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Curriculum-based measurement and developmental reading models: opportunities for cross-validation.

Curriculum-Based Measurement and Developmental Reading Models: Opportunities for Cross-Validation

The merit of basic or theoretical versus applied or practical research is a long-standing topic of debate among researchers and practitioners. Harris (1988) has argued not for a greater emphasis on either basic or applied research, but, rather, for more emphasis on the interface between the two--between the "why" and the "how" questions. Similarly, Bernice Wong (1988), editor of a 15-article series on basic versus applied research in learning disabilities, concluded that both kinds of research are necessary for advancement of knowledge and urged increased cooperation between both camps of researchers. In this vein, we propose that models of reading development constructed by basic research provide support for the applied methods of curriculum-based measurement (CBM), while the effectiveness of CBM in the field provides further verification for the validity of the reading models.

Curriculum-based assessment (CBA) is the generic term for procedures that sample a student's skills in the student's actual curriculum. A variety of models of CBA tend to emphasize measurement that is brief, is frequent, is based on the student's classroom curriculum, and can be used to monitor instructional progress and effectiveness (Frisby, 1987; Tucker, 1985).

Most models of CBA rely on informal, non-standardized procedures; however, CBM is an empirically derived, standardized form of CBA developed by Stanley Deno and his colleagues at the University of Minnesota. With CBM, academic skills are assessed through repeated 1- to 3-minute (min) rate samples using stimulus materials taken from a student's curriculum. CBM has been applied to several academic areas, but most research has dealt with reading, which is the focus of this article. Perhaps because of the variety of educational decisions for which CBM has been used, CBM has become one of the more controversial forms of CBA (Allen & Marston, 1988; Deno, 1988; Lombard, 1988a, 1988b).

The two reading models addressed here, Chall's (1983) stages of reading development and LaBerge and Samuels' (1974) model of automaticity in information processing, both view learning to read as a developmental process consisting of component skills that build on each other. These models emphasize decoding skills at the beginning levels of reading, in contrast to holistic models that emphasize whole-word meanings.

We first outline the rationale and development of CBM and its empirical support. Then we summarize two reading models before we discuss how CBM and the reading models may validate each other.



Development of Measures

One of the goals of the University of Minnesota Institute for Research on Learning Disabilities (IRLD) from 1977 to 1983 was to develop a measurement system that would allow teachers to routinely monitor student achievement. The basic requirements of such a system were that it be (a) reliable and valid, (b) simple and efficient, (c) easily understood, and (d) inexpensive--allowing for repeated measurement (Deno, 1985). Measures were developed in three areas: reading, spelling, and written language (Deno, Marston, & Mirkin, 1982; Deno, Mirkin, & Chiang, 1982; Deno, Mirkin, Lowry, & Kuehnle, 1980); but as previously noted, reading is the focus of this article.

In approaching the question of what in reading should be measured and how it should be measured, the Minnesota IRLD research group started with the assumption that the goal of reading is comprehension. They soon decided that although directly assessing comprehension through answering comprehension questions might be a task high in content validity, this approach was not simple, efficient, or economical (Deno, 1985).

Through a review of the literature on reading, other possibilities for assessment methods were determined and then tested in field trials. These methods included oral reading of words from word lists, oral reading from basal text passages, saying the meanings of underlined words, and supplying words deleted from a text passage (a cloze procedure). Student performance on these measures was then correlated with performance on standardized achievement tests (Deno, Mirkin, & Chiang, 1982). In this study of criterion validity, it was found that of the CBM tasks, all but the word-meaning task were highly correlated with the standardized, norm-referenced tests (correlations generally of .70 to .95).

The researchers had expected to find close relationships between the word-reading tasks and the standardized word-recognition subtests, but they also found that the number of words read orally in a fixed amount of time correlated highly with comprehension scores (.78 and .80 respectively, on the Literal and Inferential subtests of the Stanford Achievement Test). For the most part, the correlations of the oral reading tasks with the standardized measures were as high as or higher than the correlations of the cloze task with the criterion measures. Furthermore, the oral reading rates were found to increase consistently with increased grade levels and to show reliable differences between the performances of regular class and LD resource room students. It also was found that it was the number of words read correctly rather than the number of errors made that provided the most sensitive measure of progress over time.

