could be generalized, although generalizing is very difficult in dyslexia research. Since this study researched the effects of dyslexia in adults, participants had to be at least 18 years of age.
All 46 subjects are German native speakers. However, 3 had to be excluded from further analysis due to a global IQ-score below 85 points and 1 due to a conscious refusal of making a buying decision. No participant quit the study during participation. The sample size did not have to be reduced further due to severe missing values in the experiment’s questions for a buying decision or survey. This procedure resulted in 42 participants (women: 24; men: 18) across all conditions. 24 participants were classified as being dyslexic or weak in spelling and thus assumedly in reading as well (women: 8; men: 16). 21 of those participants were clearly identified as dyslexics according to the IQ-discrepancy criterion (women: 5; men 16)22. Respectively, 18 participants were identified as controls (women: 2; men: 16). The mean age of the sample was 34.19 (SD = 14.84) ranging between 19 and 68 years. On average, participants achieved higher intellectual global IQ-scores compared to the general population ranging from 91 to 150 points (M = 113.36; SD = 11.659). This is an indicator of the high educational level of the participants. 78.6% have been awarded a high school degree, 42.8% are students and 26.2% have been awarded an academic degree. Hence, 69% of the sample’s participants are prospective academics and academics, which is an extremely high number for a study done on learning disabilities and particular for one that comprises a bigger group of learning disabled than control subjects.
Furthermore, this sample can be seen as a healthy sample, due to a Body Mass Index (BMI)23 of 22.7, which is within the normal range category for normal health in adults ranging from 18.5 to 24.99 (Report of a WHO Consultation, 2000). Therefore, some prior knowledge about nutrition and vitamins as well as a healthy lifestyle can be assumed as this gets well illustrated by a ratio of 81% who have already had informed themselves about effects of vitamins before participating in this study.
The first confounding factor was controlled for by excluding participants with global IQ-levels below 85 points. Subsequently, it had to be checked whether it is possible and likely for participants to build a relation to the three novel products or not. Therefore, it was evaluated whether participants occasionally buy multivitamin juice in a supermarket, 50% do so. On the basis of this, a possible relationship between consumers and the designed product can be assumed. 73,8% would not buy the study’s juice in a supermarket though. For both variables, no group effect was significant.24 Additionally, a correlation analysis between an occasional buying of multivitamin juice and the willingness to buy the study’s product showed no significance (r = .129; p > .05)25. The reason most often reported in the interviews for a negative buying intention towards the study’s products was a lack of confidence in the products due to confusion after reading the text. This was partly caused by limited time for finishing the reading of and comprehending the informational brochure.
Furthermore, the IQ- and spelling tests and experiment, conducted prior to filling in the survey, resulted in a momentary positive affect slightly below average (25.6) and a momentary negative affect slightly above average (16.5), as assessed by the PANAS Mood Scale26. Hence, momentary negative affect was slightly superior to positive affect meaning participants were slightly stressed by the study. There was no significant difference found neither on the positive affect scale between dyslexics (M = 24.88; SD = 6.54) and controls (M = 26.61; SD = 5.29) nor on the negative affect scale between dyslexics (M = 17.21; SD = 5.07) and controls (M = 15.61; SD = 3.65). Neither for the positive (t (40) = -0.921, p > .05) nor the negative (t (40) = 1.134, p > . 05) affect scale. But a tendency that dyslexics scored slightly lower on the positive affect scale and slightly higher on the negative affect scale, respectively, can be reported.
Moreover, the importance of single decision criteria used for making the buying decision was evaluated by comparing the mean scores for every single criterion. This comparison of mean scores resulted in the following order starting with the information perceived as most important27: ingredients (M = 1.71; SD = 1.33), brochure (M = 2.36; SD = 1.48), picture (M = 2.95; SD = 1.55), product name (M = 3.38; SD = 1.68), and beverage container form (M = 3.62; SD = 1.68). A cross table showed no significant difference between groups (X2 = 4.038, p > .05).
Additionally, the syllable test resulted in 22 subjects who are in the norm or showed just a slightly reduced recall capacity of non-words and 13 participants with a strongly reduced syllable recall capacity.
A confirmatory factor analysis was performed to reduce dimensionality and cluster variables but did not lead to many reliable factors that explained a lot of the variance. Thus, no useful results could be obtained. This was caused by factors mentioned in the limitations. Since this analysis was not successful in explaining connections and influences on the main dependent variable “first buying decision”, a binary logistic regression analysis28 was performed to get a model of the predictors for the aforementioned binary dependent variable to test for hypothesis H1 and its sub-hypotheses H1a and H1b. Additionally, independent t-tests29 were performed to test hypotheses H2 through H5.
Hypothesis 1 expects dyslexics to make a sub-optimal buying decision more often than controls when they are presented with a completely new product, which they have to acquire information about under time constraints by means of reading. H1 was tested in two ways. First, by using a 2x2 matrix indicating a significant effect of dyslexia on making a sub-optimal buying decision (X2 = 7.292, p < .007). In the dyslexia group, 75% made a sub-optimal decision compared to 33.3% in the control group. Second, this significant relationship could be further evaluated by performing a binary logistic regression analysis, whose parameter estimates, standard errors, p-values, and odds ratio are reported in Table 3. The binary logistic regression model analysis was able to explain 33.3% of the variance in grocery shopping decision-making behaviour observed in the experiment, as assessed by the Nagelkerke R 2 and 24.8% as assessed by Cox and Snell R 2, respectively, (X2 = 11.952, p < .05). 71.4% of all cases, where influences of dyslexia or severe reading and spelling difficulties on the accuracy of the decision were predicted, were classified correctly. In line with assumptions, being classified as dyslexic according to the IQ-discrepancy criterion made making a sub-optimal, and thus false buying decision way more likely (B: 2.253; SE = .871; p < .05; Exp(B): 9.516). Conclusively, H1 can be accepted.
Abbildung in dieser Leseprobe nicht enthalten
Table 3: Results binary logistic regression H1
In H1a a positive supporting influence of higher levels of prior knowledge of vitamins and nutrition that increase the likeliness for dyslexics to make an optimal decision was predicted. This hypothesis has to be rejected due to no significant effect of independent variables measuring prior knowledge on the correct prediction of making an optimal buying decision in the dyslexic group. Significance values for separate items of this factor are illustrated in Table 3. Additionally, a second model only testing the predictive ability of the factor mean of the three items for prior knowledge did not lead to significant results either (X2 = 1.361, p > .05, R2 = 8.2%).
H1b expects dyslexics with IQ-scores higher than one standard deviation above the mean (> 115) to be more likely to make an optimal buying decision due to coping by means of increased cognitive abilities. For the analysis of H1b, the sample was split. This hypothesis has to be rejected due to no significant effect of the independent variable IQ30 on the correct prediction of making an optimal buying decision in the dyslexic group; therefore, this independent variable is not a significant predictor of decision-making in this group (X2 = .965, p > .05, R2 = 5.8%). A second analysis using an IQ-score greater than 113 points as cut-off value led to a model “trending towards significance” for the dyslexia group (X2 = 2.847; p < .1; R2 = 16.6%).
H2 hypothesizes that product name recall is less accurate in dyslexics after reading the brochure but before actually seeing the products. This hypothesis was evaluated by using an independent t-test comparing mean values of the variable “name recall”31 according to group classification. On average, dyslexics were not able to recall the product names (M = 4.42; SD = 1.47) as well as controls (M = 2.94; SD = 1.86). This difference was significant t (40) = 2.863, p < .01 and represented a medium-sized effect r = .4132. Hence, hypothesis H2 can be accepted. Furthermore, since classification of the first buying decision was done according to the ability of name recall, a positive correlation between these two variables was expected and found (r = 1; p < .001). Additionally, name recall of the product names, which participants just had decided for a minute earlier was checked in the survey for a second time qualitatively. An open question resulted in many different answers including no name recall (31%), recall of the first part of the word (that is, “Vitamin”; 29%), correct recall of the product name (21%) and recall of other parts of the name or wrong name (19%). Most notably the number of participants who could not recall the products’ name at all underlines the aforementioned results.
Hypothesis 2 is assumedly influenced by an overestimation of dyslexics of their own information processing skills. Therefore, H2a expects dyslexics’ subjective perception of their own skills to be good, even though they did not make the optimal buying decision as often as controls. An independent t-test showed that on average dyslexics (M = 3.389; SD = .83) as well as controls (M = 3.167; SD = .89) assessed their information processing skills relatively similar. Although, another independent t-test comparing mean values for the first buying decision showed significant differences t (40) = -2.224, p < .05 of a medium-sized effect r = .33 between dyslexics (M = 2.75; SD = 1.90) and controls (M = 4.17; SD = 2.229)33. Since the latter result is significant, a slight, yet not significant difference in self-assessed information processing skills would be expected (t (40) = .836, p > .05). This contradiction in significant findings suggests that effects of overestimation of their own abilities are prevalent in dyslexics, which led to an acceptance of this hypothesis.
In H2b it is predicted that dyslexics have more difficulties with differentiating between the three product names due to logographic reading. An independent t-test showed a significant difference (t (26.346) = 2.631, p < .05) between the two groups for the variable “product name differentiation”. On average, dyslexics assessed their ability to differentiate between product names as being less existent (M = 4.25; SD = .94) than controls did (M = 3.17; SD = 1.54). This difference represented a large-sized effect r = 0.534. Hence, hypothesis H2b can be accepted. However, the self-assessed ability to differentiate between product names is not a significant predictor of making an optimal first buying decision in dyslexics (X2 = .067, p > .05, R2 = .4%).
