Data from the U.S. 1992 Census of Agriculture

Description

Data from the U.S. 1992 Census of Agriculture

Usage

agpop

Format

Data frame with the following 15 variables:

county

county name

state

state abbreviation

acres92

number of acres devoted to farms, 1992

acres87

number of acres devoted to farms, 1987

acres82

number of acres devoted to farms, 1982

farms92

number of farms, 1992

farms87

number of farms, 1987

farms82

number of farms, 1982

largef92

number of farms with 1000 acres or more, 1992

largef87

number of farms with 1000 acres or more, 1987

largef82

number of farms with 1000 acres or more, 1982

smallf92

number of farms with 9 acres or fewer, 1992

smallf87

number of farms with 9 acres or fewer, 1987

smallf82

number of farms with 9 acres or fewer, 1982

region

factor with levels S (south), W (west), NC (north central), NE (northeast)

Source

U.S. 1992 Census of Agriculture

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 437.

Data from a SRS of size 300 from the U.S. 1992 Census of Agriculture

Description

Data from a SRS of size 300 from the U.S. 1992 Census of Agriculture

Usage

agsrs

Format

Data frame with the following 14 variables:

county

county name

state

state abbreviation

acres92

number of acres devoted to farms, 1992

acres87

number of acres devoted to farms, 1987

acres82

number of acres devoted to farms, 1982

farms92

number of farms, 1992

farms87

number of farms, 1987

farms82

number of farms, 1982

largef92

number of farms with 1000 acres or more, 1992

largef87

number of farms with 1000 acres or more, 1987

largef82

number of farms with 1000 acres or more, 1982

smallf92

number of farms with 9 acres or fewer, 1992

smallf87

number of farms with 9 acres or fewer, 1987

smallf82

number of farms with 9 acres or fewer, 1982

Source

U.S. 1992 Census of Agriculture

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 437.

Data from a stratified random sample of size 300 from the U.S. 1992 Census of Agriculture.

Description

Data from a stratified random sample of size 300 from the U.S. 1992 Census of Agriculture.

Usage

agstrat

Format

Data frame with the following 17 variables:

county

county name

state

state abbreviation

acres92

number of acres devoted to farms, 1992

acres87

number of acres devoted to farms, 1987

acres82

number of acres devoted to farms, 1982

farms92

number of farms, 1992

farms87

number of farms, 1987

farms82

number of farms, 1982

largef92

number of farms with 1000 acres or more, 1992

largef87

number of farms with 1000 acres or more, 1987

largef82

number of farms with 1000 acres or more, 1982

smallf92

number of farms with 9 acres or fewer, 1992

smallf87

number of farms with 9 acres or fewer, 1987

smallf82

number of farms with 9 acres or fewer, 1982

region

factor with levels S (south), W (west), NC (north central), NE (northeast)

rn

random numbers used to select sample in each stratum

weight

sampling weighs for each county in sample

Source

U.S. 1992 Census of Agriculture

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 437.

Length of Left Middle Finger and Height for 3000 Criminals

Description

Length of left middle finger and height for 3000 criminals

Usage

anthrop

Format

Data frame with the following 2 variables:

finger

length of left middle finger (cm)

height

height (inches)

Source

Macdonell, W. R. (1901). On criminal anthropometry and the identification of criminals, Biometrika, 1: 177–227.

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 438.

Length of Left Middle Finger and Height for an SRS of Size 200

Description

Length of left middle finger and height for an SRS of 200 criminals from the anthrop dataset

Usage

anthsrs

Format

Data frame with the following 2 variables:

finger

length of left middle finger (cm)

height

height (inches)

Source

Macdonell, W. R. (1901). On criminal anthropometry and the identification of criminals, Biometrika, 1: 177–227.

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 438.