On the basis of these findings, it was decided that 1-min rate measures of oral reading from either isolated word lists or text passages best met the original measurement criteria (Deno, Mirkin, & Chiang, 1982). These have been the measures used in almost all studies done by those who have adopted the Minnesota IRLD CBM procedures. Subsequent studies have continued to support the technical adequacy and usefulness of CBM in a variety of educational decision roles. See Fuchs, Fuchs, and Maxwell (1988) for recent validity data supporting these findings and Tindal (1988) and Shinn (1989) for comprehensive reviews of CBM research and use.

CBM and Reading Theories

Considering the complexity of a skill like reading, it does seem somewhat contrary to logic that a task as simple as having the student read for 1 min from a classroom text can provide much information about that student's ability to read. Our sense is that skepticism about CBM is not overcome until teachers use it with students for a reasonable period of time and see for themselves that this measure or oral reading fluency really does give them a good sense of students' general reading skills.

Thus, acceptance of CBM seems to rely heavily on face validity despite the criterion and discriminant validity studies that lend more scientific support to its usefulness. Fuchs, Fuchs, and Maxwell (1988) recognized this face validity issue when they identified alternative informal reading measures. Even though these measures do not have the criterion and construct validity of oral reading rate measures, the alternative measures may be seen by educators as more useful because of greater perceived face validity.

Perhaps part of the acceptance problem lies in the fact that those who support CBM in the literature focus on proving that it works rather than looking at why it works. This may be viewed as the difference between an empirical versus a theoretical orientation, the difference between an applied versus basic research focus, or the difference between "Because it works" and "It works because...." But, like the child who responds to a parent's frustrated "Just because" with "Because why?" educators, too, tend to feel more comfortable with a procedure when they have a sense of why it works.

When theory is discussed in relation to CBA methods, the focus generally is either on a concept of assessment that is grounded in the student's actual curriculum (Gickling & Thompson, 1985) or on the concept of basing instructional decisions on direct, repeated measurement and time-series data (Deno, 1986; Marston & Magnusson, 1988). Little attention has been paid to looking at why rate measures apparently are such valid measures of reading.

A new theory of reading does not need to be constructed to explain why CBM correlates so highly with other measures of reading and seems to be such a useful measure of reading. CBM, as a rate measure, fits well with developmental models of reading, such as Chall's (1983) stages of reading development and LaBerge and Samuels' (1974) model of automaticity.



Developmental models of reading, as described by Chall (1983), LaBerge and Samuels (1974), Perfetti and Lesgold (1979), and others, assume that reading is not holistic, but is made up of component skills. These components begin with letter-sound recognition and then proceed to decoding skills. Each component is sufficient for a time, but then new skills must be achieved if reading proficiency is to increase. Later components include the development of fluency, comprehension, and the ability to integrate and synthesize materials.

Chall's Stages of Reading

Chall (1983) has developed a six-stage model of reading development. At Stage 0 (preschool), print itself has minimal meaning for a child. In Stage 1 (grades K-2), the child must learn the alphabet, sounds of the letters, and letter groups. During this stage, the reader is "glued to the print," and his or her reading is slow and laborious. Decoding skills learned in Stage 1 are then practiced in Stage 2 (grades 2-3). By reading material that has a familiar content and language style, children develop use of context, fluency, and speed. A greater reliance on meaning also is evident.

In Chall's Stage 3 (grades 4-8), the reader is beginning to learn from reading. Characteristics of Stage 3 include the ability to concentrate less on the print and more on ideas, accurately reading details, and gaining facts from one point of view. A person must acquire a large amount of knowledge in this stage to be able to deal with Stage 4 (high school), where large quantities of material with more than one point of view are read. The Stage 4 reader is able to report varying views, but cannot integrate them. Finally, in Stage 5 (college), the reader is able to integrate and evaluate complex material representing contradictory point of view.

Most people progress through the stages in the same hierarchical sequence, although the reader's individual characteristics and environmental factors may influence the pace at which the stages are passed. The skills learned at each stage are used to develop the next stage; however, a reader does not have to completely master one stage before he or she moves to the next. A reader may not be at the same stage in all reading material, and he or she may revert to an earlier stage when faced with an unknown word or difficult passage.

