# Analysing the American National Election Study of 1948 with memisc

## Introduction

This vignette gives an example for the analysis of a typical social science data set. It is the data file of the American National Election Study of 19481 available from the American National Election Studies website. The data file contains data from to USA-wide surveys conducted October and November 1948 by the Survey Research Centre, University Michigan (principal investigators: Angus Campbell and Robert L. Kahn). The total number of cases in the data set is 662 and the number of variables is 65 (more details about this data set can be found at https://electionstudies.org/studypages/1948prepost/1948prepost.htm).

With 662 cases and 65 variables, the 1948 ANES data set is relatively small as compared to current social science data sets. Such larger data sets can be processed along the same lines as in this vignette. Unlike the 1948 ANES data, their size as well as, in some cases, legal restrictions prohibit the inclusion of such a data set into the package, however.

This vignette starts with a demonstration how a data file can be examined before loading it and how a subset of the data can be loaded into memory. After loading this subset into memory, some descriptive analyses are conducted that showcase the construction of contingency tables and of general tables of descriptive statistics using the genTable function. In addition, a logit analysis is demonstrated and the collection of several logit coefficients into a comprehensive table by the mtable function.

It should be noted that the analyses reported in the following are conducted only for purpose of demonstrating the features of the package and are not to be considered of conclusive scientific evidence of any kind.

This vignette is run with the help of the knitr package. This allows to showcase not only data management facilities provided by memisc. The following code also demonstrates how output created with some of the facilities of memisc can neatly integrated in reports generated with knitr. Before we start, we adjust knitr’s output (with which this vignette is formatted) to produce HTML where possible.

knit_print.codebook <-function(x,...)
knitr::asis_output(format_html(x,...))

knit_print.descriptions <-function(x,...)
knitr::asis_output(format_html(x,...))

knit_print.ftable <-function(x,options,...)
knitr::asis_output(
format_html(x,
digits=if(length(options$ftable.digits)) options$ftable.digits
else 0,
...))
# We can now adjust the number of digits after the comma
# for each column e.g. by adding an ftable.digits option
# to an R chunk, as in {r,ftable=c(2,2,0)}

knit_print.mtable <-function(x,...)
knitr::asis_output(format_html(x,...))

## Reading in a “portable” SPSS data file

We start with importing the data into R. The following code extracts the SPSS portable file NES1948.POR from zip file NES1948.ZIP delivered with the memisc package.

library(memisc)
options(digits=3)
nes1948.por <- unzip(system.file("anes/NES1948.ZIP",package="memisc"),
"NES1948.POR",exdir=tempfile())

Now the portable file is in a temporary directory and the path to the file is contained in the string variable nes1948.por. In the next step, the file is declared as a SPSS/PSPP portable file using the function spss.portable.file, which as first argument takes the path to the file. spss.portable.file reads in the information about the variables contained in the data set and counts the number of cases in the file. That is, standard I/O operations are used on the file, but the data read in are just thrown away without allocating core memory for the data. This counting of cases can, of course, be suppressed if it would take too long.

nes1948 <- spss.portable.file(nes1948.por)
Warning: 9 variables have duplicated labels:
V480004, V480012, V480020, V480021A, V480021B, V480033A, V480033B,
V480034A, V480034B
print(nes1948)

SPSS portable file '/tmp/RtmpmGpSo5/file427b305f9116/NES1948.POR'
with 67 variables and 662 observations

At this stage, the data are not loaded into the memory yet. But we can see which variables exist inside the data set:

names(nes1948)
 [1] "VVERSION" "VDSETNO"  "V480001"  "V480002"  "V480003"  "V480004"
[7] "V480005"  "V480006"  "V480007"  "V480008"  "V480009"  "V480010"
[13] "V480011"  "V480012"  "V480013"  "V480014A" "V480014B" "V480015A"
[19] "V480015B" "V480016A" "V480016B" "V480017A" "V480017B" "V480018"
[25] "V480019"  "V480020"  "V480021A" "V480021B" "V480022A" "V480022B"
[31] "V480023"  "V480024"  "V480025A" "V480025B" "V480026"  "V480027"
[37] "V480028"  "V480029"  "V480030"  "V480031A" "V480031B" "V480031C"
[43] "V480032A" "V480032B" "V480032C" "V480033A" "V480033B" "V480034A"
[49] "V480034B" "V480035A" "V480035B" "V480036A" "V480036B" "V480037"
[55] "V480038"  "V480039"  "V480040"  "V480041"  "V480042"  "V480043"
[61] "V480044"  "V480045"  "V480046"  "V480047"  "V480048"  "V480049"
[67] "V480050" 