Length of Left Middle Finger and Height for an Unequal-Probability Sample of Size 200

Description

Length of left middle finger and height for an unequal-probability sample of criminals of size 200 from the anthrop dataset. The probability of selection, psi[i], was proportional to 24 for y < 65, 12 for y = 65, 2 for y = 66 or 67, and 1 for y > 67.

Usage

anthuneq

Format

Data frame with the following 3 variables:

finger

length of left middle finger (cm)

height

height (inches)

prob

probability of selection

Source

Macdonell, W. R. (1901). On criminal anthropometry and the identification of criminals, Biometrika, 1: 177–227.

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 438.

Selection of Accounts for Audit in Example 6.11

Description

Selection of Accounts for Audit in Example 6.11

Usage

audit

Format

Data frame with the following 6 variables:

account

audit unit

bookval

book value of account

cumbv

cumulative book value

rn1

random number 1 selecting account

rn2

random number 2 selecting account

rn3

random number 3 selecting account

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 439.

Data from Home Owner's Survey on Total Number of Books

Description

Data from home owner's survey on total number of books

Usage

books

Format

Data frame with the following 6 variables:

shelf

shelf number

number

number of the book selected

purchase

purchase cost of the book

replace

replacement cost of book

Note

Used in Exercise 6 of Chapter 5.

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 439.

Data from the 1994 Survey of ASA Membership on Certification

Description

Data from the 1994 Survey of ASA Membership on Certification

Usage

certify

Format

Data frame with the following 11 variables:

certify

should the ASA develop some form of certification? factor with levels yes, possibly, noopinion, unlikely and no

approve

would you approve of a certification program similar to that described in the July 1993 issue of Amstat News? factor with levels yes, possibly, noopinion, unlikely and no

speccert

Should there be specific certification programs for statistics subdisciplines? factor with levels yes, possibly, noopinion, unlikely and no

wouldyou

If the ASA developed a certification program, would you attempt to become certified? factor with levels yes, possibly, noopinion, unlikely and no

recert

If the ASA offered certification, should recertification be required every several years? factor with levels yes, possibly, noopinion, unlikely and no

subdisc

Major subdiscipline; factor with levels BA (Bayesian), BE (business and economic), BI (biometrics), BP (biopharmaceutical), CM (computing), EN (environment), EP (epidemiology), GV (government), MR (marketing), PE (physical and engineering), QP (quality and productivity), SE (statistical education), SG (statistical graphics), SP (sports), SR (survey research), SS (social statistics), TH (teaching statistics in health sciences), O (other)

college

Highest collegiate degree; factor with levels B (BS or BA), M (MS), N (none), P (PhD) and O (other)

employ

Employment status; factor with levels E (employed), I (in school), R (retired), S (self-employed), U (unemployed) and O (other)

workenv

Primary work environment; factor with levels A (academia), G (government), I (industry), O (other)

workact

Primary work activity; factor with levels C (consultant), E (educator), P (practitioner), R (researcher), S (student) and O (other)

yearsmem

For how many years have you been a member of ASA?

Note

The full dataset is on Statlib

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 439. http://lib.stat.cmu.edu/asacert/certsurvey

Egg Size from Coots

Description

Selected information on egg size from coots, from a study by Arnold (1991). Data courtesy of Todd Arnold.

Usage

coots

Format

Data frame with the following 11 variables:

clutch

clutch number from which eggs were subsampled

csize

number of eggs in clutch (Mi)

length

length of egg (mm)

breadth

maximum breadth of egg (mm)

volume

calculated as 0.00507 x length x breadth^2

tmt

received supplemental feeding? factor with levels no and yes

Note

Not all observations are used for this data set, so results may not agree with those in Arnold (1991)

Source

Arnold, T.W. (1991). Intraclutch variation in egg size of American Coots, The Condor, 93: 19–27

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 440.

Data from an SRS of 100 of the 3141 Counties in the U.S.

Description

Data from an SRS of 100 of the 3141 Counties in the U.S.