Hypothesis 3 predicts an increased likelihood of making an optimal buying decision among dyslexics after being presented with the actual product packages, and able to compare them under no time constraint. Using a binary logistic regression approach with the second buying decision as dependent variable and group classification as independent predictor of the second decision showed no significant results (X2 = .017, p > .05, R2 = 0.2%). Thus, this hypothesis has to be accepted because group classification did not predict the likelihood of making an optimal buying decision. No group differences were found compared to H1 anymore. Additionally, an independent t-test supports the result obtained from the binary logistic regression analysis. The significant difference between groups regarding the first buying decision (t (40) = -2.899, p < .01); that had a medium-sized effect (r = .42), was not confirmed when comparing groups regarding the second buying decision. Dyslexics’ decision (M = 1.67; SD = .48) did not differ significantly from controls’ anymore (M = 1.83; SD = .38; t (39.821)35 = -1.248, p > .05).
H4 hypothesizes that dyslexics perceive daily grocery shopping as more stressful. A comparison of the factor mean for the independent variable “stress” by means of an independent t-test showed small differences in perception of stress levels that the general grocery shopping experience exerts on dyslexics (M = 2.972; SD = 1.05) compared to controls (M = 2.704; SD = .79), but these were not significant in this experiment (t (40) = .906, p > .05). Since, a significant difference in perceived stress did not exist, no further regression analysis had to be performed, as it can be assumed that this independent variable would not have a significant predictive ability on the first buying decision. Nevertheless, this analysis was performed and results underline the latter argument (X2 = .481, p > .05, R2 = 2.9%). Hence, hypothesis H4 has to be rejected.
In Hypothesis 5 it is predicted that dyslexics have problems with filtering crucial information out of text due to many different reasons including being or feeling overwhelmed by the amount of information they have to process in a certain period of time. Using an independent t-test for comparing answers for the independent variable “necessary information included” showed no significant results (t (40) = 1.353, p > .05) between dyslexics (M = 3.08; SD = 1.18) and controls (M = 2.61; SD = 1.04). Furthermore, an independent t-test, using the factor means of items comprising the independent variable “information processing”, did not show significant results either (t (40) = .836, p > .05). Results for dyslexics (M = 3.39; SD = .83) were relatively similar to those for controls (M = 3.17; SD = .89). Similar to H4 a significant difference between groups would be expected to be able to infer the buying decision from the self-assessed inclusion of all necessary information. However, since dyslexics made a sub-optimal first buying decision more often than controls it can be reasoned that their self-assessment must have been biased, and they were not able to include all necessary information in the decision-making process. Therefore, H5 has to be rejected.
H6 expects dyslexics in general to read less frequently than controls. Following this, it can be assumed that they are less practiced in reading, which might have led to more problems with reading and comprehending the brochure administered. Once again, using an independent t-test for evaluating the general reading ratio led to the result that there was not any significant difference (t (40) = .304, p > .05) between the general reading ratio of dyslexic participants (M = 2.29; SD = 1.01) and controls (M = 2.19; SD = 1.05). Hence, due to a lack of a significant difference between the two groups, the hypothesized effect cannot affect the first buying decision. Therefore, H6 has to be rejected for this specific sample.
Abbildung in dieser Leseprobe nicht enthalten
Figure 3 : Conceptual model and relationship of hypotheses – results 36
As illustrated by the conceptual model, linking dyslexia to economic decision-making theory is a complex task that features many direct and indirect effects, which can exert one-directional and complementary influences on each other. Since, there has not been any research done linking these two fields, designing a study controlling for specific effects was difficult because there was no existent literature that this experiment could have been built on. Furthermore, it was also difficult to keep the buying decision relatively realistic. Therefore, the positive results obtained are considered a success.
The researched sample in this study was unique in many respects. First, since it was possible to recruit participants partly at a university and other institutions that are associated with highly educated people, a higher average IQ-score of 113.36 points, which is almost one standard deviation above the mean, was found. This is not surprising on the one hand but an uncommonly high average on the other (Meyer, 2000). Second, this sample, comprising 57% dyslexics of whom 75% have been awarded a high school degree, illustrates this special selection of subjects because most dyslexics can have difficulties to get a high school degree (Warnke, 1999). Additionally, 69% of the participants in this study were pursuing an academic degree or had already been awarded one. In the literature, dyslexics who pursue academic careers successfully are seen as an exception to the norm (Warnke, 1999). Third, recruiting a sample that is almost 50:50 distributed regarding its group classification has been a challenge, since the author had not known beforehand which participant would be assigned to which group according to the individual test results of the IQ and spelling tests. Though, the assignment of subjects into the two groups could be assumed according to pre-study self-assessments. Fourth, no differences in reading frequency were found between dyslexics and controls, which can also be explained by the sample’s high educational level in the way that people with a higher educational level, especially students and academics, are more exposed to reading and cannot avoid this as much as dyslexics in other occupational careers. Thus, they are reading frequently more or less voluntarily. However, this does not allow making statements about the accuracy of their reading comprehension. Additionally, less enforced reading practice in people pursuing occupational careers that require less reading makes them less likely to develop successful coping mechanisms in this area, which could cover up their difficulties.
Therefore, level of education is a factor of particular interest. It indicates a huge complementary effect between coping mechanisms and academic success. In line with theory, this study’s aforementioned descriptive statistics indicate that being affected with dyslexia does not have to be detrimental to one’s educational success. Depending on the degree of severity many successful careers are possible, even an academic one. Especially in the field of academics, it is assumed that many dyslexics are unidentified and do not know about their learning disabilities because of their successful coping mechanisms. Furthermore, the latter fact is a reason for persisting problems with defining this specific LD and differentiating core deficits from other comorbid disabilities. Therefore, the list of observable symptoms is extensive. This study’s understanding of dyslexia is mainly based on effects that can range from reading slower and less accurate to not being able to comprehend text at all or guessing words, and thus spelling most words incorrectly.
These problems can have serious negative effects on many areas of a person’s life including one’s self-esteem, educational success, occupational success, and social relations. To antagonize these possible effects, getting diagnosed with dyslexia should not be perceived as an end to one’s career ambitions, but rather as motivation to develop successful coping mechanisms through holistic therapy. This sample illustrates well that having learning disabilities does not automatically implicate less possibilities of getting access to higher education.
Furthermore, how one’s surrounding and society reacts to a diagnosis is crucial. Often the unfortunately persistent social stigmatization can have even more detrimental effects, especially on one’s self-esteem, than the diagnosis itself. This was well-illustrated by the unfortunate event of one participant who almost collapsed psychologically when she was presented with the spelling test due to old childhood trauma that was brought back to her consciousness by this situation. Such trauma can have long lasting consequences, whose occurrences show the importance of putting more emphasis on removing social stigma because any common stigmatization of dyslexics as stupid or even retarded is definitely out of place, as many prominent, aforementioned examples from the fields of business and science illustrate (Bund & Rohwetter, 2013; Copalla, 2007). The picture displayed by the literature in combination with informal interviews, conducted after the test sessions, made the importance of how a dyslexia diagnosis is handled by one’s surrounding apparent. It has to be understood that not the experienced symptoms themselves have the most detrimental effects on dyslexics, but rather that getting labelled as learning-disabled and treated as being stupid imposes even more psychological stress on dyslexics; either by negative reactions upon admitting to being affected by this disability due to prejudices and ignorance or by simple failure in school. As a consequence, the question whether perceived self-image or outside perception of oneself has stronger detrimental effects can only be answered when considering and evaluating both aspects as complementary.
Since gender is relatively equally distributed across the sample (women: 24; men: 18), a gender effect for dyslexics emerged when controlling only for dyslexics (women: 2; men: 16), which suggests some genetic influence, as reported by Snowling (1998). The control group’s gender distribution worked as an additional control mechanism because it was comprised almost only of women (women: 16; men: 2). Although any genetic analysis in this study could not be done, the aforementioned gender distribution can be seen as an indicator that genetics play a noteworthy role in this specific LD predominantly found in men. Similar to genetic analysis any functional magnetic resonance imaging (fMRI) could not be done, which could have been used as method to test this sample for fewer neuronal connections between the inferior frontal gyrus (Broca’s area) and posterior brain areas including the parieto-temporal and the occipito-temporal area as found by S. E. Shaywitz et al. (2006). Therefore, one has to avail other indicators such as gender distribution that may allow for drawing conclusions from them on general genetic and neurological characteristics of the sample at hand. Many participants reported relatives or close family members having difficulties with reading and spelling too, so that the existence of a heritability pattern can be inferred from this – even without having any particular genetic testing done. This was the main reason for recruiting most participants by advertising this study to parents of children who engage in therapy at an institution for learning disabilities in Bonn (Germany).