Chall's model of reading development emphasizes mastery of Stages 1 and 2 (decoding and fluency) before a reader can successfully deal with the comprehension demands of later stages. Chall (1983) noted that children and adults with severe reading difficulties generally are having difficulties at Stage 1 or 2. Those who have difficulty at Stage 1 usually also have difficulty at Stage 2. It may be years before these readers are fluent even with easy material. Snider and Tarver (1987) have applied Chall's model to the area of learning disabilities, discussing in particular the circular problem poor readers may have of not being fluent enough to learn efficiently from reading (Stage 3) and, thus, not having the opportunity to gain the knowledge from reading that is needed for advancing comprehension.

This model is similar in its hierarchical progression to LaBerge and Samuels' application of the theory of automatic information processing to the reading process. In both models, the reader begins with letter recognition, proceeds to decoding, gains fluency, and develops comprehension skills. LaBerge and Samuels' model, however, contains more detailed explanations about the internal processes of reading.

Information Processing and Reading

Information-processing theory was developed to describe how people process task-oriented symbolic information. This theory, modeled after the functioning of the digital computer, proposes a set of processes or mechanisms, used by the thinking person, which are explanatory as well as descriptive (Newell & Simon, 1972). In 1974, LaBerge and Samuels first applied the theory of information processing to reading; they later modified their model on the basis of their own research and advances in cognitive theory (Samuels, 1987; Samuels & Kamil, 1984). In applying this theory to reading, Samuels (1987) continued the computer analogy by proposing that reading difficulties may be due to either "hardware" (physiological) or "software" (learned skills and strategies) deficits. The key software elements in the LaBerge and Samuels model are (a) attention, (b) visual memory, (c) phonological memory, and (d) semantic memory.

Attention is the central element of the LaBerge and Samuels model. All cognitive tasks require some level of attention--some level of effort or energy (Samuels, 1987). The term attention has been used in its overt sense to refer to the observable behavior of students, but here it will be used to refer to the covert mental energy used by a person to process incoming information. According to information processing theorists, attention is a limited capacity. If a person can reduce the amount of attention needed for a task, then more attention is available that can be devoted to a concurrent task (e.g., Bloom, 1986; Schneider & Shiffrin, 1977). LaBerge and Samuels (1974) proposed that once a task becomes automatic, the attentional demands are reduced to a minimum. Thus, the more automatic the processing at earlier levels, the more attentional capacity is left for complex levels of processing.

According to LaBerge and Samuels (1974), the beginning reader must first learn to distinguish and remember visual features of letters. Next, the reader constructs a letter code made up of the distinguishing characteristics. Eventually, codes are constructed for spelling patterns, words, and word groups. A child is able to accurately learn letters with relatively few exposures, but much attentional capacity may be used to do so. The child is not ready to move on to the next stage until letter recognition is automatic.

The next element is phonological memory. This occurs when the visual code connects with the sounds associated with the code. Once the reader can say the word, past experience with spoken communication will facilitate the connection to semantic meaning. The final element addressed in this model is semantic memory. Understanding of written materials is facilitated by the various types of experiential knowledge stored in semantic memory. The goal of fluent reading is automaticity of decoding skills at the visual and phonological memory levels, allowing the reader to focus more attention on the semantic, or meaning, aspects of what is being read.

Mature readers view reading as a holistic process because their subskills are automatic. Poor readers, however, may struggle to learn and reach automaticity on each of the subskills. A reader may process words either by components or holistically. Component processing occurs when a reader uses combinations of any sized units, less than the entire word, to recognize the word. Holistic processing, which is processing an entire word as one unit, reflects automaticity.

The two basic tasks of reading are decoding and comprehension (Samuels & Kamil, 1984). The basic tenet of LaBerge and Samuels' model of information processing is that as the decoding process becomes increasingly automatic (i.e., rapid and accurate), then more attention can be freed for the task of comprehension. The greater mental energy allocated to comprehension tasks presumably facilitates better comprehension--in effect, better reading.

This model generally has been tested through studies of word recognition and vocal latency with subjects asked to identify words of varying lengths (Samuels, LaBerge, & Bremer, 1978) or isolated words versus words in two-word combination contexts (Patberg, Dewitz, & Samuels, 1981). Findings have been consistent with the model's predictions and show that decoding fluency increases with increased age and reading ability (McCormick & Samuels, 1979; Samuels et al., 1978; Stanovich, Cunningham, & West, 1981).