Note that the variable names are all changed from uppercase to lowercase (SPSS does not distinguish uppercase and lowercase variable names and uppercase looks like shouting). Casefolding could have been suppressed by the call spsp.portable.file(nes1948.por,tolower=FALSE).

We also can ask for a description (“variable label”) for each variable:

description(nes1948)
 Variable Description VVERSION NES VERSION NUMBER VDSETNO NES DATASET NUMBER V480001 ICPSR ARCHIVE NUMBER V480002 INTERVIEW NUMBER V480003 POP CLASSIFICATION V480004 CODER V480005 NUMBER OF CALLS TO R V480006 R REMEMBER PREVIOUS INT V480007 INTR INTERVIEW THIS R V480008 PRVS PRE-ELCTN R REINT V480009 R INT IN PRE/POSTELCTN V480010 RENT CNTRL KEPT/DROPPED V480011 GOVT CONTROL PRICES V480012 WHAT TO DO W TFT-HT ACT V480013 PRESLELCTN OTCM SURPRISE V480014A WHY PPL VTD FOR TRUMAN 1 V480014B WHY PPL VTD FOR TRUMAN 2 V480015A WHY PPL VTD AGNST TRUMAN 1 V480015B WHY PPL VTD AGNST TRUMAN 2 V480016A WHY PPL VTD FOR DEWEY 1 V480016B WHY PPL VTD FOR DEWEY 2 V480017A WHY PPL VTD AGNST DEWEY 1 V480017B WHY PPL VTD AGNST DEWEY 2 V480018 DID R VOTE/FOR WHOM V480019 WN DECIDE FOR WHOM TO VT V480020 CNSD VT FOR SOMEONE ELSE V480021A XWHY DID NOT VT FOR HIM 1 V480021B XWHY DID NOT VT FOR HIM 2 V480022A WHY VT THE WAY YOU DID 1 V480022B WHY VT THE WAY YOU DID 2 V480023 VOTED STRAIGHT TICKET V480024 R NOT VT-IF VT,FOR WHOM V480025A R NOT VT-WHY DID NOT VT 1 V480025B R NOT VT-WHY DID NOT VT 2 V480026 R NOT VT-WAS R REG TO VT V480027 VTD IN PRVS PRESL ELCTN V480028 VTD FOR WHOM IN 1944 V480029 OCCUPATION OF HEAD V480030 HEAD BELONG TO LBR UN V480031A GRPS IDENTIFIED W TRUMAN 1 V480031B GRPS IDENTIFIED W TRUMAN 2 V480031C GRPS IDENTIFIED W TRUMAN 3 V480032A GRPS IDENTIFIED W DEWEY 1 V480032B GRPS IDENTIFIED W DEWEY 2 V480032C GRPS IDENTIFIED W DEWEY 3 V480033A ISSUES CONNECTED W TRMN 1 V480033B ISSUES CONNECTED W TRMN 2 V480034A ISSUES CONNECTED W DEWEY 1 V480034B ISSUES CONNECTED W DEWEY 2 V480035A PERSONAL ATTRIBUTE TRMN 1 V480035B PERSONAL ATTRIBUTE TRMN 2 V480036A PERSONAL ATTRIBUTE DEWEY 1 V480036B PERSONAL ATTRIBUTE DEWEY 2 V480037 CMPN INCIDENTS MENTIONED V480038 41-PRESLELCTN PLAN TO VT V480039 41-PLAN TO VT REP/DEM V480040 41-USA’S CNCRN W OTHERS V480041 41-SATISD USA TWRD RUSS V480042 41-INFORMATION LEVEL V480043 41-USA GV IN,AGRT RUSS V480044 41-USA-RUSS AGRT VIA U.N V480045 SEX OF RESPONDENT V480046 RACE OF RESPONDENT V480047 AGE OF RESPONDENT V480048 EDUCATION OF RESPONDENT V480049 TOTAL 1948 INCOME V480050 RELIGIOUS PREFERENCE

or even a code book using

codebook(nes1948)