Directly attached to general genetic heritability is the issue of whether one’s cognitive ability, measured by global IQ, allows for drawing conclusions about one’s cognitive skill set appropriately, since this is crucial for evaluating the IQ-discrepancy criterion used in this study for classifying participants. This criterion assumes that there is a mutual dependence between IQ and spelling abilities in terms of a person’s spelling abilities are expected on the basis of his IQ-score. An expected spelling score t-value is derived from the achieved IQ-score. If a person shows a discrepancy between his actual spelling score’s t-value and his expected spelling score t-value of either 1 or up to 2.5 standard deviations below the expected value, this person gets diagnosed with dyslexia. Application of this criterion led to explainable results, although some participants had to be removed from the sample37. However, no correlation between dyslexia and certain IQ-scores was found, so that no mutual dependence should be assumed. IQ-scores of dyslexics ranged from 98 to 135 points, thus cover a variety of global IQ-levels, as illustrated by a non-existent correlation between higher global IQ-scores and higher spelling-scores. To be able to make detailed statements about the specificity of the connection between IQ and dyslexia, cognitive abilities of every dyslexic participant would have to be analyzed individually and in more detail. Although, such a detailed analysis could not be done, a non-existent correlation between global IQ- and the spelling test scores can be seen as an indicator for a weak connection between proposed phonological core deficits of dyslexia and global IQ-levels. Therefore, it is not surprising that hypothesis H1b has to be rejected due to a non-significant effect of higher IQ-scores in dyslexics on making the optimal buying decision more likely.
The latter paragraph illustrates why there are a number of problems with this criterion. First, since dyslexia is labelled as a specific learning disability, implicating that it only affects specific areas – namely phonologic areas of cognitive functions – expecting spelling scores on the basis of global IQ-scores is contradictory. Therefore, relating one’s language and reading abilities to one’s spelling scores would lead to more sound and precise results for classification purposes. Second, there are many different equally and officially widely recognized IQ tests to determine someone’s global IQ-level. These tests are either language-based or completely language-free or mainly auditory based. However, they are proposed to yield the same sound assessment of one’s cognitive abilities. If for instance a dyslexic person whose deficits are mainly apparent in the area of reading would have to take a language-based test with a high reading share, this could lead to a high possibility that his achieved global IQ-score would not display his actual intelligence level appropriately (Siegel, 1989). Also, an inappropriate assessment of a person’s intelligence level can be due to the influence of comorbid problems including AD(H)D or an impact of deficits in lower-level functions that make an appropriate and fully focused execution of tasks difficult, but does not necessarily have to do with one’s intelligence level. It can be perceived as blocking access to higher-order processes (S. E. Shaywitz & Shaywitz, 2005).
Problems of comorbidity and interaction effects of other disabilities lead to one more issue worth discussing, the issue of undetected dyslexics who are assumed to be generally learning disabled due to an IQ-score below 8538 points. This leads to a small discrepancy between the achieved spelling- and IQ-score, which is too small (< 1 SE) to give a sole dyslexia diagnosis according to the criterion, even though underlying phonological deficits are often present. This is mostly a problem in school children but can also have consequences that last into adulthood.
Since low global IQ-scores have made the detection of dyslexia less likely in the past, official therapy has oftentimes been refused to those people, if they had not already been sent to special schools. Furthermore, the opposite case in terms of identifying a person as having reading difficulties but not being able to give a dyslexia diagnosis due to a non-fulfilment of the criterion makes the development of problems all the more likely (Snowling, 1998), and thus increases unnecessary failure in school (Warnke, 1999). Often only these secondary problems get treated but not the cause itself. In this study, application of a different testing method or a cross-battery assessment for diagnosing and subsequently classifying participants might have led to a more detailed diagnosis and identification of subjects (Meyer, 2000; Siegel, 1989).
Conclusively, it is necessary to supplement the use of the IQ-discrepancy criterion for diagnosing dyslexia with further detailed testing, which tests for low-level functions and the specific phonologic ability to make giving a detailed and sound diagnosis more likely.39
Although only 50% of the participants buy multivitamin juice occasionally, 81% had already informed themselves about vitamins sometime prior to the study. Therefore, comprehending the brochure partly and being able to relate the study’s artificial buying decision to a real buying situation of buying multivitamin juice is likely. Since 50% do not buy multivitamin juice occasionally a clearly larger number of 73.8% who would not buy any of the multivitamin juices offered in this study indicates that participants were able to develop some kind of relation with the products, although this relation does not seem to be positive.40 However, many subjects reported uncertainty about the product’s ingredients and the effects coming with it. This uncertainty is partly caused by the removal of all other nutrition information on the beverage containers, except for vitamins and fruit juice percentages, which were reported as crucial indicators for making a confident purchase decision by many people. This information had to be removed to exclude confounding effects by those factors. Additionally, this information reduction was used to control whether participants were actually able to comprehend and retain the crucial bits of information on vitamins they had been reading about in the brochure. Another reason for the weak willingness to buy the study’s products might be demonstrated by the fact that the products and their brand were unknown and thus do not have a corporate identity that can serve as a point of reference, which makes consumers feel more confident in their purchase decision (Wendler, 1983).
The impression of uncertainty and confusion might have been provoked by some framing effect, as described by Tversky and Kahneman (1981), because according to Russo et al. (1986) information about negative nutrients raise more attention in people. The brochure contains plenty of information on negative effects of overdoses of certain vitamins. This might have sparked a feeling of caution against vitamins in general due to an inability to recall detailed text passages and arguments. Furthermore, the high percentage of potential non-buyers (73,8%) points to influences and the possible use of a general avoidance strategy that was predominantly reported by dyslexics who rather take more time to acquire enough information, which gives them the feeling of being able to make a sound and optimal decision. Additionally, this fact suggests that the expected need satisfaction by these products is relatively low.
The study was set up to create a realistic environment by restricting time for the acquisition of information and by creating stress levels comparable to those throughout a workday to show possible effects of dyslexia on the process of information acquisition and comprehension. It was attempted to evoke stress by administering the intelligence and spelling tests before reading the informational brochure under time constraints. First, the administration of both tests was necessary for group classification, and second, an intelligence test is perceived as very meaningful in our society. Hence, it should be able to evoke stress to some extend (Harvard Business Review, 2012). According to the results, this structure of tasks has successfully evoked some stress as represented by a slightly negative affect across all participants. This might have reduced the supporting function that coping mechanisms can have on correct spelling, name-recall accuracy, and the filtering of crucial information from text; comparable to a level at the end of a work day. Without putting this kind of pressure on subjects, these mechanisms would have been likely to cover most deficits up, which might have led to unrealistic results. A rejection of hypothesis H1 would not have automatically implied that there would have been no deficits present in dyslexic participants though.
Moreover, since the sample was expected to be highly educated, the chosen product names had to consist of 7 syllables to make an interference of coping mechanisms less likely, since the commonly proposed norm count for syllables of non-words that one should be able to memorize and retain short-term is 6 syllables (Linder & Grissemann, 2000). But since the chosen product names are not complete non-words, they offer access cues in terms of the word “Vitamin” that seemed to make names consisting of 7 syllables suitable. The significant difference between groups for product name recall is a sign for a high likelihood that the choice of product names consisting of fewer syllables or containing more access cues would have made differentiation between those too easy to show a significant effect between groups. Additionally, product names are often fantasy names such as Schovit, Arse or Tinkle.
Following the aforementioned facts, the verification of hypothesis H1 clearly shows effects of dyslexia that led to making a sub-optimal buying decision41, which may even have harmful consequences for the consumer as juice consumption of the two overdosed juices42 in this study hypothetically could have had. Being categorized as dyslexic according to the IQ-discrepancy criterion made a sub-optimal decision 9 times more likely in this study’s sample. This number shows a tremendous effect that is a reason for concern because it implicates that dyslexics are far more susceptible to making sub-optimal buying decisions – when reading is an essential way to acquire crucial information about a product.
One explanation for this high number is that participants had to make a decision for one of the three products. This specific task led to many decisions that were made based on intuition, as the experimental setting did not directly43 allow subjects to use the aforementioned avoidance strategy. Therefore, mostly dyslexics seemed to guess when they had to decide for one of the three products due to a lack of the ability to identify and memorize the necessary new product information properly, and thus make it available to them for short-term usage. However, out of the group of dyslexics, only 25% made the optimal decision, which can be partly explained by the fact of higher IQ-scores achieved by the majority44 of them, and other coping strategies like memory. Nevertheless, tests for the predictive ability of higher IQ-scores (H1b) on making an optimal buying decision did not show significant results until the cut-off value was decreased from 115 to 113 IQ-points. This is due to a sample size where one subject more or less assigned to a group makes a statistical difference. Thus, it can be argued in favour of lowering the cut-off value down to 113 points by the fact that IQ-points are not rigid and they are susceptible to many influences, so that this slight difference should not make a large difference for the interpretation of one’s global cognitive ability (Siegel, 1989). Furthermore, defining higher levels of prior knowledge is relatively difficult; therefore, it is not surprising that this factor was not a significant predictor of the first buying decision (H1a), even though Burton et al. (1994) provide evidence for this assumption.
According to Soederberg Miller et al., prior knowledge levels are supposed to be influenced by education. This in turn is supposed to influence reading, because theoretically reading and education complement each other, and in turn this relationship is assumed to be influenced by dyslexia as well (2011). Therefore, the assumption that dyslexic participants read less than controls, and thus have less reading experience than controls (H6) would have been legitimate for a lower educated sample. Therefore, the sample’s high educational level is one plausible explanation for the similar levels of reading frequency and no predictive ability of reading frequency on the first buying decision, which led to a rejection of this hypothesis. Although reading frequency was not different between groups, problems with decoding, reading comprehension, and WMC got apparent by less accurate text and name recall. The latter abilities were assumed to play a crucial role throughout the second and third step of the buying process by Kotler et al. (2007), namely searching for product information and evaluating alternatives by reading the brochure to acquire crucial information about the until then unknown products in order to make a sound first buying decision as well as possible.
