| title: “Voting and protest activity” |
| author: “Emre Toros” |
| date: “2 September 2016” |
| output: html_document |
RQ: Does protesting activity relates with voting to different parties? Dataset: 2014 ISSP Citizenship Data - Turkey DV: Voting, coded 1 for vote, 0 for no vote, party specific IVs: Participate in demonstrations (4 level scale, higher scores for not participating), Age & Sex(Female)
rm(list=ls()) library("sjmisc")
library("sjPlot")
library("sjstats")##
## Attaching package: 'sjstats'## The following objects are masked from 'package:sjmisc':
##
## chisq_gof, cod, converge_ok, cramer, cronb, cv, deff, eta_sq,
## get_re_var, hoslem_gof, icc, levene_test, mean_n, mic, mwu,
## overdisp, phi, r2, re_var, reliab_test, rmse, se, smpsize_lmm,
## std_beta, table_values, weight, weight2, wtd_sd, wtd_selibrary("lessR")##
## lessR 3.5.0 feedback: gerbing@pdx.edu web: lessRstats.com
## -------------------------------------------------------------------
## 1. Read a text, Excel, SPSS, SAS or R data file from your computer
## into the mydata data table, enter: mydata <- Read()
## 2. For a list of help topics and functions, enter: Help()
## 3. Use theme function for global settings, Ex: theme(colors="gray")##
## Attaching package: 'lessR'## The following object is masked from 'package:sjmisc':
##
## recThe Data
library("foreign")
cd <- read.dta("D:/emre/SkyDrive/makale/datasets/International Social Survey Programme 2014 Citizenship ISSP/ISSP2014Turkey.dta")## Warning in `levels<-`(`*tmp*`, value = if (nl == nL) as.character(labels)
## else paste0(labels, : duplicated levels in factors are deprecated
## Warning in `levels<-`(`*tmp*`, value = if (nl == nL) as.character(labels)
## else paste0(labels, : duplicated levels in factors are deprecatedCreating Party Variables
levels(cd$TR_PRTY)## [1] "NAP (code 0, 2, 7 in VOTE_LE)"
## [2] "Justice and Development Party - AKP"
## [3] "Grand Union Party - BBP"
## [4] "Peace and Democracy Party - BDP"
## [5] "Republican Peoples Party - CHP"
## [6] "Democratic Party - True Path Party - DP-DYP"
## [7] "Nationalist Action Party - MHP"
## [8] "Felicity Party - SP"
## [9] "Independent Candidate"
## [10] "Invalid ballot"
## [11] "No answer"
## [12] "NAP, other countries"#AKP
cd$akp <- 0
cd$akp[cd$TR_PRTY=="Justice and Development Party - AKP"] <- 1
#CHP
cd$chp <- 0
cd$chp[cd$TR_PRTY=="Republican Peoples Party - CHP"] <- 1
#MHP
cd$mhp <- 0
cd$mhp[cd$TR_PRTY=="Nationalist Action Party - MHP"] <- 1
#BDP
cd$bdp <- 0
cd$bdp[cd$TR_PRTY=="Peace and Democracy Party - BDP"] <- 1Simple model on parties and protest behaviour - Age, sex (Female) and protest activity (higher values=not protesting)
cd$sex.n <- as.numeric(cd$SEX)
cd$demonstration <- as.numeric(cd$V19)
cd$demonstration[cd$demonstration>4] <- NA
akpfit<- Logit(akp ~ AGE + sex.n + demonstration, pred= FALSE, data= cd)##
## Response Variable: akp
## Predictor Variable 1: AGE
## Predictor Variable 2: sex.n
## Predictor Variable 3: demonstration
##
## Number of cases (rows) of data: 1509
## Number of cases retained for analysis: 1471
##
##
##
## BASIC ANALYSIS
##
## Model Coefficients
##
## Estimate Std Err z-value p-value Lower 95% Upper 95%
## (Intercept) -1.9771 0.3233 -6.115 0.000 -2.6108 -1.3434
## AGE 0.0008 0.0004 1.978 0.048 0.0000 0.0017
## sex.n 0.1505 0.1066 1.412 0.158 -0.0584 0.3594
## demonstration 0.4029 0.0763 5.281 0.000 0.2534 0.5524
##
##
## Odds ratios and confidence