Usage

counties

Format

Data frame with the following 18 variables:

RN

random number used to select the country

state

state (two-letter abbreviation)

county

county

landarea

land area, 1990 (square miles)

totpop

total population, 1992

physician

active nonfederal physicians on Jan. 1, 1990

enroll

school enrollment in elementary or high school, 1990

percpub

percent of school enrollment in public schools

civlabor

civilian labor force, 1991

unemp

number unemployed, 1991

farmpop

farm population, 1990

numfarm

number of farms, 1987

farmacre

acreage in farms, 1987

fedgrant

total expenditures in federal funds and grants, 1992 (millions of dollars)

fedciv

civilians employed by federal government, 1990

milit

military personnel, 1990

veterans

number of veterans, 1990

percviet

percentage of veterans from Vietnam era, 1990

Source

U.S. Bureau of Census, 1994

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 440.

Data from a Sample of Divorce Records

Description

Data from a sample of divorce records for states in the Divorce Registration Area (National Center for Health Statistics 1987)

Usage

divorce

Format

Data frame with the following 20 variables:

state

state name

abbrev

state abbreviation

samprate

sampling rate for state

numrecs

number of records sampled in state

hsblt20

number of records in sample with husband's age < 20

hsb2024

number of records with 20 <= husband's age <= 24

hsb2529

number of records with 25 <= husband's age <= 29

hsb3034

number of records with 30 <= husband's age <= 34

hsb3539

number of records with 35 <= husband's age <= 39

hsb4044

number of records with 40 <= husband's age <= 44

hsb4549

number of records with 45 <= husband's age <= 49

hsbge50

number of records with wife's age >= 50

wflt20

number of records in sample with wife's age < 20

wf2024

number of records with 20 <= wife's age <= 24

wf2529

number of records with 25 <= wife's age <= 29

wf3034

number of records with 30 <= wife's age <= 34

wf3539

number of records with 35 <= wife's age <= 39

wf4044

number of records with 40 <= wife's age <= 44

wf4549

number of records with 45 <= wife's age <= 49

wfge50

number of records with wife's age >= 50

Source

National Center of Health Statistics (1987). TODO

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 440.

Simple Random Sample of Golf Courses

Description

Simple Random Sample (SRS) of 120 golf courses taken from the population of the (now defunct) Website www.golfcourse.com

Usage

golfsrs

Format

Data frame with the following 16 variables:

RN

random number used to select golf course for sample

state

state name

holes

number of holes

type

type of course; factor with levels priv (private), semi (semi-private), pub (public), mili (military) and res (resort)

yearblt

year the course was built

wkday18

greens fee for 18 holes during week

wkday9

greens fee for 9 holes during week

wkend18

greens fee for 18 holes on weekend

wkend9

greens fee for 9 holes on weekend

backtee

back-tee yardage

rating

course rating

par

par for course

cart18

golf cart rental fee for 18 holes

cart9

golf cart rental fee for 9 holes

caddy

Are caddies available? factor with levels yes and no

pro

Is a golf pro available? factor with levels yes and no

Source

The now defunct website golfcourse.com (https://web.archive.org/web/19991108203827/http://golfcourse.com/)

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and TODO.

Height and gender of 2000 persons in an artificial population

Description

Height and gender of 2000 persons in an artificial population

Usage

htpop

Format

height

height of person, cm

gender

factor with levels F (female) and M (male)

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. 230–234 and 441.

Height and gender for an SRS of 200 persons, taken from htpop

Description

Height and gender for an SRS of 200 persons, taken from htpop

Usage

htsrs

Format

rn

random number used to select the unit

height

height of person, cm

gender

factor with levels F (female) and M (male)

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. 230–234 and 442.

Height and gender for a stratified random sample from htpop

Description

Height and gender for a stratified random sample of 160 women and 40 men taken from the htpop population

Usage

htstrat

Format

rn

random number used to select the unit

height

height of person, cm

gender

factor with levels F (female) and M (male)

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. 230–234 and 442.

