Staffing a Fast Food Restaurant Case Study Presentation attached are the requirement for case 3. STAFFING A FAST FOOD RESTAURANTRequirements:Build a workab

Staffing a Fast Food Restaurant Case Study Presentation attached are the requirement for case 3. STAFFING A FAST FOOD RESTAURANTRequirements:Build a workable schedule in Excel showing how many managers and employees are needed each hour from Sunday through Saturday for the counter/cashier or kitchen, setup, and cleanup, and justify your conclusions quantitatively (i.e., schedule based at least partly on forecasting formulas). You may exceed scheduled hours on a given day, but not by more than one hour. For scheduling purposes, the work week starts on Monday, and ends on Sunday. Show each day’s work schedule on a PowerPoint slide (there will be 7 slides for schedules).Prepare a table showing how many hours each employee (not including managers) will be working for the upcoming week. Place this table on a PowerPoint slide. ANNE E. CUNNINGHAM and KEITH E. STANOVICH
What Reading
Does for the Mind
Anne E. Cunningham is visiting associate professor in cognition
and development in the graduate school of education at the
University of California, Berkeley. Her research examines the
cognitive and motivational processes that underlie reading ability
and the cognitive consequences of reading skill and engagement.
Keith E. Stanovich is professor of applied psychology at the
University of Toronto/Ontario Institute for Studies in Education.
His recent awards include the Sylvia Scribner Award from the
American Educational Research Association and the Oscar S.
Causey Award from the National Research Conference for his
distinguished and substantial contributions to literacy research.
This research was supported by a Spencer Foundation Small
Grant to Anne E. Cunningham and grant No. 410-95-0315 from
the Social Sciences and Humanities Research Council of Canada
to Keith E. Stanovich.
Reading has cognitive consequences that extend
beyond its immediate task of lifting meaning
from a particular passage. Furthermore, these
consequences are reciprocal and exponential in
nature. Accumulated over time—spiraling either
upward or downward—they carry profound
implications for the development of a wide
range of cognitive capabilities.
Concern about the reciprocal influences of
reading achievement has been elucidated
through discussions of so-called “Matthew
effects” in academic achievement (Stanovich,
1986; Walberg & Tsai, 1983). The term
“Matthew effects” is taken from the Biblical
passage that describes a rich-get-richer and
poor-get-poorer phenomenon. Applying this
Journal of Direct Instruction
concept to reading, we see that very early in
the reading process poor readers, who experience greater difficulty in breaking the spellingto-sound code, begin to be exposed to much
less text than their more skilled peers
(Allington, 1984; Biemiller, 1977–1978).
Further exacerbating the problem is the fact
that less-skilled readers often find themselves
in materials that are too difficult for them
(Allington, 1977, 1983, 1984; Gambrell,
Wilson, & Gantt, 1981). The combination of
deficient decoding skills, lack of practice, and
difficult materials results in unrewarding early
reading experiences that lead to less involvement in reading-related activities. Lack of
exposure and practice on the part of the lessskilled reader delays the development of automaticity and speed at the word recognition
level. Slow, capacity-draining word recognition
processes require cognitive resources that
should be allocated to comprehension. Thus,
reading for meaning is hindered; unrewarding
reading experiences multiply; and practice is
avoided or merely tolerated without real cognitive involvement.
The disparity in the reading experiences of children of varying skill may have many other consequences for their future reading and cognitive
development. As skill develops and word recog-
Journal of Direct Instruction, Vol. 1, No. 2, pp. 137–149.
Reprinted with permission from The American Federation
of Teachers. American Educator, Vol. 22, No. 1–2, pp. 8–15.
Address correspondence to Anne E. Cunningham and Keith
E. Stanovich at acunning@socretes.berkeley.edu.
137
nition becomes less resource demanding and
more automatic, more general language skills,
such as vocabulary, background knowledge,
familiarity with complex syntactic structures,
etc., become the limiting factor on reading ability (Chall, 1983; Sticht, 1979). But the sheer
volume of reading done by the better reader has
the potential to provide an advantage even here
if—as our research suggests—reading a lot
serves to develop these very skills and knowledge bases (Cunningham & Stanovich, 1997;
Echols, West, Stanovich, & Zehr, 1996;
Stanovich & Cunningham, 1992, 1993). From
the standpoint of a reciprocal model of reading
development, this means that many cognitive
differences observed between readers of differing skill may in fact be consequences of differential practice that itself resulted from early differences in the speed of initial reading acquisition.