Good information processing skills are needed to make this decision as sound as possible due to the information density of the brochure. But from the confirmation of H2a, saying that dyslexics overestimate their information processing skills, it can be inferred that dyslexics do not perceive and rate their information processing skills as compromised as they often are. Since they made an optimal decision less often than controls, it can be argued that their actual information processing skills and WMC, as hypothesized in H2, are not as good and accurate as those of controls. This lack of being able to filter crucial information from text (H5) in a given time period can have many causes that cannot all be discussed here in detail. Two important reasons are logographic reading and level of schooling.
First, according to Perfetti et al. (1979), logographic reading is very likely to be prevalent in less skilled readers, which leads to imprecise identification of single words, and thus to a lower probability of detecting small but crucial differences in names for example. These difficulties in the decoding process got significant and obvious by the results of H2. Printing product names in italics was an attempt to give participants an access cue about what they should focus on, but seemingly did not provide enough support to detect all names or differences in names correctly. From this it can be inferred that these small changes in format did not create enough cognitive arousal to be recognized by dyslexics (Kahneman, 2003). This difference between groups is conforming to dyslexics’ self-assessment. This group perceived the differentiation between product names as more difficult and challenging (H2a).
Second, even though the educational level of the complete sample was high, it became obvious that participants who only achieved a “realschul” 45 degree, have all had serious problems to acquire the crucial information from the brochure necessary to make an optimal decision. This is likely to be attributable to the high information density of the text, which is comparable to a scientific text. It is likely that these participants have not been frequently exposed to such a high demand of conceptual integration throughout their previous lives. On the one hand, it can be assumed that text structure and fact density overwhelmed them because of fewer practice and concepts of how to deal with them at hand, so that no subject with this degree was able to make the optimal decision in either group. However, on the other hand, if the brochure had been designed using an easier text structure, it can be assumed that dyslexics using successful compensation strategies would have been able to make the optimal decision; thus, effects may not have turned out significant. There are real world examples for brochures with a comparably high information density often related to health topics. Conclusively, it can be argued that the assumptions made when designing the brochure led to the right degree of difficulty, so that controls had almost no problems of acquiring and retaining the crucial information contrary to highly educated and successfully compensated dyslexics.
Many factors, mainly prevalent in the dyslexia group, are indicative of a significant difference between groups concerning the first buying decision. However, this difference was not observable during the second buying decision anymore. Having different possibilities enabled dyslexics to make the optimal buying decision almost with the same likelihood than controls.46 This result was expected due to the experimental set-up though. There are many reasons for explaining this difference. First, the second buying decision was made under artificial and simplified circumstances in terms of no present stimulus overflow by other surrounding products, customers, tall and badly structured isles among others, so that only one set of stimuli in terms of the three available products were present. Second, information on each beverage container was enormously reduced and not comparable to the amount of information present on products normally found in supermarkets. Therefore, decoding and comparing the products’ fantasy names was presumably easier in the second buying decision than when reading the brochure. Stimulus density of products available in a supermarket is more comparable to the extent of the brochure. Additionally, the special beverage container design forced the subjects’ attention on the table of vitamins, which is supported by results that report the table of ingredients as the main decision criterion. This finding is in line with theory by Jacoby et al. (1977). Third, participants did not have any time constraint for making their decision, which is known as the crucial element in dyslexics for achieving similar text comprehension levels (Stothers & Klein, 2010). Thus, the verification of H3 can be seen as reassurance that time constraints, stress levels, visual and haptic cues play a crucial role as supporting elements in a dyslexic’s decision-making process (Viswanathan et al., 2009). Such cues represent a form of low product involvement that has been created, which has been found to be crucial for information comprehension (Wendler, 1983). Having more time results in fewer stimuli that have to be processed and filtered simultaneously, which should provide a general framework for a successful shopping trip. Therefore, the conclusion can be drawn that rising stress levels, limited time by whatever reason, low product involvement, stimulus overflow or a product that requires acquisition of information through difficult text can lead to the aforementioned problems.
This was the reason for assuming that general grocery shopping stress-levels are perceived significantly different between members of the two groups (H4). This hypothesis had to be rejected because both groups tend to plan their grocery shopping trips well beforehand. However, participants based this result upon self-assessments. Therefore, it has to be interpreted with caution, as hypothesis 2a has already shown that dyslexics’ subjective impression of their own abilities is not always accurate, assumedly due to the perception of their own stress-levels as normal.
Finally, a difference in momentary stress levels at the end of a study session was assumed as well, but did not turn out to be significant. Nevertheless, a slight tendency towards more negative and less positive affect in dyslexics was noticeable though. A significant difference was expected because participants had to engage in a lot of reading throughout the study. That these demands did not lead to a significant difference in stress levels is likely to be attributable to an also non-significant difference in reading frequency between dyslexics and controls, indicating similar practice in reading, so that reading throughout the testing session did not lead to more stress in dyslexics. The sample’s aforementioned high IQ-levels and high educational level might be a plausible explanation.
Time and money constraints as well as constraints in recruiting suitable adult participants made the application of more tests impossible. Hence, this study has some limitations.
First, there is a possibility that the present sample does not represent the general dyslexic population because a widely prevalent opinion among dyslexia researchers is that people affected by this learning disability have serious problems to get a high school degree or even go on to university education (Warnke, 1999). Therefore, the high number of dyslexic students and academics in this sample suggests that drawing general conclusions from this sample has to be done with caution and might be a source of bias. On the other hand, this sample’s characteristics might be an indicator for a large number of unreported cases of highly-compensated dyslexics who can have problems to make an optimal buying decision as well, but are oftentimes not subjects of clinical or sub-clinical studies.
Second, a lack of previously done research on dyslexia in the context of grocery shopping, has led to a complicated process of designing the informational brochure used according to the research question. The design is assumedly very susceptible to framing effects, and therefore may alter results.
Third, the experiment itself is a simplified version of the task described at the outset of this paper – choosing a multivitamin juice product in a busy supermarket – due to an artificial room setting and beverage container design. The specific room setting took away all typical influences of a supermarket surrounding including stimulus overflow by other products and costumers, time spent on searching for products, and the specific, hectic supermarket atmosphere. Additionally, beverage containers had to be reduced and simplified to a large extend, for example by leaving contained sugar levels out, which was reported by some participants to be a normally crucial nutrient in making a purchase decision. Also, this simplification reduces the information overload on the container, normally created by advertising slogans, to an unusual extent.
Fourth, being presented with information in an isolated and artificially created setting might have increased the attentiveness of subjects, which might not represent levels comparable to the amount of attention usually devoted to decision-making in a grocery shopping context.
Fifth, the survey design was not completely appropriate for the analysis intended to perform. It only allowed using it for the purpose of quantitative analyses to some extent due to a lack of clear distinction between items referring to separate independent variables, different scales within items of the same independent variable and a lack of three items per independent variable. Furthermore, some independent variables did not show a good reliability value47 due to interaction effects caused by the complexity of the topic, as illustrated by the model in Figure 3.
Sixth, further detailed testing is necessary to test for indirect effects as well as interaction effects associated with dyslexia and might allow for drawing more detailed conclusions with respect to the crucial determinants that influence a grocery shopping purchase decision by dyslexics.
Since this study shows that a variety of IQ-levels and different deficits can be present in dyslexics that make them far more susceptible for making a sub- optimal or even harmful buying decision, the question is raised as to how this probability can be lowered pivotally with respect to particular dyslexic information processing characteristics? Or in economic terms, how can their information acquisition costs be reduced? Supporting an early identification of dyslexics by using precise testing, which can form the basis of a detailed assessment of one’s cognitive strengths and weaknesses, is likely to lead to the application of more appropriate educational methods, so that intelligent children whose cognitive functions are partly affected by dyslexia get the chance to get more appropriate education than it is possible at German special schools. This is basically important in two respects; first, the development of behavioural problems and trauma caused by social stigmatizations might be prevented at an early stage, and second, this may lead to higher levels of education, which have been found to be a relevant prerequisite for the interpretation and use of nutrition information (Jacoby et al., 1977).
Following this, according to Moorman (1990), consumers need a frame of reference to encode nutrition information. Although existing knowledge was not a predictor of the purchase decision in this study, evidence suggests that it can enhance information processing by providing readily accessible references. Hence, providing reference points and information on consequences could maximize one’s comprehension (Moorman, 1990). This is crucial because the level of nutrition information consumers acquire at the point of sale essentially impacts their purchase decision (Moorman, 1996). This is supposed to apply particularly to dyslexics.
On the company’s side, it has to be acknowledged that product label design is another important aspect of information acquisition by the consumer. There is evidence for a higher acquisition rate of information on unhealthy products, if consumers understand product labels better (Moorman, 1996), due to the generation of more arousal. Thus, effort-reducing product labels can have immediate and powerful effects on purchases (Russo et al., 1986), and presumably also on the ratio of optimal buying decisions. For instance, through higher memory capacity (Soederberg Miller et al., 2011). A specific example for a product label design that should be able to reduce framing effects is illustrated in Burton et al. (1994). Furthermore, corporations might want to consider offering well-arranged and text-reduced packages that would enhance comprehension levels of dyslexic consumers.