(this is not shown here because the output would have taken more then thirty pages). We can also get a codebook of the first few variabels instead, with

codebook(nes1948[1:5])

VVERSION‘NES VERSION NUMBER’

 Storage mode: double Measurement: interval

 Min: 1 . 000 Max: 1 . 000 Mean: 1 . 000 Std.Dev.: 0 . 000

VDSETNO‘NES DATASET NUMBER’

 Storage mode: character Measurement: nominal

 Min: "1948 T" Max: "1948 T"

V480001‘ICPSR ARCHIVE NUMBER’

 Storage mode: double Measurement: interval

 Min: 7218 . 000 Max: 7218 . 000 Mean: 7218 . 000 Std.Dev.: 0 . 000

V480002‘INTERVIEW NUMBER’

 Storage mode: double Measurement: interval

 Min: 1001 . 000 Max: 1662 . 000 Mean: 1331 . 500 Std.Dev.: 191 . 103

V480003‘POP CLASSIFICATION’

 Storage mode: double Measurement: nominal

 Values and labels N Percent 1 ‘METROPOLITAN AREA’ 182 27 . 5 2 ‘TOWN OR CITY’ 354 53 . 5 3 ‘OPEN COUNTRY’ 126 19 . 0

### Reading in a subset of the data

After we have decided which variables to use we can read in a subset of the data:

vote.48 <- subset(nes1948,
select=c(
V480018,
V480029,
V480030,
V480045,
V480046,
V480047,
V480048,
V480049,
V480050
))

The subset of the ANES 1948 we read in is now contained in the variable vote.48, which contains an object of class data.set. A data.set is an “embellished” version of a data.frame, a data structure intended to contained labelled vectors. labelled vectors contain the all the special information attached to the variables in the original data set, such as variable labels, value labels, and general missing values. A short summary of this special information shows up after a call to str.