Types of Sampling Used for Articles in a Sample of Journals

Description

Types of Sampling Used for Articles in a Sample of Journals

Usage

journal

Format

Data frame with the following 3 variables:

numemp

number of articles in 1988 that used sampling

prob

number of articlues that used probability sampling

nonprob

number of articles that used nonprobability sampling

Source

Jacoby and Handlin (1991). TODO

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 442.

Draw Samples Using Lahiri's Method

Description

Draw Samples Using Lahiri's Method

Usage

lahiri.design(relsize, n, clnames = seq(along = relsize))

Arguments

Value

clusters vector of n PSUs selected with replacement and with probability proportional to relsize

Note

Original code from Lohr (1999), p. 452 – 453.

Author(s)

Sharon Lohr, slightly modified by Tobias Verbeke

References

Lahiri, D. B. (1951). A method of sample selection providing unbiased ratio estimates, Bulletin of the International Statistical Institute, 33: 133 – 140.

Survey of Parents of Children Non-Immunized against Measles

Description

Roberts et al. (1995) report on the results of a survey of parents whose children had not been immunized against measles during a recent campaign to immunize all children in the first five years of secondary school.

Usage

measles

Format

Data frame with 11 variables. A parent who refused consent (variable 4) was asked why, with responses in variables 5-10. A parent could give more than one reason for not having the child immunized.

school

school attended by child

form

parent received consent form

returnf

parent returned consent form

consent

parent gave consent for measles immunization

hadmeas

child had already had measles

previmm

child had been immunized against measles

sideeff

parent concerned about side effects

gp

parent wanted GP (general practitioner) to give vaccine

noshot

child did not want injection

notser

parent thought measles not serious illness

gpadv

GP advised that vaccine was not needed

Note

The original data were unavailable; univariate and multivariate summary statistics from these artificial data, however, are consistent with those in the paper.

Source

Roberts R. J. et al. (1995). Reasons for non-uptake of measles, mumps, and rubella catch up immunisation in a measles epidemic and side effects of the vaccine, British Medical Journal, 310, 1629–1632.

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 442.

Victimization Incidents in the July-December 1989 NCVS

Description

Selected variables for victimization incidents in the July-December 1989 NCVS. Note that some variables were recoded from the original data file.

Usage

ncvs

Format

Data frame with the following seven variables:

wt

incident weight

sex

factor with levels male and female

violent

violent crime? factor with levels no and yes

injury

did the victim have injuries? factor with levels no and yes

medcare

factor with levels yes if the victim received medical care and no otherwise

reppol

was the incident reported to the police? factor with levels yes and no

numoff

number of offenders involved in crime; factor with levels one, more (more than one) and dontknow

Source

Incident-level concatenated file, NCS8864I, in NCJ-130915, U.S. Department of Justice 1991.

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 443.

Data Collected in the New York Bight

Description

Data collected in the New York Bight for June 1974 and June 1975 (Wilk et al. 1977)

Usage

nybight

Format

Data frame with the following 7 variables:

year

year

stratum

stratum membership, based on depth

catchnum

number of fish caught during trawl

catchwt

total weight (kg) of fish caught during trawl

numspp

number of species of fish caught during trawl

depth

depth of station (m)

temp

surface temperature (degrees Celsius)

Note

Two of the original strata were combined because of insufficient sample sizes.

Source

Wilk, S.J. et al. (1977). Fishes and associated environmental data collected in New York bight, June 1974 - June 1975. NOAA Technical Report NMFS SSRF-716. Washington, D.C: Government Printing Office.

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 443.

Otters Data

Description

Data on number of holts (dens) in Shetland, United Kingdom used in Kruuk et al. (1989). (Data courtesy of Hans Kruuk).

Usage

otters

Format

Data frame with the following three variables:

section

coastline section

habitat

type of habitat (stratum)

holts

number of holts

Source

Kruuk, H.A. et al. (1989). An estimate of numbers and habitat preferences of otters Lutra lutra in Shetland, UK., Biological Conservation, 49: 241–254.