In addition to more understandable product labels, one more option to antagonize sub-optimal decision-making would be to increase the acquisition, comprehension, and knowledge by providing take-home hand-outs (Russo et al., 1986) or in nowadays online available information that displays nutrition information in an easily comprehendible way. Secondly, the use of visual cues such as the picture of a traffic light to label products in a simplistic way could give consumers a pictorial point of reference. However, the latter option would definitely need people who advocate for it, and an institution that designs such policies and controls for them. A third option would be to relieve dyslexic consumers of their confusion by offering easily comprehendible and trustworthy product information in terms of general product design that would increase their confidence in the corresponding brand, and thus leads to more purchases due to a prevention of the avoidance strategy. Therefore, particularly tailored product design and presentation could be worth lots of money for companies.
On the consumer’s side, dyslexics might reduce the probability of making a sub-optimal decision and antagonize effects of confusion by taking additional time and planning their shopping trip carefully and extensively in advance. This might be a way to cope with daily stress and the demands that are put upon them from the outside, but not in a way that they have to avoid buying unknown products. Furthermore, this planning might lead to an increased acquisition rate of nutrition information, which is estimated at only 10% for non-dyslexics during a long shopping trip (Jacoby et al., 1977). Similarly, books such as “Lebensmittel-Lügen” published by the German consumer advice center try to reduce confusion about marketing tricks and framing effects on packages (Klein et al., 2013). This is a first step towards helping dyslexics as well.
Moreover, adequate educational programmes for dyslexic consumers may increase the likelihood of these people to have important product information more available to them in terms of stressing the personal benefits of using, and the negative consequences of failing to use, available information (Cole & Gaeth, 1990). Dyslexics should be given the right methods to cope with their problems and similarly society should start to view dyslexics as people with cognitive assets in opposition to viewing them as stupid or retarded without acknowledging their cognitive abilities. This is underlined, and well-illustrated, by this study’s sample and prominent examples from the business world mentioned at the outset of this thesis.
This study can be extended and followed up in several respects. First, since it was done with highly educated dyslexics, doing research on the effects of lower educated and less compensated dyslexics on grocery shopping decision-making would be one interesting possibility for future research. Second, using a different brochure design might yield different results. Third, there is a huge body of research on influences of product label formats on its comprehension, but this research has only looked at effects of non-learning-disabled people or at low48 and high literates, but not at “medium”49 literate people such as dyslexics. This study should be the start of more studies on this topic coming in the future. Fourth, linking clinical research on learning disabilities including dyslexia and dyscalculia to other everyday activities, particularly in the context of economics, represents abundant research options. Coming from a point that is looking at reasons that cause stress for dyslexics in everyday life, and at possibilities to decrease these resulting stress factors. Possible other interesting research fields include shopping in general, finance, health, insurances, retirement pensions, products with important manuals, administrative processes and general processes where contracts and fine print play a crucial role.
Fifth, a few further questions would be worth evaluating as well. For example, does the business world have a particular perception of dyslexics? Following a negative answer on this question, the subsequently arising question would be how this group might be helped by the removal of stigmas? Also, does the perception of a successful businessman change if he admits to be diagnosed with dyslexia?
Sixth, an issue that has not been discussed so far is “digital dementia” (Spitzer, 2012). Since, the sample at hand is relatively young, a higher ratio of young dyslexics is not surprising but the extent to which those were not able to spell many words became apparent. This is supposedly influenced by the frequent use of different media, predominantly smartphones and computers who offer autocorrect options. Thus, one does not have to think about spelling or memorization of spelling so much anymore, and can rely on these gadgets instead, which is a trend that should be considered when conducting research relating to dyslexia in the future.
This study shows that dyslexia, especially in adults, is a complex learning disability that can have many causes and cannot be clearly differentiated from comorbid problems. Problems can have multi-layered causes. Being affected by this specific LD can have some effects on grocery shopping decision-making by increasing the probability of making a sub-optimal decision by the factor nine. Dyslexics’ decision-making in this context is influenced by worse WMC, information processing skills, and logographic reading, which leads to worse name recall compared to a control group. Simple time constraints made this apparent. Giving dyslexics enough time for making their decision in combination with simplified pictorial access cues can lead to a better decision quality. IQ levels and participants’ reading frequency did not play a significant role. Intuitive decisions or avoiding to make a decision is an often-used strategy by dyslexics to manage demands and tasks put upon them by their surroundings. High global cognitive abilities and a high educational level show that viewing dyslexics as stupid or even retarded is definitely out-dated and has ever been. Many prominent successful examples from business and science illustrate this well. Emphasizing their special talents as assets seem to be legitimate, and a step towards removing persistent stigma.
A more understandable and well-arranged product design in combination with extensive planning of the shopping trip by dyslexics is likely to support them in making an optimal purchase decision.
Abbildung in dieser Leseprobe nicht enthalten
A1. Survey structure and relations
Abbildung in dieser Leseprobe nicht enthalten
Table 4: Survey structure and relations
A2. Logographic reading
Abbildung in dieser Leseprobe nicht enthalten50
Figure 4 : Example logographic reading
A3. Brain areas associated with reading
Abbildung in dieser Leseprobe nicht enthalten51
Figure 5: Distinct brain areas associated with reading
A4. Gap model reading achievement
This model illustrates that the gap between good and poor readers remains stable over time, if both groups get proper reading instruction. Hence both groups are getting better to a similar extend but the difference, obvious from the start, and neurobiological in origin persists.
Abbildung in dieser Leseprobe nicht enthalten52
Figure 6: GAP-model of persistent differences in reading achievement
A5. Causal model of structures in dyslexia
Abbildung in dieser Leseprobe nicht enthalten53
Figure 7: Causal model of structures in dyslexia
A6. Hypotheses overview
Abbildung in dieser Leseprobe nicht enthalten
Table 5: Hypotheses overview
A7. Conceptual model and results
Abbildung in dieser Leseprobe nicht enthalten
A8. Product design
Abbildung in dieser Leseprobe nicht enthalten
Abbildung in dieser Leseprobe nicht enthalten
Alba, J. W. (1983). The Effects of Product Knowledge on the Comprehension, Retention, and Evaluation of Product Information. Advances in Consumer Research, 10 (1), 577–580.
Alexander-Passe, N. (2012). Self-Harm, Suicide and Dyslexia. In Dyslexia and Mental Health. New York: Nova Science Pub Inc. Retrieved from http://www.dyslexia-research.com/page37.html
Anderson, S. L., Adams, G., & Plaut, V. C. (2008). The cultural grounding of personal relationship: The importance of attracti. Journal of Personality and Social Psychology, 95 (2), 352–368.
Anderson, S. P., & de Palma, A. (2012). Competition for attention in the Information (overload) Age. The RAND Journal of Economics, 43 (1), 1–25.
Arnold, M. J., & Reynolds, K. E. (2011). Hedonic Shopping Motivations. In Handbook of Marketing Scales (Third., pp. 77–95). Los Angeles: SAGE Publications.
Aylott, R., & Mitchell, V. (1998). An exploratory study of grocery shopping stressors. International Journal of Retail and Distribution Management, 26 (9), 362–373.
Bach, P. B., & Lewis, R. J. (2012). Multiplicities in the Assessment of Multiple Vitamins. JAMA: the journal of the American Medical Association, 308 (18), 1916–1917.
Badian, N. A. (1997). Dyslexia and the Double Deficit Hypothesis. Annals of Dyslexia, 47.
Bearden, W. O., Netemeyer, R. G., & Haws, K. L. (2011). Handbook of Marketing Scales - Multi-Item Measures for Marketing and Consumer Behavior Research (Third.). Los Angeles: SAFE Publications.
Belz, G. G. (2008). Lebe länger und gesünder. Berlin, Heidelberg: Springer Berlin Heidelberg.
Bernoulli, D. (1954). Exposition of a New Theory on the Measurement of Risk. Econometrica, 22 (1), 23–36.
Bishop, D. V. M., Bright, P., Bishop, S. J., James, C., Tallal, P., & Delaney, T. (1999). Different Origin of Auditory and Phonological Processing Problems in Children With Language Impairment: Evidence From a Twin Study. Journal of Speech, Language, and Hearing Research, 42 (1), 155–168.
Bradburn, N. M., Sudman, S., & Wansink, B. (2004). Asking Questions (First., pp. 136–251). San Francisco: JOSSEY-BASS.
Bub, A., Watzl, B., Blockhaus, M., Briviba, K., Liegibel, U., Müller, H., … Rechkemmer, G. (2003). Fruit juice consumption modulates antioxidative status, immune status and DNA damage. The Journal of nutritional biochemistry, 14 (2), 90–98.
Bund, K., & Rohwetter, M. (2013, August 14). Wahnsinns-Typen. DIE ZEIT, pp. 19–21.
Burton, S., Biswas, A., & Netemeyer, R. (1994). Effects of Alternative Nutrition Label Formats and Nutrition Reference Information on Consumer Perceptions, Comprehension, and Product Evaluations. Journal of Public Policy & Marketing, 13 (1), 36–47.
Childers, Houston, & Heckler. (2011). Style of Processing Scale: SOP. In Handbook of Marketing Scales (Third., pp. 295–296). Los Angeles: SAGE Publications.