These studies were conducted to lend empirical support to LaBerge and Samuels' model. At the same time, they also support the stages of reading presented by Chall (1983). First and second graders usually are at Chall's second stage of development, in which they should be gaining fluency. Stanovich et al. (1981) demonstrated the development of decoding automaticity in first-grade students, and the studies by McCormick and Samuels (1979) and Patberg et al. (1981) both indicated that good second-grade readers used holistic processing within context. The Patberg et al. study also included fourth graders, the age corresponding with Chall's Stage 3. At this age, as well as at second grade, good readers appeared to use holistic (i.e., more automatic) processing. Holistic processing should enable sufficient fluency within a passage to allow for comprehension, or learning from reading, to take place.


Chall's stages of reading, LaBerge and Samuels' model of automaticity, and CBA are but three pieces in a vast literature on reading and its assessment. Neither Chall nor LaBerge and Samuels considered their ways of looking at reading as being at the level of a formal, general theory. Rather, these are specific models proposed with the intent of generating hypotheses to be tested through research, eventually leading to a better understanding of the complex act of reading (Chall, 1983; Samuels, 1985). Samuels (1985) described a good model as being capable of (a) summarizing previously collected data, (b) helping to understand present events, and (c) helping to develop hypotheses and predictions. Thus, a major role for theory and theoretical models is to provide organizing and simplifying structures for complex data (Calfee & Drum, 1978; Swanson, 1988). Conversely, an important role of applied research is in validating theories developed from basic research (Licht, 1988; Torgesen, 1988).

The remainder of this article examines how the two complementary reading models described previously help in understanding why CBM's simple reading rate measures are proving to be viable measures of reading skill. We also consider how the research that supports CBM might be viewed as supporting developmental reading models, and we propose some possible areas of future research where predictions based on the reading models and tested with CBM may lead to a better understanding of good and poor reading.

Why Reading Rate?

Decoding speed is a key element in both developmental reading models discussed. According to these models, a reader must be able to decode words rapidly and efficiently before being able to fully develop the more complex skills of reading. The reading models predict that better readers are more fluent in word recognition tasks because their basic visual and phonological decoding skills have become more automatic and, thus, more rapid than those of poor readers. CBM reading rates, as measures of rapidity of word recognition, therefore, might be assumed to reflect this automaticity of decoding. The models predict that as decoding becomes more automatic, more attention is available for comprehension; with greater mental resources devoted to it, comprehension improves. This, then, would be the basis for the positive correlation between comprehension skills and decoding fluency found by Deno, Mirkin, and Chiang (1982) and Fuchs, Fuchs, and Maxwell (1988).

It was not a fluke that oral reading rates became the measure of choice in the CBM system and have held up as valid measures of reading over the past 10 years. These measures were the result of solid research techniques, which included attention to the existing literature. The surprise of the researchers that their rate measures correlated so highly with comprehension measures (Deno, 1985) is consistent with the view widely held at that time that comprehension is independent of reading rate, especially oral reading rate. Although some literature in the 1970s discussed the importance of processing speed in overall reading skill available (e.g., Gibson, 1985; LaBerge & Samuels, 1974; Perfetti & Hogaboam, 1975), much of the literature on the role of decoding fluency in comprehension has appeared since the initial development of the CBM rate measures in 1978-1979 (e.g., Chall, 1983; McCormick & Samuels, 1979; Patberg et al., 1981; Perfetti & Lesgold, 1979; Samuels, 1981, 1987; Samuels, Miller, & Eisenberg, 1979; Snider & Tarver, 1987).

In 1978, Calfee and Drum pointed out that speed and fluency measures were at one time considered important measures of performance by reading teachers. Based on the emerging evidence from information-processing studies, they urged practitioners and researchers to take another look at the possible usefulness of speed and fluency measures. At about the same time, Perfetti and Lesgold (1979) concluded that empirical evidence clearly showed that, "Coding speed and reading achievement are highly related for young readers" (p. 63). More recently, in a national report on basic findings in reading (Anderson, Heibert, Scott, & Wilkinson, 1985), reading aloud unfamiliar materials with acceptable fluency is cited as a more valid means of assessing reading skills than are standardized tests. Oral fluency and speed measures also seem to be receiving more attention in the professional reading literature (e.g., Bowers, Steffy, & Tate, 1988; Walsh, Price, & Gillingham, 1988).

When we did an informal review of recent college texts on theories of reading and reading comprehension, we found that, despite the evidence supporting reading rates, rate rarely is discussed in conjunction with comprehension or assessment. When rate is discussed (e.g., Farr & Carey, 1986), it tends to be in the sense of pointing out that reading rates vary depending on the kind of material read, discussing problems with silent-reading assessment methods, or getting caught up in the problem of assuming that proponents of assessing reading rate are saying that simply increasing speed of reading in any material will cause an increase in comprehension (the old problem of assuming that correlation implies causation).