The increased reading experiences of children
who master the spelling-to-sound code early
thus might have important positive feedback
effects that are denied the slowly progressing
reader. In our research, we have begun to
explore these reciprocal effects by examining
the role that reading volume plays in shaping
the mind and will share many of our findings in
this article.
We should say at the outset that the complexity
of some of the work we will describe in this article was necessitated in large part by the fact that
it is difficult to tease apart the unique contribution that reading volume affords. One of the difficulties is that levels of reading volume are correlated with many other cognitive and behavioral
characteristics. Avid readers tend to be different
from nonreaders on a wide variety of cognitive
skills, behavioral habits, and background variables (Guthrie, Schafer, & Hutchinson, 1991;
Kaestle, 1991; Zill & Winglee, 1990). Attributing
any particular outcome to reading volume is thus
extremely difficult.
138
Theoretical Reasons
to Expect Positive
Cognitive Consequences
from Reading Volume
In certain very important cognitive domains,
there are strong theoretical reasons to expect a
positive and unique effect of avid reading.
Vocabulary development provides a case in
point. Most theorists are agreed that the bulk of
vocabulary growth during a child’s lifetime
occurs indirectly through language exposure
rather than through direct teaching (Miller &
Gildea, 1987; Nagy & Anderson, 1984; Nagy,
Herman, & Anderson, 1985; Sternberg, 1985,
1987). Furthermore, many researchers are convinced that reading volume, rather than oral language, is the prime contributor to individual differences in children’s vocabularies (Hayes, 1988;
Hayes & Ahrens, 1988; Nagy & Anderson, 1984;
Nagy & Herman, 1987; Stanovich, 1986).
The theoretical reasons for believing that reading volume is a particularly effective way of
expanding a child’s vocabulary derive from the
differences in the statistical distributions of
words that have been found between print and
oral language. Some of these differences are
illustrated in Table 1, which displays the results
of some of the research of Hayes and Ahrens
(1988), who have analyzed the distributions of
words used in various contexts.
The table illustrates the three different categories of language that were analyzed: written
language sampled from genres as difficult as scientific articles and as simple as preschool books;
words spoken on television shows of various
types; and adult speech in two contexts varying
in formality. The words used in the different
contexts were analyzed according to a standard
frequency count of English (Carroll, Davies, &
Richman, 1971). This frequency count ranks
Summer 2001
the 86,741 different word forms in English
according to their frequency of occurrence in a
large corpus of written English. So, for example,
the word “the” is ranked number 1, the 10th
most frequent word is “it,” the word “know” is
ranked 100, the word “pass” is ranked 1,000,
the word “vibrate” is 5,000th in frequency, the
word “shrimp” is 9,000th in frequency, and the
word “amplifier” is 16,000th in frequency. The
first column, labeled Rank of Median Word, is
simply the frequency rank of the average word
(after a small correction) in each of the categories. So, for example, the average word in children’s books was ranked 627th most frequent in
the Carroll et al. word count; the average word
in popular magazines was ranked 1,399th most
frequent; and the average word in the abstracts
of scientific articles had, not surprisingly, a very
low rank (4,389).
What is immediately apparent is how lexically
impoverished is most speech, as compared to
written language. With the exception of the
special situation of courtroom testimony, average frequency of the words in all the samples of
oral speech is quite low, hovering in the
400–600 range of ranks.
The relative rarity of the words in children’s
books is, in fact, greater than that in all of the
adult conversation, except for the courtroom
testimony. Indeed, the words used in children’s
books are considerably rarer than those in the
speech on prime-time adult television. The categories of adult reading matter contain words
that are two or three times rarer than those
heard on television.
These relative differences in word rarity have
direct implications for vocabulary development.