Clemons, E. K. (2008). How Information Changes Consumer Behavior and How Consumer Behavior Determines Corporate Strategy. Journal of Management Information Systems, 25 (2), 13–40.
Cole, C. A., & Gaeth, G. J. (1990). Cognitive and Age-Related Differences in the Ability to Use Nutritional Information in a Complex Environment. Journal of Marketing Research, XXVll, 175–184.
Coppola, G. (2007). The ability to grasp the big picture, persistence, and creativity are a few of the entrepreneurial traits of many dyslexics. Just ask Charles Schwab. BusinessWeek Online.
Daniels, J. C. (2006). Figure Reasoning Test, Form A (Third.). Frankfurt am Main: Harcourt Test Services GmbH.
Deimel, W. (2002). Diagnostik der Lese-Rechtschreibstörung. In Legasthenie: Zum aktuellen Stand der Ursachenforschung, der diagnostischen Methoden und der Förderkonzepte (pp. 115–129). Bochum: Verlag Dr. Dieter Winkler.
Deutsche Gesellschaft für Ernährung e.V. (2013). Referenzwerte für die Nährstoffzufuhr. Retrieved October 05, 2013.
Duncan, C. C., Rumsey, J. M., Wilkniss, S. M., Denckla, M. B., Hamburger, S. D., & Odou-Potkin, M. (1994). Developmental dyslexia and attention dysfunction in adults: Brain potential indices of information processing. Psychophysiology, 31 (4), 386–401.
European Dyslexia Association. (2013a). Causes of dyslexia. Retrieved October 28, 2013, from http://www.eda-info.eu/en/causes-of-dyslexia.html
European Dyslexia Association. (2013b). Incidence and emotional effects. Retrieved November 08, 2013, from http://www.eda-info.eu/en/dyslexia-incidence-and-emotional-effects.html
Ferrer, E., Shaywitz, B. A., Holahan, J. M., Marchione, K., & Shaywitz, S. E. (2010). Uncoupling of reading and IQ over time: empirical evidence for a definition of dyslexia. Psychological science, 21 (1), 93–101.
Field, A. (2009). Discovering Statistics Using SPSS (Third.). Los Angeles: SAGE Publications.
Finucci, J. M., Guthrie, J. T., Childs, A. L., Abbey, H., & Childs, B. (1976). The genetics of specific reading disability. Annals of Human Genetics, 40 (1), 1–23.
Fisher, S. E., & Francks, C. (2006). Genes, cognition and dyslexia: learning to read the genome. Trends in Cognitive Sciences, 10 (6), 250–257.
Fletcher, J. M., Shaywitz, S. E., Shankweiler, D. P., Katz, L., Liberman, I. Y., Stuebing, K. K., … Shaywitz, B. A. (1994). Cognitive profiles of reading disability: Comparisons of discrepancy and low achievement definitions. Journal of Educational Psychology, 86 (1), 6–23.
Flynn, J. R. (1987). Massive IQ gains in 14 nations: What IQ tests really measure. Pschological Bulletin, 101 (2), 171–191.
Galaburda, A. M. (1993). Neurology of Developmental Dyslexia. Current Opinion in Neurobiology, 3 (2), 237–242.
Galaburda, A. M., LoTurco, J., Ramus, F., Fitch, R. H., & Rosen, G. D. (2006). From genes to behavior in developmental dyslexia. Nature Neuroscience, 9 (10), 1213–7.
Grigorenko, E. L., Wood, F. B., Meyer, M. S., Hart, L. A., Speed, W. C., Shuster, A., & Pauls, D. L. (1997). Susceptibility Loci for Distinct Components of Developmental Dyslexia on Chromosomes 6 and 15. American Journal of Human Genetics, 60 (1), 27–39.
Hammill, D. D., Leigh, J. E., McNutt, G., & Larsen, S. C. (1987). A New Definition of Learning Disabilities. Journal of Learning Disabilities, 20 (2), 109–113.
Harvard Business Review. (2012, May). IQ Performance Anxiety. Harvard Business Review, 90 (5), 30.
Heikkilä, K., Fransson, E. I., Nyberg, S. T., Zins, M., Westerlund, H., Westerholm, P., Kivimäki, M. (2013). Job strain and health-related lifestyle: findings from an individual-participant meta-analysis of 118,000 working adults. American journal of public health, 103 (11), 2090–2097.
Hui, S. K., Bradlow, E. T., & Fader, P. S. (2009). Testing Behavioral Hypotheses Using an Integrated Model of Grocery Store Shopping Path and Purchase Behavior. Journal of Consumer Research, 36 (3), 478–493.
Ibrahimovic, N., Bulheller, S., & Horn, R. (2006). Intelligenz - Basis - Faktoren, Handanweisung. Intelligenz - Basis - Faktoren. Harcourt Test Services.
Jacobson, E. (2011). Examining Reading Comprehension in Adult Literacy. Adult Basic Education and Literacy Journal, 5 (3), 132–140.
Jacoby, J., Chestnut, R. W., & Silberman, W. (1977). Consumer Use and Comprehension of Nutrition Information. Journal of Consumer Research, 4 (2), 119–128.
Jayakumar, G. S. D. S., & Sulthan, A. (2013). Stress Symptoms : Structural Equation Modelling. SCMS Journal of Indian Management, 10 (3), 95–109.
Just, M. A, & Carpenter, P. A. (1980). A theory of reading: from eye fixations to comprehension. Psychological review, 87 (4), 329–54.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47 (2), 263–291.
Kahneman, D. (2003). A perspective on judgment and choice: mapping bounded rationality. The American psychologist, 58 (9), 697–720.
Kersting, M., & Althoff, K. (2004). RT - Rechtschreibungstest Nichtraucher. (Deutsche Gesellschaft für Personalwesen, Ed.) (3rd ed.). Göttingen: Hogrefe.
Kim, B.-D., & Park, K. (1997). Studying patterns of consumer’s grocery shopping trip. Journal of Retailing, 73 (4), 501–517.
Klein, B., Schauff, A., Weiß, C., Schwein, M., Lammsalami, D., & Erdbeeren, E. (2013). Lebensmittel-Lügen, Wie die Food-Branche trickst und tarnt (First., pp. 1–224). Düsseldorf: Verbraucherzentrale Nordrhein-Westfalen.
Knivsberg, A.-M., & Andreassen, A. B. (2008). Behaviour, attention and cognition in severe dyslexia. Nordic Journal of Psychiatry, 62 (1), 59–65.
Kotler, P., Bliemel, F., & Keller, K. L. (2007). Marketing Management: Strategien für wertschaffendes Handeln. New Jersey: Addison Wesley in Pearson Education.
Krosnick, J. A. (1999). Maximizing Questionnaire Quality. In J. P. Robinso, P. R. Shaver, & L. S. Wrightsman (Eds.), Measures of political attitudes (second., Vol. 2, pp. 37–58). San Diego: Academic Press.
Kruidenier, J. R. (2002). Research-based principles for adult basic education reading instruction. Washington DC: National Institute for Literacy.
Linder, M., & Grissemann, H. (2000). Züricher Lesetest - Förderdiagnostik bei gestörtem Schriftspracherwerb (6th ed.). Bern: Verlag Hans Huber.
Logan, J., Hendry, C., Courtney, N., Brown, J., Frazer, G., & Uk, M. (2008). Unlocking the potential of the UK’s Hidden Innovators (pp. 1–77).
Lynch, J. G., Netemeyer, R. G., Spiller, S. A., & Zammit, A. (2011). A Generalizable Scale of Propensity to Plan. In Handbook of Marketing Scales (Third., pp. 225–227). Los Angeles: SAGE Publications.
Lyon, G. R., Shaywitz, S. E., & Shaywitz, B. A. (2003). Defining Dyslexia, Comorbidity , Teachers ’ Knowledge of Language and Reading A Definition of Dyslexia. Annals of Dyslexia, 53 (1), 1–14.
McDougall, P., Borowsky, R., MacKinnon, G. E., & Hymel, S. (2005). Process dissociation of sight vocabulary and phonetic decoding in reading: a new perspective on surface and phonological dyslexias. Brain and language, 92 (2), 185–203.
Meffert, H., Burmann, C., & Kirchgeorg, M. (2012). Produkt- und programmpolitische Entscheidungen. In Grundlagen marktorientierter Unternehmensführung (Eleventh., pp. 385–387). Wiesbaden: Gabler Verlag.
Meij, H. van der, & Meij, J. van der. (2013). QuikScan formatting as a means to improve text recall. Journal of Documentation, 69 (1), 81–97.
Meng, H., Smith, S. D., Hager, K., Held, M., Liu, J., Olson, R. K., … Gruen, J. R. (2005). DCDC2 is associated with reading disability and modulates neuronal development in the brain. Proceedings of the National Academy of Sciences of the United States of America, 102 (47), 17053–17058.
Meyer, M. S. (2000). The Ability–Achievement Discrepancy: Does it Contribute to an Understanding of Learning Disabilities? Educational Psychology Review, 12 (3), 315–337.
Misra, S., & Stokols, D. (2011). Psychological and Health Outcomes of Perceived Information Overload. Environment and Behavior, 44 (6), 737–759.
Moorman, C. (1990). The Effects of Stimulus and Consumer Characteristics on the Utilization of Nutrition Information. Journal of Consumer Research, 17 (December), 362–374.