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 443.

Ozone Readings from Eskdalemuir, for 1994 and 1995

Description

Hourly ozone readings in parts per billion (ppb) from Eskdalemuir, Scotland, for 1994 and 1995

Usage

ozone

Format

Data frame with the following 25 variables:

date

date (day/month/year)

GMT1

ozone reading at 1:00 GMT

GMT2

ozone reading at 2:00 GMT

GMT3

ozone reading at 3:00 GMT

GMT4

ozone reading at 4:00 GMT

GMT5

ozone reading at 5:00 GMT

GMT6

ozone reading at 6:00 GMT

GMT7

ozone reading at 7:00 GMT

GMT8

ozone reading at 8:00 GMT

GMT9

ozone reading at 9:00 GMT

GMT10

ozone reading at 10:00 GMT

GMT11

ozone reading at 11:00 GMT

GMT12

ozone reading at 12:00 GMT

GMT13

ozone reading at 13:00 GMT

GMT14

ozone reading at 14:00 GMT

GMT15

ozone reading at 15:00 GMT

GMT16

ozone reading at 16:00 GMT

GMT17

ozone reading at 17:00 GMT

GMT18

ozone reading at 18:00 GMT

GMT19

ozone reading at 19:00 GMT

GMT20

ozone reading at 20:00 GMT

GMT21

ozone reading at 21:00 GMT

GMT22

ozone reading at 22:00 GMT

GMT23

ozone reading at 23:00 GMT

GMT24

ozone reading at 24:00 GMT

Source

Air Quality Information Centre: retrieved from a now defunct URL (http://www.aeat.co.uk)

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 443.

Samples Dataset

Description

All possible SRSs that can be generated from the population in Example 2.1 of Lohr(1999).

Usage

samples

Format

Data frame with the following 10 variables:

snum

sample number

unit1

first unit in sample

unit2

second unit in sample

unit3

third unit in sample

unit4

fourth unit in sample

value1

value for first unit in sample

value2

value for second unit in sample

value3

value for third unit in sample

value4

value for fourth unit in sample

that

t hat, i.e. estimate of the population total based on the given sample

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. 26–27 and 444.

Breathing Holes of Seals

Description

Data on number of breathing holes found in sampled areas of Svalbard fjords, reconstructed from summary statistics given in Lydersen and Ryg (1991)

Usage

seals

Format

Data frame with the following 2 variables:

zone

zone number for sampled area

holes

number of breathing holes Imjak found in area

Note

The data are used in Chapter 4, Exercise 11.

Source

Lydersen, C. and Ryg, M. (1991). Evaluating breeding habitat and populations of ringed seals Phoca hispida in Svalbard fjords, Polar Record, 27: 223–228.

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 444.

Steps used in Selecting an SRS

Description

Steps used in selecting the simple random sample (SRS) in Example 2.4 of Lohr(1999).

Usage

selectrs

Format

Data frame with the following 5 variables:

a

random number generated between 0 and 1

b

ceiling(3048*RN), with RN the random number in column a

c

distinct values in column b

d

new values generated to replace duplicates in b

e

final set of distinct values to be used in sample

Note

the set of indices in column e was used to select observations from agpop into dataset agsrs.

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. 31–34 and 444.

Unequal-Probability Sample of Counties in the US

Description

counties selected with probability proportional to 1992 population

Usage

statepop

Format

state

state abbreviation

county

county

landarea

land area of country, 1990 (square miles)

popn

population of county, 1992

phys

number of physicians, 1990

farmpop

farm population, 1990

numfarm

number of farms, 1987

farmacre

number of acres devoted to farming, 1987

veterans

number of veterans, 1990

percviet

percent of veterans from Vietnam era, 1990

Source

City and Counties Book, 1994

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. 190 – 192 and 444.

