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Simple View of Reading
The Simple View of Reading: Advancements and false impressions
Hugh W. Catts, Ph.D.
Florida State University
Remedial and Special Education (in press)
Contact information: Warren Building, 201 Bloxham St., Tallahassee, FL, 32306,
hcatts@fsu.edu
Disclosure information: The author was a Co-Investigator for a grant, Language Basis of Reading
Comprehension, funded by the Institute of Educational Science (R305F1000002) as part of the Reading
for Understanding Initiative. Work from this project is referenced in several instances in the
commentary.
Simple View of Reading
The Simple View of Reading (SVR) was introduced by Gough and Tunmer over 30 years
ago in a short paper in this journal (Gough & Tunmer, 1986). At the time, it was unlikely that the
authors had any appreciation of the impact their rather simple but insightful conceptualization
would have on the field of reading. Most of this impact has been positive and has led to
significant advancements in our understanding of reading comprehension. In this commentary, I
will highlight these advancements as well as the contributions of the other research papers in this
volume. I will also raise the possibility that the impact of the SVR has not been completely
positive. I will argue that the simplicity of its presentation has unintentionally contributed to
some false impressions about comprehension, and in doing so, has led us astray in important
ways.
A Framework for Reading Comprehension
Since the introduction of the SVR, hundreds of studies have used this model to guide
their investigation and/or interpret their results. Many investigations have directly examined the
main premise of the model; that is, reading comprehension is the product of decoding and
language comprehension1. This work has confirmed that much of the variance in reading
comprehension can be accounted for by individual differences in decoding and language
comprehension (Catts, Hogan, & Adlof, 2005; de Jong & van der Leij, 2002; Hoover & Gough,
1990). This has been shown to be the case in English readers as well as readers of other
alphabetic orthographies including Greek (Protopapas, Simos, Sideridis, & Mouzaki, 2012),
Hebrew (Joshi, Ji, Breznitz, Amiel, & Yulia, 2015), and Italian (Tobia & Bonifacci, 2015) as
well as non-alphabetic writing systems like Chinese (Ho, Chow, Wong, Waye, & Bishop, 2012;
See Florit & Cain 2011 for review). The SVR has also been used to account for individual
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differences in reading comprehension of second-language learners (Hoover & Gough, 1990;
Verhoeven & van Leeuwe, 2012) and dual-language users (Bonifacci & Tobia, 2017).
Whereas decoding and language comprehension account for much of the variance in
reading comprehension, the relative relationship of these components to comprehension appears
to vary across the school grades (Catts, et al., 2005; Language and Reading Research
Consortium, 2015; Tilstra, McMaster, van den Broek, Kendeou, & Rapp, 2009). In the initial
school grades, decoding abilities explain a majority of the variance in reading comprehension,
whereas in later grades, it is the language comprehension component that accounts for most of
the variability. It is not surprising that for children just learning to read that decoding skill has the
greatest impact on comprehension. The shift to the dominance of language comprehension
appears to occur once decoding has become faster and more automatic, and the vocabulary,
grammar, and discourse demands of reading materials have increased. This occurs somewhere
around 3rd or 4th grade for typically developing readers in English (Catts et al., 2005; Language
and Reading Research Consortium, 2015). However, this may occur later in more opaque
orthographies (Joshi et al., 2015) and perhaps even earlier in transparent orthographies. For
example, in a cross-sectional study involving Italian speaking children, Tobia and Bonifacci
(2015) found that language comprehension was the primary contributor to reading
comprehension right from the beginning of the primary grades (also see Florit & Cain, 2011).
The changing relationship between factors in the SVR was also investigated by Lonigan,
Burgess, and Schatschneider (2018, this volume). Using a latent variable approach, they showed
that decoding and language comprehension accounted for nearly all of the variance in reading
comprehension among a sample of children in grades 3 through 5. Like the studies above, they
observed that decoding played a larger role in reading comprehension in earlier grades and
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language comprehension played a greater role in later grades. Such a finding may indicate that
language comprehension is more important for more skilled readers than it is for less skilled
readers. Lonigan and colleagues further provided some limited support for this inference using
quantile regression analyses to look at relationships across comprehension ability independent of
grade. Finally, the authors highlight an important finding that is often overlooked. That is, the
variance shared by decoding and language comprehension typically accounts for as much, if not
more, variance in reading comprehension than does the unique variance of either variable. They
speculate that this shared variance may be related to general cognitive linguistic ability, and as
such, may limit how easy it will be to substantially improve reading comprehension.