Moorman, C. (1996). A Quasi Experiment to Assess the Consumer and Informational Determinants of Nutrition Information Processing Activities: The Case of the Nutrition Labeling and Education Act. Journal of Public Policy & Marketing, 15 (1), 28–44.
Moosburger, H., & Kelava, A. (2012). Testtheorie und Fragebogenkonstruktion (2nd ed., pp. 1–443). Berlin, Heidelberg: Springer-Verlag GmbH.
Morgan, P. W. (1896). A Case of Congenital Word Blindness. The British Medical Journal, 2 (1871), 1378.
Norman, P., & Fraser, L. (2013). Self-reported general health and Body Mass Index: a U-shaped relationship? Public Health, 127 (10), 938–945.
Novak, T. P., & Hoffman, D. L. (2011). Situation-Specific Thinking Styles: SSTS. In Handbook of Marketing Scales (Third., pp. 292–294). Los Angeles: SAGE Publications.
Perfetti, C. A., Goldman, S. R., & Hogaboam, T. W. (1979). Reading skill and the identification of words in discourse context. Memory & Cognition, 7 (4), 273–282.
Płonka, J., Toczek, A., & Tomczyk, V. (2012). Multivitamin Analysis of Fruits, Fruit–Vegetable Juices, and Diet Supplements. Food Analytical Methods, 5 (5), 1167–1176.
Rampersaud, G. C., Bailey, L. B., & Kauwell, G. P. A. (2003). National survey beverage consumption data for children and adolescents indicate the need to encourage a shift toward more nutritive beverages. Journal of the American Dietetic Association, 103 (1), 97–100.
Rampl, L., & Wobker, I. (2012). Marketing Bootcamp: Statistik mit SPSS. Friedrichshafen: Zeppelin Universität.
Ramus, F. (2003). Theories of developmental dyslexia: insights from a multiple case study of dyslexic adults. Brain, 126 (4), 841–865.
Rasilo, H., Räsänen, O., & Laine, U. K. (2013). Feedback and Imitation by a caregiver guides a virtual infant to learn native phonemes and the skill of speech inversion. Speech Communication, 55 (9), 909–931.
Recker, W. W., & Kostyniuk, L. (1978). Factors Influencing Destination Choice for the Urban Grocery Shopping Trip. Transportation, 7 (1), 19–33.
Report of the National Reading Panel. (2000). Teaching children to read: an evidence based assessement of the scientific research literature on reading and its implications for reading instruction. Washington, D.C.
Report of a WHO Consultation. (2000). Obesity: preventing and managing the global epidemic. WHO Technical Report Series 854. Geneva.
Russo, J. E., Staelin, R., Nolan, C. a., Russell, G. J., & Metcalf, B. L. (1986). Nutrition Information in the Supermarket. Journal of Consumer Research, 13 (1), 48.
Schumacher, J., Anthoni, H., Dahdouh, F., König, I. R., Hillmer, A. M., Kluck, N., … Kere, J. (2006). Strong Genetic Evidence of DCDC2 as a Susceptibility Gene for Dyslexia. The American Journal of Human Genetics, 78 (1), 52–62.
Shaywitz, B. A., Fletcher, J. M., Holahan, J. M., & Shaywitz, S. E. (1992). Discrepancy Compared to Low Achievement Definitions of Reading Disability: Results from the Connecticut Longitudinal Study. Journal of Learning Disabilities, 25 (10), 639–648.
Shaywitz, S. E. (1998). Dyslexia. The New England Journal of Medicine, 338 (5), 307–312.
Shaywitz, S. E., Mody, M., & Shaywitz, B. A. (2006). Neural Mechanisms in Dyslexia. Current Directions in Psychological Science, 15 (6), 278–281.
Shaywitz, S. E., & Shaywitz, B. A. (2005). Dyslexia (specific reading disability). Biological Psychiatry, 57 (11), 1301–9.
Shaywitz, S. E., Shaywitz, B. A., Fletcher, J. M., & Escobar, M. D. (1990). Prevalence of Reading Disability in Boys and Girls. The Journal of the American Medical Association, 264 (8), 998–1002.
Shaywitz, S. E., Shaywitz, B. A., Fulbright, R. K., Skudlarski, P., Mencl, W. E., Constable, R. T., … Gore, J. C. (2003). Neural Systems for Compensation and Persistence: Young Adult Outcome of Childhood Reading Disability. Biological Psychiatry, 54 (1), 25–33.
Siegel, L. S. (1989). IQ is Irrelevant to the Definition of Learning Disabilities. Journal of Learning Disabilities, 22 (8), 469–478.
Snowling, M. (1998). Dyslexia as a Phonological Deficit: Evidence and Implications. Child Psychology and Psychiatry Review, 3 (1), 4–11.
Soederberg Miller, L. M., Gibson, T. N., Applegate, E. A, & de Dios, J. (2011). Mechanisms underlying comprehension of health information in adulthood: the roles of prior knowledge and working memory capacity. Journal of health psychology, 16 (5), 794–806.
Spitzer, M. (2012). Im Gehirn speichern oder auslagern in der Wolke? In Digitale Demenz (pp. 96–109). München: Droemer Verlag.
Sproles, G. B., & Kendall, E. (2011). Shopping Styles: Consumer Styles Inventory: CSI. In Handbook of Marketing Scales (Third., pp. 374–377). Los Angeles: SAGE Publications.
Stanovich, K E., & West, R. F. (2000). Individual differences in reasoning: implications for the rationality debate? The Behavioral and Brain Sciences, 23 (5), 645–665.
Stanovich, Keith E, Siegel, L. S., Barsky, V., Chee, M., Duval, L., Metsala, J., Smith, S. (1994). Phenotypic Performance Profile of Children With Reading Disabilities : A Regression-Based Test of the Phonological-Core Variable-Difference Model. Journal of Educational Psychology, 86 (1), 24–53.
Storey, M. L., Forshee, R. A, & Anderson, P. A. (2006). Beverage Consumption in the US Population. Journal of the American Dietetic Association, 106 (12), 1992–2000.
Stothers, M., & Klein, P. D. (2010). Perceptual organization, phonological awareness, and reading comprehension in adults with and without learning disabilities. Annals of Dyslexia, 60 (2), 209–37.
Suri, R., & Monroe, K. B. (2003). The Effects of Time Constraints on Consumers’ Judgments of Prices and Products. Journal of Consumer Research, 30 (1), 92–104.
Suri, R., Monroe, K. B., & Koc, U. (2012). Math Anxiety and its effects on consumer’s preference for price promotions formats. Journal of the Academy of Marketing Science, 41, 271–282.
Taylor, S. E. (1965). Eye movements in Reading: Facts and Fallacies. American Educational Research Journal, 2 (4), 187–202.
Thorndike, R. (1963). The Concepts of Over- and Under-Achievement. The Educational Forum, 28 (1), 79.
Thurstone, L. L. (1941). The Chicago Test of Primary Mental Abilities. Chicago: Science Research Associates.
Tijms, J. (2004). Verbal memory and phonological processing in dyslexia. Journal of Research in Reading, 27 (3), 300–310.
Tversky, A., & Kahneman, D. (1981). The Framing of Decisions and the Psychology of Choice. American Association for the Advancements of Science, 211 (4481), 453–458.
Verplanken, B., & Aarts, H. (1999). Habit, Attitude, and Planned Behaviour: Is Habit an Empty Construct or an Interesting Case of Goal-directed Automaticity? European Review of Social Psychology, 10 (1), 101–134.
Village, E. G. (2001). The Use and Misuse of Fruit Juice in Pediatrics. Pediatrics, 107 (5), 1210–1213.
Viswanathan, M., Hastak, M., & Gau, R. (2009). Understanding and Facilitating the Usage of Nutritional Labels by Low-Literate Consumers. Journal of Public Policy & Marketing, 28 (2), 135–145.
Wadsworth, S. J., Olson, R. K., Pennington, B. F., & DeFries, J. C. (2000). Differential Genetic Etiology of Reading Disability as a Function of IQ. Journal of Learning Disabilities, 33 (2), 192–199.
Warnke, A. (1999). Reading and spelling disorders: clinical features and causes. European Child & Adolescent Psychiatry, 8 (3), 2–12.
Watson, Clark, & Tellegen. (2011). Positive and Negative Affect Scales: PANAS. In Handbook of Marketing Scales (Third., pp. 315–316). Los Angeles: SAGE Publications.
Weeden, J., & Sabini, J. (2005). Physical Attractiveness and Health in Western Societies: A Review. Psychological bulletin, 131 (5), 635–653.
Weiss, U., & Timm, T. (2013). Die Nadel im Heuhaufen Wo liegt der Nutzen von Predictive Analytics? Blue Yonder Facts and Figures.
Wendler, E. R. (1983). Consumer Information and Conficence: Moderating Effects of Perceived Comprehension and Risk. Advances in Consumer Research, 10 (1), 364–369.
Wilcke, A., Weissfuss, J., Kirsten, H., Wolfram, G., Boltze, J., & Ahnert, P. (2009). The role of gene DCDC2 in German dyslexics. Annals of Dyslexia, 59 (1), 1–11.
Wolf, O. T. (2009). Stress and memory in humans: twelve years of progress? Brain research, 1293, 142–154.