str(vote.48)
Data set with 662 obs. of 9 variables:
$V480018: Nmnl. item w/ 7 labels for 1,2,3,... + ms.v. num 1 2 1 2 1 2 2 1 2 1 ...$ V480029: Nmnl. item w/ 12 labels for 10,20,30,... + ms.v.  num  70 30 40 10 10 20 80 80 40 40 ...
$V480030: Nmnl. item w/ 4 labels for 1,2,8,... + ms.v. num 1 2 2 2 2 2 2 2 1 1 ...$ V480045: Nmnl. item w/ 3 labels for 1,2,9 + ms.v.  num  1 2 2 2 1 2 1 2 1 1 ...
$V480046: Nmnl. item w/ 4 labels for 1,2,3,... + ms.v. num 1 1 1 1 1 1 1 1 1 1 ...$ V480047: Nmnl. item w/ 7 labels for 1,2,3,... + ms.v.  num  3 3 2 3 2 3 4 5 2 2 ...
$V480048: Nmnl. item w/ 4 labels for 1,2,3,... + ms.v. num 1 2 2 3 3 2 1 1 2 2 ...$ V480049: Nmnl. item w/ 8 labels for 1,2,3,... + ms.v.  num  4 7 5 7 5 7 5 2 5 6 ...
$V480050: Nmnl. item w/ 6 labels for 1,2,3,... + ms.v. num 1 1 2 1 2 1 1 1 1 2 ... This output shows, for example, that variable V480018 has the description (variable label) “DID R VOTE/FOR WHOM” is considered as having nominal level of measurement, has seven value labels and one defined missing value. Since the variable names in the ANES data set are not very mnemonic, we rename the variables: vote.48 <- rename(vote.48, V480018 = "vote", V480029 = "occupation.hh", V480030 = "unionized.hh", V480045 = "gender", V480046 = "race", V480047 = "age", V480048 = "education", V480049 = "total.income", V480050 = "religious.pref" ) Since many data sets available from public repositories have such non-mnemonic variable names as in this example, it might be convenient to do the data loading and renaming in one step. Indeed it is possible: vote.48 <- subset(nes1948, select=c( vote = V480018, occupation.hh = V480029, unionized.hh = V480030, gender = V480045, race = V480046, age = V480047, education = V480048, total.income = V480049, religious.pref = V480050 )) Before we start with analyses, we take a closer look at the data. codebook(vote.48) vote‘DID R VOTE/FOR WHOM’  Storage mode: double Measurement: nominal Missing values: 9  Values and labels N Valid Total 1 ‘VOTED - FOR TRUMAN’ 212 32 . 1 32 . 0 2 ‘VOTED - FOR DEWEY’ 178 27 . 0 26 . 9 3 ‘VOTED - FOR WALLACE’ 1 0 . 2 0 . 2 4 ‘VOTED - FOR OTHER’ 11 1 . 7 1 . 7 5 ‘VOTED - NA FOR WHOM’ 20 3 . 0 3 . 0 6 ‘DID NOT VOTE’ 238 36 . 1 36 . 0 9 M ‘NA WHETHER VOTED’ 2 0 . 3 occupation.hh‘OCCUPATION OF HEAD’  Storage mode: double Measurement: nominal Missing values: 99  Values and labels N Valid Total 10 ‘PROFESSIONAL, SEMI-PROFESSIONAL’ 44 6 . 9 6 . 6 20 ‘SELF-EMPLOYED, MANAGERIAL, SUPERVISORY’ 73 11 . 5 11 . 0 30 ‘OTHER WHITE-COLLAR (CLERICAL, SALES, ET’ 79 12 . 5 11 . 9 40 ‘SKILLED AND SEMI-SKILLED’ 164 25 . 9 24 . 8 60 ‘PROTECTIVE SERVICE’ 6 0 . 9 0 . 9 70 ‘UNSKILLED, INCLUDING FARM AND SERVICE W’ 85 13 . 4 12 . 8 80 ‘FARM OPERATORS AND MANAGERS’ 105 16 . 6 15 . 9 92 ‘STUDENT’ 7 1 . 1 1 . 1 94 ‘UNEMPLOYED’ 5 0 . 8 0 . 8 95 ‘RETIRED, TOO OLD OR UNABLE TO WORK’ 38 6 . 0 5 . 7 96 ‘HOUSEWIFE’ 28 4 . 4 4 . 2 99 M ‘NA’ 28 4 . 2 unionized.hh‘HEAD BELONG TO LBR UN’  Storage mode: double Measurement: nominal Missing values: 8 - Inf  Values and labels N Valid Total 1 ‘YES’ 150 23 . 3 22 . 7 2 ‘NO’ 493 76 . 7 74 . 5 8 M ‘DK’ 5 0 . 8 9 M ‘NA’ 14 2 . 1 gender‘SEX OF RESPONDENT’  Storage mode: double Measurement: nominal Missing values: 9  Values and labels N Valid Total 1 ‘MALE’ 302 45 . 8 45 . 6 2 ‘FEMALE’ 357 54 . 2 53 . 9 9 M ‘NA’ 3 0 . 5 race‘RACE OF RESPONDENT’  Storage mode: double Measurement: nominal Missing values: 9  Values and labels N Valid Total 1 ‘WHITE’ 585 90 . 7 88 . 4 2 ‘NEGRO’ 60 9 . 3 9 . 1 3 ‘OTHER’ 0 0 . 0 0 . 0 9 M ‘NA’ 17 2 . 6 age‘AGE OF RESPONDENT’  Storage mode: double Measurement: nominal Missing values: 9  Values and labels N Valid Total 1 ‘18-24’ 57 8 . 7 8 . 6 2 ‘25-34’ 142 21 . 7 21 . 5 3 ‘35-44’ 174 26 . 6 26 . 3 4 ‘45-54’ 125 19 . 1 18 . 9 5 ‘55-64’ 86 13 . 1 13 . 