The SVR has also contributed to our understanding of the cognitive prerequisites of
reading comprehension and to the early identification of comprehension problems. Gough and
Tunmer’s original paper paid particular attention to decoding. It was written at the height of the
whole language movement, and the authors wanted to highlight the importance of decoding to
comprehension. Their paper as well as others led to the consideration of decoding-related
prerequisites such as phonological awareness, rapid naming, and letter knowledge for the early
identification of reading problems. More recently, proponents of the SVR have turned their
attention to language comprehension and its corresponding prerequisites. This work has focused
on the language basis of reading comprehension and on early language abilities as important
prerequisites and predictors of later comprehension (Catts, Herrara, Nielsen, & Bridges, 2015;
Kendeou, van den Broek, White; & Lynch, 2009; Storch & Whitehurst, 2002. Language and
Reading Research Consortium and Chiu (2018, this volume) extend this work by documenting
the pathway of prekindergarten language abilities though language comprehension to 3rd grade
reading comprehension. They found that measures of vocabulary, grammar, and discourse in
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preschool children predicted 3rd grade language comprehension, which in turn was related to 3rd
grade reading comprehension. The implications of this line of research is that if we are going to
adequately identify children at risk for the full range of reading disabilities, early screening
protocols need to include measures of oral language as well as decoding-related predictors (also
see Catts, Nielson, Bridges, & Liu, 2016; Foorman, Torgesen, Crawford, & Petscher, 2009).
The SVR has also proven to be useful in the classification of reading disabilities. Gough
and Tunmer (1986) introduced the SVR in part to illustrate how poor readers might be classified
into three types; those with problems in decoding, language comprehension, or both. The first
was dyslexia, the second hyperlexia and the third was referred to as garden variety reading
disability. Subsequent studies using the SVR framework have identified each of these types of
problems among children with comprehension deficits (Aaron, Joshi, & Williams, 1999; Ebert
& Scott, 2016). There is also some indication that the percentage of each group among poor
readers may change across grades (Catts et al., 2005), which is in keeping with the previously
mentioned finding that the contribution of decoding and language comprehension to reading
comprehension changes across grades.
The SVR has drawn particular attention to poor readers with the second type of reading
problem (Cain & Oakhill, 1999; Nation, Cocksey, Taylor, & Bishop, 2010). This group is more
often referred to as poor comprehenders or children with a specific comprehension deficit rather
than Gough and Tunmer’s original designation of hyperlexic. These children have poor reading
comprehension but adequate or better decoding. The SVR assumes that these children have a
primary problem in language comprehension. Indeed, research has shown that poor
comprehenders have oral language deficits (Catts, Adlof, & Weismer, 2006; Nation et al., 2010).
However, poor comprehenders’ problems go beyond oral language and include difficulties in
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working memory, inferencing, and world knowledge (Cain, 2006; Cain & Oakhill, 1999;
Compton, Miller, Gilbert, & Steacy, 2013). These factors, however, may still be considered to
be part of language comprehension, and variability in them may be partitioned into this
component along with variability due to oral language. Nonetheless, to effectively address the
problems of poor comprehenders, we need to know specifically what comprises this component
and what aspects are malleable. By its nature, the SVR partitions the variance of reading
comprehension into decoding and language comprehension, but does not specify subcomponents.
There have been some attempts to further delineate language comprehension (Language and
Reading Research Consortium & Logan, 2017; Lervag, Hulme, Melby-Lervag, 2017), but
additional work is needed to more fully understand this construct and its influence on reading
comprehension. This is particularly the case since the SVR is being used to set standards and
guide educational practice in the schools (Rose, 2006).