Information on States

Description

Number of counties, land area, and population for the 50 states plus the District of Columbia

Usage

statepps

Format

Date frame with the following 7 variables:

state

state name

counties

number of counties in state

cumcount

cumulative number of counties

landarea

land area of state, 1990 (square miles)

cumland

cumulative land area

popn

population of state, 1992

cumpopn

cumulative population

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 445.

Survey of Youth in Custody, 1987

Description

The 1987 Survey of Youth in Custody sampled juveniles and young adults in long-term, state-operated juvenile institutions. Residents of facilities at the end of 1987 were interviewed about family background, previous criminal history, and drug and alcohol use. Selected variables from the survey are contained in the syc data frame.

Usage

syc

Format

stratum

stratum number

psu

psu (facility) number

psusize

number of eligible residents in psu

initwt

initial weight

finalwt

final weight

randgrp

random group number

age

age of resident

race

race of resident: factor with levels 1 (white), 2 (black), 3 (Asian/Pacific Islander), 4 (American Indian, Aleut, Eskimo), 5 (other)

ethnicty

ethnicity; factor with levels hispanic and notHispanic

educ

highest grade before sent to correctional institution; factor with levels 0 (never attended), 1-12 (highest grade attended), 13 (GED), 14 (other)

sex

factor with levels male and female

livewith

factor with levels 1 (mother only), 2 (father only), 3 (both mother and father), 4 (grandparents), 5 (other relatives), 6 (friends), 7 (foster home), 8 (agency or institution), 9 (someone else)

famtime

Has anyone in your family, such as your mother, father, brother, sister, ever served time in jail or prison? factor with levels yes and no

crimtype

most serious crime in current offense; one of violent (e.g. murder, rape, robbery, assault), property (e.g. burglary, larceny, arson, fraud, motor vehicle theft), drug (drug possession or trafficking), publicorder (weapons violation, perjury, failure to appear in court), juvenile (juvenile-status offense, e.g. truancy, running away, incorrigible behavior)

everviol

Ever put on probation or sent to correctional institution for violent offense? factor with levels no and yes

numarr

number of times arrested (integer)

probtn

number of times on probation

corrinst

number of times previously committed to correctional institution

evertime

Prior to being sent here, did you ever serve time in a correctional institution? factor with levels yes and no

prviol

previously arrested for violent offense; factor with levels no and yes

prprop

previously arrested for property offense; factor with levels no and yes

prdrug

previously arrested for drug offense; factor with levels no and yes

prpub

previously arrested for public-order offense; factor with levels no and yes

prjuv

previously arrested for juvenile-status offense; factor with levels no and yes

agefirst

age first arrested (integer)

usewepn

Did you use a weapon... for this incident? factor with levels yes and no

alcuse

Did you drink alcohol at all during the year before being sent here this time? factor with levels yes, noduringyear, noatall

everdrug

Ever used illegal drugs? factor with levels no, yes

Source

Inter-University Consortium on Political and Social Research, NCJ-130915, U.S. Department of Justice 1989.

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. 235–239 and 445.

Elementary School Teacher Workload Data

Description

Selected variables from a study on elementary school teacher workload in Maricopa County, Arizona.

Usage

teachers

Format

data frame with the following 6 variables:

dist

school district size; factor with levels large and me/sm (medium/small)

school

school identifier

hrwork

number of hours required to work at school per week

size

class size

preprmin

minutes spent per week in school on preparation

assist

minutes per week that a teacher's aide works with the teacher in the classroom

Note

The study is described in Exercise 16 of Chapter 15. The psu sizes are given in teachmi. The large stratum had 245 schools; the small/medium stratum had 66 schools.

Source

Data courtesy of Rita Gnap (1995).

References

Gnap, R. (1995). Teacher load in Arizona elementary school districts in Maricopa County. Ph.D. diss., Arizona State University

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 446.