0 6 ‘65 AND OVER’ 70 10 . 7 10 . 6 9 M ‘NA’ 8 1 . 2 education‘EDUCATION OF RESPONDENT’  Storage mode: double Measurement: nominal Missing values: 9  Values and labels N Valid Total 1 ‘GRADE SCHOOL’ 292 44 . 4 44 . 1 2 ‘HIGH SCHOOL’ 266 40 . 4 40 . 2 3 ‘COLLEGE’ 100 15 . 2 15 . 1 9 M ‘NA’ 4 0 . 6 total.income‘TOTAL 1948 INCOME’  Storage mode: double Measurement: nominal Missing values: 9  Values and labels N Valid Total 1 ‘UNDER$500’ 25 3 . 8 3 . 8 2 ‘$500-$999’ 43 6 . 6 6 . 5 3 ‘$1000-1999’ 110 16 . 8 16 . 6 4 ‘$2000-2999’ 185 28 . 2 27 . 9 5 ‘$3000-3999’ 142 21 . 7 21 . 5 6 ‘$4000-4999’ 66 10 . 1 10 . 0 7 ‘$5000 AND OVER’ 84 12 . 8 12 . 7 9 M ‘NA’ 7 1 . 1 religious.pref‘RELIGIOUS PREFERENCE’  Storage mode: double Measurement: nominal Missing values: 9  Values and labels N Valid Total 1 ‘PROTESTANT’ 460 70 . 0 69 . 5 2 ‘CATHOLIC’ 140 21 . 3 21 . 1 3 ‘JEWISH’ 25 3 . 8 3 . 8 4 ‘OTHER’ 14 2 . 1 2 . 1 5 ‘NONE’ 18 2 . 7 2 . 7 9 M ‘NA’ 5 0 . 8 We now have obtained a codebook, which contains information of the class and type of the variables in the data set, the value labels and defined missing values, and counts of the distinct values of the variables. ## Analysis ### Some descriptive analyses We start our analyses with a contingency table, but first we make some preparations: We recode the variables of interest into a smaller number of categories in order to get results that are easier to read and interpret. vote.48 <- within(vote.48,{ vote3 <- recode(vote, 1 -> "Truman", 2 -> "Dewey", 3:4 -> "Other" ) occup4 <- recode(occupation.hh, 10:20 -> "Upper white collar", 30 -> "Other white collar", 40:70 -> "Blue collar", 80 -> "Farmer" ) relig3 <- recode(religious.pref, 1 -> "Protestant", 2 -> "Catholic", 3:5 -> "Other,none" ) race2 <- recode(race, 1 -> "White", 2 -> "Black" ) }) Having constructed the unordered factors vote3, occup4, relig3, and race2 we can proceed examining the association the vote, occupational class, relgious denomination, and race. First, we look upon a simple contingency table. ftable(xtabs(~vote3+occup4,data=vote.48))  occup4 vote3 Upper white collar Other white collar Blue collar Farmer Truman 17 30 114 26 Dewey 67 31 36 14 Other 2 0 4 3 Tables of percentages may seem more informative about the impact of various factors on the vote. So we use the function genTable to obtain such tables of percentages: gt1 <- genTable(percent(vote3)~occup4,data=vote.48) ## For knitr-ing, we use {r, ftable.digits=c(2,2,2,0)} here. ftable(gt1,row.vars=2)  occup4 Truman Dewey Other N Upper white collar 19 . 77 77 . 91 2 . 33 86 Other white collar 49 . 18 50 . 82 0 . 00 61 Blue collar 74 . 03 23 . 38 2 . 60 154 Farmer 60 . 47 32 . 56 6 . 98 43 NA 43 . 10 51 . 72 5 . 17 58 Obviously, voters from farmer and blue collar worker households were especially supportive of President Truman, while voters of upper white collar background largely supported the Republican Candidate Dewey. gt2 <- genTable(percent(vote3)~relig3,data=vote.48) ftable(gt2,row.vars=2)  relig3 Truman Dewey Other N Protestant 44 . 71 50 . 98 4 . 31 255 Catholic 66 . 02 33 . 98 0 . 00 103 Other,none 68 . 18 29 . 55 2 . 27 44 NA NaN NaN NaN 0 This table shows that Catholics and adherents of other denominations were more supportive of Truman than of Dewey. gt3 <- genTable(percent(vote3)~race2,data=vote.48) ftable(gt3,row.vars=2)  race2 Truman Dewey Other N White 51 . 33 45 . 48 3 . 19 376 Black 64 . 71 35 . 29 0 . 00 17 NA 88 . 89 11 . 11 0 . 00 9 African Americans apparently supported Truman by a large majority. The number of members of this group in the sample is very small, however, so that such an inference would be very shaky. gt4 <- genTable(percent(vote3)~total.income,data=vote.48) ftable(gt4,row.vars=2)  total.income Truman Dewey Other N UNDER$500 50 . 00 50 . 00 0 . 00 8 $500-$999 61 . 