If most vocabulary is acquired outside of formal
teaching, then the only opportunities to acquire
new words occur when an individual is exposed
to a word in written or oral language that is outside his/her current vocabulary. That this will
happen vastly more often while reading than
Journal of Direct Instruction
while talking or watching television is illustrated in the second column of Table 1. The column lists how many rare words per 1000 are
contained in each of the categories. A rare word
is defined as one with a rank lower than 10,000;
roughly a word that is outside the vocabulary of
a fourth to sixth grader. For vocabulary growth
to occur after the middle grades, children must
be exposed to words that are rare by this definition. Again, it is print that provides many more
such word-learning opportunities. Children’s
Table 1
Selected Statistics for Major Sources
of Spoken and Written Language
(Sample Means)
Rank Rare
of
Words
Median per
Word l000
I. Printed texts
Abstracts of scientific articles
Newspapers
Popular magazines
Adult books
Comic books
Children’s books
Preschool books
II. Television texts
Popular prime-time adult shows
Popular prime-time children’s
shows
Cartoon shows
Mr. Rogers and Sesame Street
III. Adult speech
Expert witness testimony
College graduates to friends,
spouses
4389
1690
1399
1058
867
627
578
128.0
68.3
65.7
52.7
53.5
30.9
16.3
490
543
22.7
20.2
598
413
30.8
2.0
1008
496
28.4
17.3
Adapted from Hayes and Ahrens (1988).
139
books have 50 percent more rare words in them
than does adult prime-time television and the
conversation of college graduates. Popular magazines have roughly three times as many opportunities for new word learning as does prime time
television and adult conversation. Assurances by
some educators that “What they read and write
may make people smarter, but so will any activity that engages the mind, including interesting
conversation” (Smith, 1989) are overstated, at
least when applied to the domain of vocabulary
learning. The data in Table 1 indicate that conversation is not a substitute for reading.
It is sometimes argued or implied that the type
of words present in print but not represented in
speech are unnecessary words—jargon, academic doublespeak, elitist terms of social advantage,
or words used to maintain the status of the
users but that serve no real functional purpose.
A consideration of the frequency distributions
Table 2
Examples of words that do not appear
in two large corpora of oral language
(Berger, 1977; Brown, 1984) but that
have appreciable frequencies in written
texts (Carroll, Davies & Richman,
1971; Francis & Kucera, 1982):
display
dominance
dominant
exposure
equate
equation
gravity
hormone
infinite
invariably
140
literal
legitimate
luxury
maneuver
participation
portray
provoke
relinquish
reluctantly
of written and spoken words reveals this argument to be patently false. Table 2 presents a list
of words that do not occur at all in two large corpora of oral language (Berger, 1977; Brown,
1984), but that have appreciable frequencies in
a written frequency count (Francis & Kucera,
1982). The words participation, luxury, maneuver,
provoke, reluctantly, relinquish, portray, equate, hormone, exposure, display, invariably, dominance, literal,
legitimate, and infinite are not unnecessary appendages, concocted to exclude those who are unfamiliar with them. They are words that are necessary to make critical distinctions in the physical
and social world in which we live. Without such
lexical tools, one will be severely disadvantaged
in attaining one’s goals in an advanced society
such as ours. As Olson (1986) notes:
It is easy to show that sensitivity to the
subtleties of language are crucial to some
undertakings. A person who does not
clearly see the difference between an
expression of intention and a promise or
between a mistake and an accident, or
between a falsehood and a lie, should
avoid a legal career or, for that matter, a
theological one.
The large differences in lexical richness
between speech and print are a major source of
individual differences in vocabulary development. These differences are created by the
large variability among children in exposure to
literacy. Table 3 presents the data from a study
of the out-of-school time use by fifth graders
conducted by Anderson, Wilson, and Fielding
(1988). From diaries that the children filled out
daily over several months’ time, the investigators estimated how many minutes per day that
individuals were engaged in reading and other
activities while not in school. The table indicates that the child at the 50th percentile in
amount of independent reading was reading
approximately 4.6 minutes per day, or about a
half an hour per week, over six times as much as
the child at the 20th percentile in amount of
Summer 2001
reading time (less than a minute daily). Or, to
take another example, the child at the 80th percentile in amount of independent reading time
(14.2 minutes) was reading over twenty times
as much as the child at the 20th percentile.
Anderson et al. (1988) estimated the children’s
reading rates and used these, in conjunction
with the amount of reading in minutes per day,
to extrapolate a figure for the number of words
that the children at various percentiles were
reading. These figures, presented in the far
right of the table, illustrate the enormous differences in word exposure that are generated by
children’s differential proclivities toward reading. For example, the average child at the 90th
percentile reads almost two million words per
year outside of school, more than 200 times
more words than the child at the 10th percentile, who reads just 8,000 words outside of
school during a year. To put it another way, the
entire year’s out-of-school reading for the child
at the 10th percentile amounts to just two days
reading for the child at the 90th percentile!
These dramatic differences, combined with the
lexical richness of print, act to create large
vocabulary differences among children.