In a review of research on oral reading, Allington (1984) focused almost entirely on error analysis in the use of oral reading as an assessment tool. In fact, while discussing research on the efficacy of instruction in decoding skills, Allington essentially dismissed the importance of rate when he said, "There is no reason to believe the coding emphasis should elicit any trend other than more accurate and faster reading across time" (p. 847). Yet, in his conclusions, Allington noted that fluency is too important to ignore any more in research. A review of reading assessment practices by Johnston (1984) also did not discuss oral reading rate. Johnston commented, however, that "statistical evaluation procedures have not generally been applied to oral reading techniques" (p. 153). No wonder teachers are skeptical when told that the CBM reading rate measures are highly related to comprehension--that is not what they have learned in their theory and methods classes.

The correlation between oral reading rate and comprehension does not mean that one simply causes the other, especially with a task as complex as reading. However, if the theoretical models are accurate and automaticity of decoding is a direct correlate of comprehension, oral reading rates may, in fact, be a more theoretically sound method of assessing overall reading skill, at least at some stages of reading development, than are many traditional methods.

The use of reading rates to assess reading skill represents a general skill assessment, not a diagnostic assessment of specific skill deficits. Such an assessment in analogous to a physician's taking a patient's temperature and blood pressure to get a general picture of the patient's health, whereas blood tests and x-rays provide more specific diagnostic information. This relationship of oral reading rate to general reading "health" is evident in the finding of Fuchs, Fuchs, and Maxwell (1988) that oral reading rate correlated more highly with a measure of general reading comprehension than with a measure of specific word-attack skills. Thus, it seems that the applied decoding fluency reflected in these rate measures represents something more holistic than simply knowledge of specific decoding rules.

CBM Support for Reading Models

CBM and the reading models described here have been developing independently over the past decade. We have argued that the reading models provide a theoretical explanation for the validity of CBM's reading rate measures. At the same time, the apparent applied validity of these measures supports the validity of the reading models themselves. There is evidence from CBM studies that reading rate scores increase with increased age and grade level (Deno, Mirkin, & Chiang, 1982), that good readers read faster than poor readers and handicapped readers read the slowest of all (Marston & Magnusson, 1985; Shinn, Ysseldyke, Deno, & Tindal, 1986), and that reading rates for individual students increase as their reading skill increases (Fuchs, Deno, & Mirkin, 1984; Germann & Tindal, 1985; Marston & Magnusson, 1985). All these studies have provided solid, field-based evidence for the theoretical relationship between reading rate and reading skill.

Construct Validity

Reading comprehension is a theoretical construct. It is something that only can be inferred through observable behavior. Theories and models of reading have been constructed; to the extent that behavior such as performance on a measure of "comprehension" fits with the models or theories, it is assumed that the construct of comprehension is, in fact, being measured and that the model or theory is tenable. This is the essence of the concept of construct validity. Not only does the test need to be consistent with the predictions of the construct theory, but the theory needs to be consistent with the behavior observed on the test.

We do not first "prove" the theory, and then validate the test, nor conversely. In any probable inductive type of inference from a pattern of observations, we examine the relation between the total network of theory and observations. The system involves propositions relating test to construct, construct to other constructs, and finally relating some of these constructs to observables. In ongoing research the chain of inference is very complicated.... If the prediction is not confirmed, any link in the chain may be wrong. (Cronbach & Meehl, 1955, p. 294)

The connections between CBM and developmental reading models cited in this article appear to add to this interlocking system of construct hypotheses and observations-- what Cronbach and Meehl referred to as the "nomological network." But, as they pointed out, theories can never be proven, only disproven. Negative evidence can reflect errors in any part of the system and should lead to modifications in the system; supporting evidence just adds to the confirmatory body of data. The evidence presented in this article appears to be confirmatory, but much more empirical evidence is needed before the connections can be considered as more than tentative, and it is probable that modifications will need to be made along the way.