54 38 . 46 0 . 00 13 $1000-1999 64 . 41 32 . 20 3 . 39 59$2000-2999 66 . 99 30 . 10 2 . 91 103 $3000-3999 47 . 52 48 . 51 3 . 96 101$4000-4999 45 . 83 50 . 00 4 . 17 48 $5000 AND OVER 31 . 82 68 . 18 0 . 00 66 NA 50 . 00 25 . 00 25 . 00 4 The table of percentage of vote by income suggests that income had some considerable influence on the choice either of Truman or of Dewey, but the unequal distribution of income categories warrants a more refined analysis that takes into account the uncertainty about the vote percentages. Therefore, the percentages of support for Truman broken down by income shown with confidence intervals: ## For knitr-ing, we use {r, ftable.digits=c(2,2,2)} here. inc.tab <- genTable(percent(vote3,ci=TRUE)~total.income,data=vote.48) ftable(inc.tab,row.vars=c(3,2))  total.income vote3 Percentage lower upper UNDER$500 Truman 50 . 00 15 . 70 84 . 30 Dewey 50 . 00 15 . 70 84 . 30 Other 0 . 00 0 . 00 36 . 94 $500-$999 Truman 61 . 54 31 . 58 86 . 14 Dewey 38 . 46 13 . 86 68 . 42 Other 0 . 00 0 . 00 24 . 71 $1000-1999 Truman 64 . 41 50 . 87 76 . 45 Dewey 32 . 20 20 . 62 45 . 64 Other 3 . 39 0 . 41 11 . 71$2000-2999 Truman 66 . 99 57 . 03 75 . 94 Dewey 30 . 10 21 . 45 39 . 92 Other 2 . 91 0 . 60 8 . 28 $3000-3999 Truman 47 . 52 37 . 49 57 . 70 Dewey 48 . 51 38 . 45 58 . 67 Other 3 . 96 1 . 09 9 . 83$4000-4999 Truman 45 . 83 31 . 37 60 . 83 Dewey 50 . 00 35 . 23 64 . 77 Other 4 . 17 0 . 51 14 . 25 $5000 AND OVER Truman 31 . 82 20 . 89 44 . 44 Dewey 68 . 18 55 . 56 79 . 11 Other 0 . 00 0 . 00 5 . 44 NA Truman 50 . 00 6 . 76 93 . 24 Dewey 25 . 00 0 . 63 80 . 59 Other 25 . 00 0 . 63 80 . 59 Occupational class is more evenly distributed in the sample, thus it may be possible to obtain more precise estimates of the percentages of support for Truman for occupational classes: occup.tab <- genTable(percent(vote3,ci=TRUE)~occup4,data=vote.48) ftable(occup.tab,row.vars=c(3,2))  occup4 vote3 Percentage lower upper Upper white collar Truman 19 . 77 11 . 96 29 . 75 Dewey 77 . 91 67 . 67 86 . 14 Other 2 . 33 0 . 28 8 . 15 Other white collar Truman 49 . 18 36 . 14 62 . 30 Dewey 50 . 82 37 . 70 63 . 86 Other 0 . 00 0 . 00 5 . 87 Blue collar Truman 74 . 03 66 . 35 80 . 75 Dewey 23 . 38 16 . 94 30 . 86 Other 2 . 60 0 . 71 6 . 52 Farmer Truman 60 . 47 44 . 41 75 . 02 Dewey 32 . 56 19 . 08 48 . 54 Other 6 . 98 1 . 46 19 . 06 NA Truman 43 . 10 30 . 16 56 . 77 Dewey 51 . 72 38 . 22 65 . 05 Other 5 . 17 1 . 08 14 . 38 The upper and lower white-collar and blue-collar classes are quite distinct with regard to the percentages of support for Truman. The point estimates of the percentages are outside the confidence intervals of the respective other occupational classes, the confidence intervals do not even overlap. However, it is not clear whether farmers are distinct from the blue-collar and lower white-collar classes. ### Logit modelling of candidate choice In the following we conduct a logit analysis of the vote for Truman. First, we assign non-standard contrasts the categorical predictors. Here, the function contr is used to assign treatment (dummy) contrasts to occup4 and total.income with baseline category 3 and 4, respectively. vote.48 <- within(vote.48,{ contrasts(occup4) <- contr("treatment",base = 3) contrasts(total.income) <- contr("treatment",base = 4) }) We now fit some logistic regression models of the impact occupational class, income, and religious denomination on the vote choice supporting Truman. The contrasts of the occupational class and income factors are such that they compare the choices of the members of the blue-collar class with all other classes and the middle income group ($ 2000-2999) with the other income groups. The religious denomination factor compares Protestants with Catholics and those with other or no denominations.