Expanding the Simple View
Research on the SVR has also considered whether or not additional factors are needed in
the model. One such line of work has focused on the question of whether reading fluency needs
to be added or if word reading accuracy is sufficient to capture the variance due to decoding
(Adlof, Catts, & Little, 2006; Kershaw & Schatschneider, 2012; Protopapas et al., 2012;
Language and Reading Research Consortium, 2015; Tilstra et al., 2009). In related studies,
researchers have also sought to determine if naming speed adds to the prediction of reading
comprehension (Johnston & Kirby, 2006; Joshi & Aaron, 2000). The results of this line of
investigation are not entirely consistent; some studies indicate a need to include fluency (or
naming speed) and others do not. One factor that may help explain the inconsistency of the
findings is the grade at which reading is examined. For example, a recent investigation found
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that in 1st and 2nd grade, word reading accuracy was the best predictor of reading comprehension
(beyond language comprehension), but in 3rd grade it was word reading fluency that was the best
predictor (Language and Reading Research Consortium, 2015). This may indicate that once
children become more accurate in their word reading, fluency may be a more sensitive indicator
of word reading ability and the variance it explains in reading comprehension. Furthermore, in
more transparent orthographies, fluency may be a more powerful predictor of word reading
ability than accuracy from the beginning of school (Florit & Cain, 2011). Thus, the components
of SVR may need to be qualified based on the grade and the transparency of the orthography.
Another line of research has proposed that vocabulary may account for variance in
reading comprehension over and above decoding and language comprehension (Braze, Tabor,
Shankweiler, & Mencl, 2007; Ouellette & Beers, 2010). This finding is difficult to reconcile
with the SVR because vocabulary is typically considered to be part of the language
comprehension component. One explanation for the observance of the unique contribution of
vocabulary in some studies is that measures of vocabulary are more reliable or more central to
the construct of language comprehension than are other language measures used in these studies.
Support for the latter hypothesis comes from latent variable approaches that have found that
when language comprehension is assessed by multiple indicators and modeled accordingly, a
vocabulary factor is no longer needed within a simple view model (Braze et al., 2016;
Protopapas, Mouzaki, Sideridis, Kotsolakou, A & Simos, 2013; Tunmer & Chapman, 2012).
There have been further expansions on the SVR that have added variables that have not
typically been considered to be part of the model. For example, Aaron, Joshi, Gooden, & Bentum
(2008) proposed the Component Model of Reading, in which psychological (motivation, interest,
learned helplessness) and ecological (classroom environment, peer influence) components were
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added to the cognitive components of the SVR. There is some initial support for this model
(Chiu, McBride-Chang, & Lin, 2012; Ortiz, Folsum, Al Otaiba, Greulich, Thomas-Tate, &
Connor, 2012), but a full test of its validity is yet to be shown. In another line of work, Francis,
Kulesz & Benoit (2018, this volume) further expand, or as they say, alter the SVR to include
other factors not typically included in this model. In what they call the Complete View of
Reading, they add text-level variables to the components of the SVR. As I also make reference to
in the next section, Francis and colleagues argue that it is not just individual differences in
cognitive abilities between readers that influence comprehension, but also how individual readers
make use of these abilities to comprehend different texts for different purposes. They note that
another well-known framework of reading comprehension, the Text and Discourse Framework
(e.g., McNamara, Graesser, & Louwerse, 2012) has been especially concerned with how texts
and features of linguistic discourse impact comprehension, However, seldom have investigators
examined how reader and text characteristics impact RC in the same model. In fact, most studies
involving the SVR have treated text features as nuisance variables and have averaged across
texts or text activities to control these variables. In this volume, Francis and colleagues employ
cross-classified random effects models to test their view of reading comprehension. They find
evidence for text-level as well as person-level effects on reading comprehension that vary across
readers. In addition, they add a developmental perspective by looking at how these effects vary
across 6th to 8th grades.
False Impressions
Whereas the SVR has significantly advanced our understanding of reading
comprehension, the simplicity of its presentation has also contributed to some false impressions
about comprehension. The SVR highlights decoding and comprehension in a comparable manner
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and typically displays them graphically in the same sized fonts and/or boxes (Kirby & Savage,
2008; Language and Reading Research Consortium, 2015; Protopapas, Simos, Sideridis, &
Mouzaki, 2012; van Wingerden, Segers, van Balkom, & Verhoeven, 2017). In addition, in many
of the studies of the SVR that use statistical modeling, the constructs representing decoding and
comprehension are displayed graphically in similar ways and often have a similar number of
indicators (Tobia & Bonifacci, 2015; Tunmer & Chapman, 2012; van Wingerden et al., 2017).