Examining the Consequences
of Differential Degrees
of Reading Volume
It is one thing to speculate on how these differences in reading volume may result in specific
cognitive consequences in domains like vocabulary; it is another to demonstrate that these
effects are occurring. In our research, we have
sought empirical evidence for the specific
effects of reading volume, effects that do not
simply result from the higher cognitive abilities
and skills of the more avid reader. Although
there are considerable differences in amount of
reading volume in school, it is likely that differences in out-of-school reading volume are an
Journal of Direct Instruction
even more potent source of the rich-get-richer
and poor-get-poorer achievement patterns.
Therefore, we have sought to examine the
unique contribution that independent or out-ofschool reading makes toward reading ability,
aspects of verbal intelligence, and general
knowledge about the world. As part of this
research program, our research group has pioneered the use of a measure of reading volume
that has some unique advantages in investigations of this kind (Cunningham and Stanovich,
1990; Stanovich and West, 1989).
In all, we developed two measures of adults’
reading volume and one for children’s reading
volume. Briefly, the children’s measure, named
the Title Recognition Test (TRT), requires
children to pick out the titles of popular children’s books from a list of titles that includes
Table 3
Variation in Amount
of Independent Reading
Words Read
Per Year
%
Independent
Reading
Minutes Per Day
98
90
80
70
60
50
40
30
20
10
2
65.0
21.1
14.2
9.6
6.5
4.6
3.2
1.3
0.7
0.1
0.0
4,358,000
1,823,000
1,146,000
622,000
432,000
282,000
200,000
106,000
21,000
8,000
0
Adapted from Anderson, Wilson, and Fielding
(1988).
141
equal numbers of made-up titles. This task is
easy to administer to large numbers of children,
it does not make large cognitive demands, and
its results are reliable—it is not possible for
children to distort their responses toward what
they perceive as socially desirable answers.
Because the number of wrong answers can be
counted against correct ones, it is possible to
remove the effects of guessing from the results
(see Cunningham & Stanovich, 1990; 1991; and
Stanovich and West, 1989 for a full description
of these instruments and a discussion of the
logic behind them). The adults’ measures,
named the Author Recognition and Magazine
Recognition Test, have the same task requirements and are described fully in Stanovich and
West (1989).
hierarchical multiple regression to solve the interpretive problem that avid readers excel in most
domains of verbal learning and that, therefore,
our measures of reading volume might be spuriously correlated to a host of abilities
(Cunningham & Stanovich, 1990, 1991;
Stanovich & Cunningham, 1992, 1993;
Stanovich & West, 1989). We have found that
even when performance is statistically equated
for reading comprehension and general ability,
reading volume is still a very powerful predictor
of vocabulary and knowledge differences. Thus,
we believe that reading volume is not simply an
indirect indicator of ability; it is actually a
potentially separable, independent source of
cognitive differences.
A score on the Title Recognition Test, of course,
is not an absolute measure of children’s reading
volume and previous literacy experiences, but it
does provide us with an index of the relative differences in reading volume. This index enables
us to ask what effects reading volume (rather
than general reading comprehension and word
decoding ability) has on intelligence, vocabulary,
spelling, and children’s general knowledge. In
short, it enables us to ask the question, does
reading—in and of itself—shape the quality of
our mind?
Reading Volume
as a Contributor
to Growth in Verbal Skills
The titles appearing on the TRT were selected
from a sample of book titles generated in pilot
investigations by groups of children ranging in
age from second grade through high school. In
selecting the items that appear on any one version of the TRT, an attempt was made to
choose titles that were not prominent parts of
classroom reading activities in these particular
schools. Because we wanted the TRT to probe
out-of-school rather than school-directed reading, an attempt was made to choose titles that
were not used in the school curriculum.
In our technical reports on this work, we have
used a powerful statistical technique known as
142
In several studies, we have attempted to link
children’s reading volume to specific cognitive
outcomes after controlling for relevant general
abilities such as IQ. In a study of fourth-, fifth-,
and sixth-grade children, we examined whether
reading volume accounts for differences in
vocabulary development once controls for both
general intelligence and specific verbal abilities
were invoked (Cunningham & Stanovich,
1991). We employed multiple measures of
vocabulary and controlled for the effects of age
and intelligence. We also controlled for the
effect of another ability that may be more closely linked to vocabulary acquisition mechanisms:
decoding ability. Decoding skill might mediate a
relationship between reading volume and a variable like vocabulary size in numerous ways.
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