model1 <- glm((vote3=="Truman")~occup4,data=vote.48,
family="binomial")
model2 <- glm((vote3=="Truman")~total.income,data=vote.48,
family="binomial")
model3 <- glm((vote3=="Truman")~occup4+total.income,data=vote.48,
family="binomial")
model4 <- glm((vote3=="Truman")~relig3,data=vote.48,
family="binomial")
model5 <- glm((vote3=="Truman")~occup4+relig3,data=vote.48,
family="binomial")

First, we use mtable to construct a comparative table of the estimates of model1, model2, and model3. We thus can compare the impact of occupational class and income on the choice of candidate Truman.

mtable(model1,model2,model3,summary.stats=c("Nagelkerke R-sq.","Deviance","AIC","N"))

Calls:
model1: glm(formula = (vote3 == "Truman") ~ occup4, family = "binomial",
data = vote.48)
model2: glm(formula = (vote3 == "Truman") ~ total.income, family = "binomial",
data = vote.48)
model3: glm(formula = (vote3 == "Truman") ~ occup4 + total.income, family = "binomial",
data = vote.48)

===============================================================================
model1      model2      model3
-------------------------------------------------------------------------------
(Intercept)                                1.047***    0.708***    1.316***
(0.184)     (0.210)     (0.268)
occup4: Upper white collar/Blue collar    -2.448***               -2.328***
(0.327)                 (0.357)
occup4: Other white collar/Blue collar    -1.080***               -1.015**
(0.315)                 (0.323)
occup4: Farmer/Blue collar                -0.622                  -0.792*
(0.362)                 (0.383)
total.income: UNDER $500/$2000-2999                   -0.708      -0.662
(0.737)     (1.056)
total.income: $500-$999/$2000-2999 -0.238 0.912 (0.607) (1.143) total.income:$1000-1999/$2000-2999 -0.115 0.144 (0.343) (0.440) total.income:$3000-3999/$2000-2999 -0.807** -0.527 (0.289) (0.338) total.income:$4000-4999/$2000-2999 -0.875* -0.509 (0.358) (0.411) total.income:$5000 AND OVER/$2000-2999 -1.470*** -0.535 (0.337) (0.405) ------------------------------------------------------------------------------- Nagelkerke R-sq. 0.246 0.085 0.274 Deviance 404.190 524.433 390.551 AIC 412.190 538.433 410.551 N 344 398 340 =============================================================================== Significance: *** = p < 0.001; ** = p < 0.01; * = p < 0.05  mtable returns an object of class "mtable". When formatted it looks close to the requirements of typical social science publications. Yet at least we want to change the technical variable names into non-technical ones, for which we can use relabel: relabel(mtable( "Model 1"=model1, "Model 2"=model2, "Model 3"=model3, summary.stats=c("Nagelkerke R-sq.","Deviance","AIC","N")), UNDER="under", "AND OVER"="and over", occup4="Occup. class", total.income="Income", gsub=TRUE )  Calls: Model 1: glm(formula = (vote3 == "Truman") ~ occup4, family = "binomial", data = vote.48) Model 2: glm(formula = (vote3 == "Truman") ~ total.income, family = "binomial", data = vote.48) Model 3: glm(formula = (vote3 == "Truman") ~ occup4 + total.income, family = "binomial", data = vote.48) ==================================================================================== Model 1 Model 2 Model 3 ------------------------------------------------------------------------------------ (Intercept) 1.047*** 0.708*** 1.316*** (0.184) (0.210) (0.268) Occup. class: Upper white collar/Blue collar -2.448*** -2.328*** (0.327) (0.357) Occup. class: Other white collar/Blue collar -1.080*** -1.015** (0.315) (0.323) Occup. class: Farmer/Blue collar -0.622 -0.792* (0.362) (0.383) Income: under$500/$2000-2999 -0.708 -0.662 (0.737) (1.056) Income:$500-$999/$2000-2999                               -0.238       0.912
(0.607)     (1.143)
Income: $1000-1999/$2000-2999                              -0.115       0.144
(0.343)     (0.440)
Income: $3000-3999/$2000-2999                              -0.807**    -0.527
(0.289)     (0.338)
Income: $4000-4999/$2000-2999                              -0.875*     -0.509
(0.358)     (0.411)
Income: $5000 and over/$2000-2999                          -1.470***   -0.535
(0.337)     (0.405)
------------------------------------------------------------------------------------
Nagelkerke R-sq.                                0.246       0.085       0.274
Deviance                                      404.190     524.433     390.551
AIC                                           412.190     538.433     410.551
N                                             344         398         340
====================================================================================
Significance: *** = p < 0.001; ** = p < 0.01; * = p < 0.05  