As such, these presentations have often led to the impression that comprehension is not all that
different from decoding in terms of its complexity and malleability. At a rational level, we know
this is not the case, and Gough and Tunmer (1986) surely did not intend us to have this
impression. However, the way things are presented matters and can lead us to think in illogical
ways. For example, we somehow have the impression that something that costs $9.99 is much
less expensive than something that costs $10 or that a house with a higher listing price is really
worth more than the same house with a lower listing. Nobel Laureate, Daniel Kahneman (2011)
refers to the latter as the “anchoring effect,” and he and his colleague Amos Tversky have
uncovered many ways in which the mind is tricked by the presentation of the variables involved.
Our false impressions, derived in part from the SVR and models like it, may be another example
of such trickery.
One false impression that I believe the SVR has contributed to is the notion that
comprehension, both language and reading comprehension, is unidimensional and not nearly as
complex as it really is. By displaying comprehension alongside decoding in a comparable
fashion, we have often been led to think that comprehension, like decoding, is a “single thing.”
Again at a rational level, we know that this is not true. However, we generally give the
complexity of comprehension “a nod” and go on to measure it with a single test (or construct)
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and talk about it as if it were a single entity. In reality, comprehension is a multidimensional
cognitive activity and one of the most complex behaviors that we engage in on a regular basis.
The extent of the complexity has been recognized in the literature for many years (e.g., Kintsch
& van Dijk, 1978: Lipson & Wixson, 1986) and was summarized over 15 years ago by the
RAND Corporation Reading Study Group (2002). This group conceptualized reading
comprehension as a multidimensional ability that is influenced by reader, text, and task variables.
According to this model, comprehension is much more variable than the SVR model would lead
us to believe. In fact, any one individual may have many different levels of reading
comprehension depending on what they are reading and why they are reading it. Thus, despite
what is sometimes implied by the SVR, we cannot reduce comprehension to a single entity or
score. To adequately understand the processes involved in comprehension and the individual
differences in these processes, we need to examine it from a multidimensional perspective.
Pearson, Valencia, and Wixson (2014) argue quite convincingly that we can only adequately
measure reading comprehension by considering how well students comprehend specific texts for
specific purposes. Gough and colleagues (Gough, Hoover, & Peterson, 1996) actually
acknowledged this in describing their original model. They noted that while decoding was a
general factor, comprehension was quite variable and specific to what was being read.
Unfortunately, this notion has been overlooked in many applications of the SVR.
Another way I believe the SVR has played a part in leading us astray is in our
expectations about the malleability of comprehension. In recent years, we have made great
strides in teaching children to decode and read words. In the United States, No Child Left Behind
(NCLB, 2002) and its related initiatives of Reading First and Early Reading First have led to the
implementation of research-based practices for teaching children to read and spell words. Other
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countries have experienced similar advancements in their early reading instruction (e.g., Rose,
2006). While changes in instructional practices have not always translated into improvements in
reading, research has documented that well designed interventions directed at decoding abilities
can significantly impact these abilities in struggling readers (Denton, Tolar, & Francis, 2013;
Lovett et al., 2017).
Given our successes in decoding, I think our false impressions about the comparability of
decoding and comprehension led us to expect that the successes with decoding could be
replicated with comprehension. For example, the recent Reading for Understanding Initiative
(RFU), funded by the Institute of Education Sciences, began with rather high expectations of our
ability to better understand and instruct comprehension (Douglas & Albro, 2014). Indeed, this
initiative has led to important new knowledge concerning comprehension (Language and
Reading Research Consortium, 2015; and other papers in this volume), but this knowledge has
not readily translated into significant instructional gains in reading comprehension. RFU studies
have found that instruction on the component skills of comprehension have led to improvements
in these component skills, but for the most part, have not significantly impacted performance on
standardized tests of listening or reading comprehension (Phillips, Kim, Lonigan, & Connors,
2015); Piasta, Language and Reading Research Consortium, & Jiang, 2016; Wanzek, Swanson,
Vaughan, Roberts & Fall, 2016). Other research programs have reported similar findings
(Elleman, Lindo, Morphy, & Compton, 2009; Fuchs et al., in press; Lesaux, Kieffer, Faller, &
Kelley, 2010). Furthermore, in a meta-analysis of intervention studies for struggling readers over
the last 30 years, Scammacca, Roberts, Vaughan, and Stuebing (2015) reported that the average
effect size of interventions on standardized measures of reading comprehension was .19, which
is a small effect. In addition, comparable or lower effect sizes were found by Boulay, Goodson,
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Frye, Blocklin, and Prize (2015) for reading comprehension in a review of RCT intervention
studies funded by the Department of Education Striving Readers Initiative.