World Health Organization. (2010). International Statistical Classification of Diseases and Related Health Problems. Retrieved November 15, 2013, from http://apps.who.int/classifications/icd10/browse/2010/en
Yule, W., Rutter, M., Berger, M., & Thompson, J. (1974). Over- and under-achievement in reading: distribution in the general population. The British journal of educational psychology, 44 (1).
Yule, W., Rutter, M. (1975). The Concept of Specific Reading Retardation. Journal of Child Psychology and Psychiatry, 16 (3), 181-197.
1 In a German sample only 2% did so (Warnke, 1999).
2 Modifications are in square brackets.
3 This is an important symptom and can have effects on the acquisition process of literacy but subjects could not be particularly checked for it in this study.
4 This sub-type is also called surface or morphemic.
5 Metacognitive awareness of phonological structures including rimes, syllables, onsets and phonemes is referred to as phonological awareness (Stothers & Klein, 2010).
6 Logographic reading is the word’s identification latency in context, which is based on two processes: predictability of a word in the given context and identification of the word by using a word’s baseline (Perfetti et al., 1979). If the words baseline is relatively similar wrong word identification is likely to occur (Figure 4 in Appendix).
7 Articulation and word analysis are assigned to Broca’s area (Lyon et al., 2003). Word analysis is assigned to the parieto-temporal area. It operates on individual units such as phonemes and processes relatively slowly (S. E. Shaywitz et al., 2003). Visual word-form is assigned to the left occipito-temporal area (LOT). Neurons within this region are coding for word and letter strings and influence skilled, fluent reading. This region operates on the whole word and processes information very rapidly compared to the parieto-temporal region (S. E. Shaywitz et al., 2006).
8 Evidence suggests a positive effect of phonologically-based reading intervention on the development of neural systems in anterior and posterior brain regions in children, which underlines the importance of early and sound detection of problems caused by dyslexia.
9 Deciding for the multivitamin juice that contains a 100% dosage of all vitamins included, according to the recommended daily allowance, is defined as making an optimal buying decision in this study.
10 Visuo-spatial processes such as perceptual grouping are described as perceptual organization (Stothers & Klein, 2010).
11 The intelligence quotient has a median of 100 with a standard deviation of 15.
12 T-scores have a median of 50 with a standard deviation of 10. Thus, 1.5 standard deviations below the median results in a t-score of 35.
13 This issue whether someone can be considered an “under-achiever” (Thorndike, 1963) who is not performing according to expectations is an important argument in the ongoing debate about the IQ-discrepancy criterion, because IQ-score, as a measure, is nothing that can be perceived as something absolute and fixed. An “under-achiever” has to be understood in an individual sense, because individual differences in performance within a group are normal and conforming to expectations. A group incorporates and represents a range of different feature characteristics (Deimel, 2002) .
14 Alternatively proposed methods deserve to be mentioned, because when applied altogether they would allow a more sound and differentiated diagnosis compared to the IQ-discrepancy-criterion, however, they do not apply to this study due to technical, time and money constraints.
15 An overcorrection would be the switching from bad to worse product.
16 9 out of 10 reasons for changing food consumption behaviour was intended to avoid a negative nutrient.
17 An optimal buying decision means making a buying decision that is “right” and does not have any harmful effects on the consumer’s body due to overdosed ingredients contained by the “false” products.
18 By speaking of dyslexics, I refer to the group comprised of dyslexics and participants with serious reading and spelling difficulties.
19 Water-soluble vitamins C and B contained in multivitamin juices are associated with proper functioning of the nervous and respiratory systems, creation of red blood cells and synthesis of nucleic and fatty blood cells (Płonka, Toczek, & Tomczyk, 2012). Nevertheless intake of 100% fruit juice should not exceed 0.35 litre a day, beyond this amount consumption is already called excessive (Rampersaud, Bailey, & Kauwell, 2003). An excessive consumption of juice can lead to diseases including obesity, malabsorbtion of carbohydrates (Village, 2001).
20 Americans between the ages of 20-39 years consume almost the same amount of fruit juice and drinks than milk (Storey, Forshee, & Anderson, 2006).
21 (Arnold & Reynolds, 2011; Childers, Houston, & Heckler, 2011; Lynch, Netemeyer, Spiller, & Zammit, 2011; Novak & Hoffman, 2011; Sproles & Kendall, 2011; Watson, Clark, & Tellegen, 2011).
22 This gender effect was significant (X2 = 19.185; p < .001) and in line with Snowling (1998) reporting dyslexia is predominant in males. Also gender and group classification were significantly correlated (r = .637; p < .001).
23 BMI is calculated by means of the following formula: . Its categories are associated with risk of non-communicable diseases (Norman & Fraser, 2013).
24 Occasional buying of multivitamin juice: dyslexics (M = 1.46; SD = .51), controls (M = 1.56; SD = .51), t (40)= -.611, p > .05. Willingness to buy the study’s products: dyslexics (M = 1.77; SD = .43), controls (M = 1.78; SD = .43), t (38) = -.037, p > .05.
25 Scores on the positive affect scale as well as on a negative affect scale range from 10 to 50, with higher scores representing higher levels of positive or negative affect, respectively. Mean score for momentary positive affect = 29.7 and for negative affect = 14.8 (Watson et al., 2011).
26 Scores on the positive affect scale as well as on a negative affect scale range from 10 to 50, with higher scores representing higher levels of positive or negative affect, respectively. Mean score for momentary positive affect = 29.7 and for negative affect = 14.8 (Watson et al., 2011).
27 Scale ranges from 1 most important to 5 least important.
28 A binary logistic regression approach has been used due to the data’s following characteristics: one categorical outcome variable, two or more predictors of both types and different participants (Field, 2009). All analyses have been performed with the computer program SPSS 22.0.
29 This study illustrates an exception due to the classification method. Participants got classified after the test session and thus accounted for different and independent groups but were presented with the same experimental stimuli and questions. This study design made a classification in the usual way described by Field (2009) impossible. Therefore, the use of independent t-tests to test this between subject design for proving some of the hypothesis seemed appropriate, because with these items it was most important to analyze single items according to group classification.
30 Variable was binary coded as in one group showing IQ-scores up to 115 and a second group showing IQ-scores of 116 and higher.
31 The variable “name recall” was normally distributed.
32 The effect size for the contrast analysis is calculated according to the following formula: . Effect sizes are classified as follows: 0.1 = small effect; 0.3 = medium effect; 0.5 = large effect.
33 The bigger the mean value the more often participants have made an optimal decision.
34 Levene-Test: differentiating between product names, p = .011.
35 Levene’s test for equality of variances was only significant fort the second buying decision p = .013 but not for the first decision p = .268.
36 + represents hypothesis was confirmed; – represents hypothesis was not confirmed.
37 Exclusion of three participants due to IQ-scores < 85.
38 In this study two participants who had to be excluded from the analysis due to an IQ-score < 85 reported being affected by ADHD and showed obvious spelling problems. Their hyperactivity seemed to make them unable to focus on tasks of the IQ test.
39 Giving a detailed diagnosis is particularly important in children, so that they do not get “parked” as special needs students way too early. Because “parking” them at special schools can be detrimental to their general development by not discovering and promoting this person’s strengths appropriately. This can have strong effects in many respects all the way up into adulthood.
40 Otherwise a 50:50 share would have been expected.
41 Sub-optimal decision: dyslexics 75%; controls: 33,3%.
42 Viwamenolatipo and Vitamenolatipo.
43 One subject refused to make a decision.
44 Four out of six achieved a global IQ-score above 113 points and five out of those six are students or academics.
45 German secondary education degree.
46 Optimal decision: dyslexics: 66.7%; controls: 83.3%.
47 Values for Cronbach’s alpha <= 0.5.
48 In the literature having low literacy skills is seen as being almost illiterate (Viswanathan et al., 2009).
49 I propose to call the most prevalent forms of dyslexia “medium literate” due to a non-fit of definitions for low and high literacy (Viswanathan et al., 2009).
50 “Männchen” is the German word for manikin; “Mädchen” is the German word for girl.
51 Adapted from S. E. Shaywitz et al. (2006).
52 Adapted from S. E. Shaywitz and Shaywitz (2005).
53 Adapted from Warnke (1999).
Masterarbeit, 74 Seiten
Bachelorarbeit, 34 Seiten
Bachelorarbeit, 106 Seiten
Bachelorarbeit, 46 Seiten
Forschungsarbeit, 8 Seiten
Doktorarbeit / Dissertation, 340 Seiten
Diplomarbeit, 138 Seiten
Bachelorarbeit, 82 Seiten
Bachelorarbeit, 46 Seiten
Der GRIN Verlag hat sich seit 1998 auf die Veröffentlichung akademischer eBooks und Bücher spezialisiert. Der GRIN Verlag steht damit als erstes Unternehmen für User Generated Quality Content. Die Verlagsseiten GRIN.com, Hausarbeiten.de und Diplomarbeiten24 bieten für Hochschullehrer, Absolventen und Studenten die ideale Plattform, wissenschaftliche Texte wie Hausarbeiten, Referate, Bachelorarbeiten, Masterarbeiten, Diplomarbeiten, Dissertationen und wissenschaftliche Aufsätze einem breiten Publikum zu präsentieren.
Kostenfreie Veröffentlichung: Hausarbeit, Bachelorarbeit, Diplomarbeit, Dissertation, Masterarbeit, Interpretation oder Referat jetzt veröffentlichen!