The comparison of the pseudo-R-Square values of model 1 and 2 suggests that occupational class has a stronger influence on a preference for Truman than household income. Indeed, if occupational class is taken into account, the effect of income is no longer statistically significant as the column corresponding to model 3 indicates.

Second, we compare the effect of occupational class and religious denomination on the preference for Truman along the same lines as above. We use mtable to collect the estimates of model1, model4, and model5 into a common table.

relabel(mtable(
"Model 1"=model1,
"Model 4"=model4,
"Model 5"=model5,
summary.stats=c("Nagelkerke R-sq.","Deviance","AIC","N")),
occup4="Occup. class",
relig3="Religion",
gsub=TRUE
)

Calls:
Model 1: glm(formula = (vote3 == "Truman") ~ occup4, family = "binomial",
data = vote.48)
Model 4: glm(formula = (vote3 == "Truman") ~ relig3, family = "binomial",
data = vote.48)
Model 5: glm(formula = (vote3 == "Truman") ~ occup4 + relig3, family = "binomial",
data = vote.48)

====================================================================================
Model 1     Model 4     Model 5
------------------------------------------------------------------------------------
(Intercept)                                     1.047***   -0.213       0.698**
(0.184)     (0.126)     (0.216)
Occup. class: Upper white collar/Blue collar   -2.448***               -2.385***
(0.327)                 (0.337)
Occup. class: Other white collar/Blue collar   -1.080***               -1.098***
(0.315)                 (0.326)
Occup. class: Farmer/Blue collar               -0.622                  -0.346
(0.362)                 (0.374)
Religion: Catholic/Protestant                               0.877***    0.685*
(0.243)     (0.292)
Religion: Other,none/Protestant                             0.975**     1.191**
(0.347)     (0.441)
------------------------------------------------------------------------------------
Nagelkerke R-sq.                                0.246       0.060       0.281
Deviance                                      404.190     537.711     393.105
AIC                                           412.190     543.711     405.105
N                                             344         402         344
====================================================================================
Significance: *** = p < 0.001; ** = p < 0.01; * = p < 0.05  `

A comparison of the pseudo-R-squared values suggests that also the effect of religious denomination is weaker than that of occupational class. However, as the third column in the above table indicates the effect of religious denomination remains statistically significant.

1. National Election Studies, 1948: Post-Election Study [dataset]. Ann Arbor, MI: University of Michigan, Center for Political Studies [producer and distributor], 1999. ANES Dataset ID: 1948.T; ICPSR Study Number: 7218. These materials are based on work supported by the National Science Foundation under Grant Nos.: SBR-9707741, SBR-9317631, SES-9209410, SES-9009379, SES-8808361, SES-8341310, SES-8207580, and SOC77-08885. Any opinions, findings and conclusions or recommendations expressed in these materials are those of the author(s) and do not necessarily reflect those of the National Science Foundation.