One reason for the difficulty in improving reading comprehension may be that a large
portion of the variance in comprehension is related to general cognitive-linguistic abilities that
are rather stable in nature. Lonigan et al. (2018, this volume) found that a considerable amount
of the variance in reading comprehension (40-70%) was shared by decoding and language
comprehension and suggested that this shared variance was the result of one or more general
cognitive-linguistic factors. Other studies have also found that the common variance between
decoding and language comprehension accounts for as much, if not more, variance in reading
comprehension than does the unique variance of either (e.g., Catts et al., 2005). It is still
possible that the cognitive-linguistic factors that underlie this common variance are malleable,
but it is not clear at this point what they are and how they might be changed.
Beyond its relationship to general cognitive-linguistic abilities, reading comprehension is
far more complex than decoding. As noted above, comprehension is not a single thing but a
multidimensional cognitive activity. Because of this, significant and widespread improvements
in comprehension are unlikely to result from general instructional approaches such as teaching
children to use reading strategies. Research does show that we can make some positive changes
by teaching children to be more strategic readers (Swanson, Hairrell, & Kent, 2014). However,
strategy instruction is likely to work best when strategies are specific to the purpose of reading,
and when they are combined with adequate content knowledge (Willingham, 2006). More
generally, the multidimensional nature of comprehension lends itself better to instruction that is
tailored to students’ abilities with specific texts and tasks. This instruction would entail
identifying educationally relevant reading comprehension activities and directly teaching the
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component skills/knowledge bases involved in these activities. For example, in science
curricula, students are often asked to evaluate an argument such as the benefits of solar power or
the effects of climate change. Instruction for such a comprehension activity might best begin
with a review of the content knowledge associated with solar power or climate change.
Adequate content knowledge is critical for comprehension and should be central to any
instruction directed at improving it (Willingham, 2006). Given the centrality of content
knowledge, it is always surprising how little attention has been devoted to it in comprehension
intervention. Gough et al. (1996) clearly recognized this, but again the role of content knowledge
in the SVR has typically been neglected. Following a review of the content knowledge related to
the argument, students would be provided with specific strategy instruction in how to identify a
claim, evaluate the evidence, and consider the bias of both the author and the reader. They
would also be given instruction and practice in how to best communicate this evaluation in the
required task format (written essay, graphic presentation, oral report, etc). As noted above,
comprehension is typically associated with a task, and as such, instruction in task demands
should lead to better outcomes. A comparable scenario could be devised to evaluate the
effectiveness of this intervention. Such assessment would be much more informative than a
standardized measure of comprehension. If an assessment is not matched well with the
intervention and a theory of change related directly to the intervention, it would not be surprising
that one would find proximal gains but no significant gains on distal standardized measures. This
probably explains in part why most reported intervention gains on standardized instruments are
so small.
In summary, the SVR has, in many ways, been a useful framework for our understanding
of comprehension. It has helped us conceptualize the processes involved in comprehension and
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how these might contribute to individual differences. The SVR has also led to insights into ways
to classify and identify children with reading disabilities. Despite these advancements, the SVR
has also contributed to false impressions about the complexity and malleability of
comprehension. I have argued that we need to more fully recognize the multidimensional nature
of comprehension and take a more specific approach to intervention (and assessment). I have
suggested one scenario for intervention, but there are numerous other educationally relevant
comprehension activities that could be the target of similar intervention. By taking a less than
simple view of comprehension we should be better able to design specific interventions that can
impact students’ performances in relevant ways.
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Footnote
1 The term “language comprehension” is used to refer to language and related processes that play
an important role in understanding words, sentences, and discourses regardless of the modality
(reading or listening). It is analogous to Gough and Tunmer’s (1986) notion of linguistic