The Opportunities

The opportunities to tie together the basic research of model building with the applied research of CBM are intriguing. Massaro (1984) pointed out that the research relating to model building generally examines specific components of the reading process rather than normal reading. Up to this point, studies of automaticity usually have been done in lab-like settings using special equipment to assess how long it takes a person to respond vocally or to press a button after a word is seen (latency measures) (e.g., McCormick & Samuels, 1979; Patberg et al., 1981; Samuels et al., 1979; Santa, Santa, & Smith, 1977). It seems that the number of words read within a specified time period may be an analogous measure of processing speed. After all, "speed is simply the reciprocal of latency" (Gough, 1984, p. 228).

Although the oral reading rate time samples may be somewhat rougher measures than the laboratory latency measures, seemingly they would be more useful with large groups of subjects, and reading rate samples would provide a measure, with proven technical adequacy, more like the subjects' everyday reading experiences. With the districtwide norming recommended for some CBM decision roles (Shinn, 1988), there already is a massive pool of reading rate data on regular as well as special education students in some school districts. Although these data have been collected primarily for special education decision-making purposes, they could provide a wealth of information about normal readers, as well.

The research advantages go both ways. Whereas CBM may prove a useful tool in testing reading models, these models can provide predictive hypotheses to be tested that lead to even more accurate uses of CBM, especially with students of different developmental skill levels. Moving beyond examinations of content validity, criterion validity, and predictive validity into the realm of construct validity adds immeasurably to the power and possibilities of CBM.

Future data collection procedures should be developed cooperatively by basic and applied researchers and by those interested in normal learning and those interested in exceptional learning. Questions that need to be addressed further and some basic sources relating to these questions include: Are rate measures equally valid at all ages? (Chall, 1983); Can progress through Chall's developmental reading stages be monitored through CBM's repeated measures procedures? (Chall, 1983; Deno, 1986; Germann & Tindal, 1985); What is the impact of phonological versus visual processing on rate? (Stanovich, 1988); What is the role of accuracy? (Deno, 1985; Fuchs, Fuchs & Maxwell, 1988; LaBerge & Samuels, 1974); What is the relationship between ability to read isolated words and ability to read curriculum passages for readers of varying abilities and at various grade levels--is either a better measure of a disabled reader's skills? (Deno, Mirkin, & Chiang, 1982; McCormick & Samuels, 1979); Is the relationship between reading rate and comprehension the same for disabled readers as it is for normal readers? (LaBerge & Samuels, 1974; Lovett, 1987; Snider & Tarver, 1987); Are there minimum reading rates for certain levels of comprehension? Are these minimums universal or do they vary by individual? (Deno & Mirkin, 1977; Fuchs, Fuchs & Deno, 1982; Shapiro & Lentz, 1985); What is the impact on rate of type of curriculum over time? (Calfee & Drum, 1978; Good & Salvia, 1988).

Kamil (1984) stated: "It is rare that researchers from either end of the basic-applied continuum interact to generate mutually interesting research problems" (p.44). Both the information-processing and the CBM researchers, in fact, have expanded their research beyond its initial focus with careful attention paid to doing quality research that can lead to practical applications. Yet, as with many lines of research, most studies in both areas are conducted by a relatively small core of investigators who focus on questions they find of particular interest. If the research avenues proposed here were taken up by investigators from a variety of domains of interest (e.g., curriculum and instruction, learning disabilities, developmental disabilities, gifted education, reading development, perception, cognition), the richness of differences in perspective could contribute significantly to our overall understanding of the processes involved in good and poor reading.


Theories serve an explanatory function, but theories rely on the support of empirical data to remain viable. Applied research findings do not necessarily need to fit a theoretical framework to be useful, but such a framework can provide valuable guidance in refining that empirical knowledge. The theoretical models of automaticity and stages of reading development and CBM as an empirically derived assessment tool provide potentially fruitful ground where theory can inform practice and practice can inform theory.

The reading rate measures of CBM seem to be consistent with developmental models of reading on the basis of the separate literatures available in these areas of research. What is needed now are studies specifically designed to test the connections between the reading models and CBM.


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MARGARET L. POTTER (CEC Chapter #228) is an Assistant Professor and the Director of the School Psychology Training Program at Moorhead State University, Moorhead, Minnesota. HEIDI M. WAMRE is a School Psychologist in the Heartland Area Education Agency, Newton, Iowa.

We would like to thank our colleagues at Moorhead State University and the manuscript reviewers for their comments and suggestions.
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Author:Potter, Margaret L.; Wamre, Heidi M.
Publication:Exceptional Children
Date:Sep 1, 1990
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