Cluster Sizes for Elementary School Teacher Workload Data

Description

Cluster sizes for the study on elementary school teacher workload in Maricopa County, Arizona.

Usage

teachmi

Format

data frame with the following 6 variables:

dist

school district size; factor with levels large and me/sm (medium/small)

school

school identifier

popteach

number of teachers in that school

ssteach

number of surveys returned from that school

Note

The study is described in Exercise 16 of Chapter 15. The actual date are given in teachers.

Source

Data courtesy of Rita Gnap (1995).

References

Gnap, R. (1995). Teacher load in Arizona elementary school districts in Maricopa County. Ph.D. diss., Arizona State University

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 446.

Follow-Up Study of Nonrespondents from Gnap (1995)

Description

Follow-up study of nonrespondents from the Gnap (1995) study on the workload of elementary school teachers in Maricopa County, Arizona.

Usage

teachnr

Format

data frame with the following 6 variables:

hrwork

number of hours required to work at school per week

size

class size

preprmin

minutes spent per week in school on preparation

assist

minutes per week that a teacher's aide works with the teacher in the classroom

Note

The study is described in Exercise 16 of Chapter 15. The actual date are given in teachers. Cluster size data for the original study are given in teachmi.

Source

Data courtesy of Rita Gnap (1995).

References

Gnap, R. (1995). Teacher load in Arizona elementary school districts in Maricopa County. Ph.D. diss., Arizona State University

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 446.

ASU Winter Closure Survey

Description

Selected variables from the Arizona State University Winter Closure Survey, taken in January 1995. This survey was taken to investigate the attitudes and opinions of university employees toward the closing of the university between December 25 and January 1.

Usage

winter

Format

data frame with the following 6 variables:

class

stratum number; factor with levels faculty, classstaff (classified staff), admstaff (administrative staff) and acprof (academic professional)

yearasu

factor with levels 1 (1-2 years), 2 (3-4 years), 3 (5-9 years), 4 (10-14 years) and 5 (15 or more years)

vacation

In the past, have you usually taken vacation days in the entire period between December 25 and January 1? factor with levels no and yes

work

Did you work on campus during Winter Break Closure? factor with levels no and yes

havediff

Did the Winter Break Closure cause you any difficulty/concerns? factor with levels no and yes

negaeffe

Did the Winter Break Closure negatively affect your work productivity? factor with levels no and yes

ownsupp

I was unable to obtain staff support in my department/office. factor with levels yes and no

othersup

I was unable to obtain staff support in other departments/offices. factor with levels yes and no

utility

I was unable to access computers, copy machine, etc. in my department/office. factor with levels yes and no

environ

I was unable to endure environmental conditions - e.g., not properly climatized. factor with levels yes and no

uniserve

I was unable to access university services necessary to my work; factor with levels yes and no

workelse

I was unable to work on my assignments because I work in another department/office; factor with levels yes and no

offclose

I was unable to work on my assignments because my office was closed; factor with levels yes and no

treatsta

compared to other departments/offices, I feel staff in my department/office were treated fairly; factor with levels strongagr (strongly agree), agree, undecided, disagree, strdisagr (strongly disagree)

treatme

compared to other people working in my department/office, I feel I was treated fairly; factor with levels strongagr (strongly agree), agree, undecided, disagree, strdisagr (strongly disagree)

process

How satisfied are you with the process used to inform staff about Winter Closure? factor with levels verysat (very satisfied), satisfied, undecided, dissatisfied and verydissat (very dissatisfied)

satbreak

How satisfied are you with the fact that ASU had a Winter Break Closure this year? factor with levels verysat (very satisfied), satisfied, undecided, dissatisfied and verydissat (very dissatisfied)

breakaga

Would you want to have Winter Break Closure again? factor with levels no and yes

Source

courtesy of the ASU Office of University Evaluation.

References

Lohr (1999). Sampling: Design and Analysis, Duxbury, p. TODO and 447–448.