intervals
##
## Odds Ratio Lower 95% Upper 95%
## (Intercept) 0.1385 0.0735 0.2610
## AGE 1.0008 1.0000 1.0017
## sex.n 1.1624 0.9433 1.4325
## demonstration 1.4962 1.2884 1.7375
##
##
## Model Fit
##
## Null deviance: 2016.414 on 1470 degrees of freedom
## Residual deviance: 1978.110 on 1467 degrees of freedom
##
## AIC: 1986.11
##
## Number of iterations to convergence: 4
##
##
## Collinearity
##
## Tolerance VIF
## AGE 0.996 1.004
## sex.n 0.999 1.001
## demonstration 0.996 1.004
##
##
##
## ANALYSIS OF RESIDUALS AND INFLUENCE
## Data, Fitted, Residual, Studentized Residual, Dffits, Cook's Distance
## [sorted by Cook's Distance]
## [res.rows = 20 out of 1471 cases (rows) of data]
## --------------------------------------------------------------------
## AGE sex.n demonstration akp fitted residual rstudent dffits cooks
## 397 999 2 4 0 0.6849 -0.6849 -1.546 -0.2565 0.020864
## 511 999 2 4 0 0.6849 -0.6849 -1.546 -0.2565 0.020864
## 728 999 2 4 0 0.6849 -0.6849 -1.546 -0.2565 0.020864
## 738 999 2 4 0 0.6849 -0.6849 -1.546 -0.2565 0.020864
## 1265 999 2 4 0 0.6849 -0.6849 -1.546 -0.2565 0.020864
## 543 999 1 4 0 0.6516 -0.6516 -1.477 -0.2534 0.019189
## 545 999 1 4 0 0.6516 -0.6516 -1.477 -0.2534 0.019189
## 1340 999 1 4 0 0.6516 -0.6516 -1.477 -0.2534 0.019189
## 549 999 2 3 0 0.5923 -0.5923 -1.363 -0.2435 0.016185
## 950 999 2 3 0 0.5923 -0.5923 -1.363 -0.2435 0.016185
## 1134 999 1 2 0 0.4552 -0.4552 -1.120 -0.2144 0.010656
## 1135 999 2 3 1 0.5923 0.4077 1.038 0.1860 0.007666
## 916 22 1 1 1 0.1970 0.8030 1.811 0.1332 0.007496
## 45 28 1 1 1 0.1978 0.8022 1.809 0.1332 0.007478
## 1221 36 1 1 1 0.1989 0.8011 1.805 0.1333 0.007457
## 485 44 1 1 1 0.1999 0.8001 1.802 0.1333 0.007441
## 1306 46 1 1 1 0.2002 0.7998 1.802 0.1334 0.007437
## 624 50 1 1 1 0.2008 0.7992 1.800 0.1334 0.007431
## 566 32 2 1 1 0.2234 0.7766 1.740 0.1342 0.007032
## 872 38 2 1 1 0.2242 0.7758 1.737 0.1342 0.007015sjp.glm(akpfit)## Waiting for profiling to be done...
chpfit<- Logit(chp ~ AGE + sex.n + demonstration, pred= FALSE, data= cd)##
## Response Variable: chp
## Predictor Variable 1: AGE
## Predictor Variable 2: sex.n
## Predictor Variable 3: demonstration
##
## Number of cases (rows) of data: 1509
## Number of cases retained for analysis: 1471
##
##
##
## BASIC ANALYSIS
##
## Model Coefficients
##
## Estimate Std Err z-value p-value Lower 95% Upper 95%
## (Intercept) -0.5318 0.3587 -1.483 0.138 -1.2347 0.1712
## AGE 0.0001 0.0006 0.123 0.902 -0.0010 0.0012
## sex.n 0.0480 0.1427 0.337 0.736 -0.2316 0.3277
## demonstration -0.3395 0.0817 -4.155 0.000 -0.4996 -0.1794
##
##
## Odds ratios and confidence intervals
##
## Odds Ratio Lower 95% Upper 95%
## (Intercept) 0.5876 0.2909 1.1867
## AGE 1.0001 0.9990 1.0012
## sex.n 1.0492 0.7932 1.3878
## demonstration 0.7121 0.6067 0.8358
##
##
## Model Fit
##
## Null deviance: 1302.229 on 1470 degrees of freedom
## Residual deviance: 1286.032 on 1467 degrees of freedom
##
## AIC: 1294.032
##
## Number of iterations to convergence: 4
##
##
## Collinearity
##
## Tolerance VIF
## AGE 0.996 1.004
## sex.n 0.999 1.001
## demonstration 0.996 1.004
##
##
##
## ANALYSIS OF RESIDUALS AND INFLUENCE
## Data, Fitted, Residual, Studentized Residual, Dffits, Cook's Distance
## [sorted by Cook's Distance]
## [res.rows = 20 out of 1471 cases (rows) of data]
## --------------------------------------------------------------------
## AGE sex.n demonstration chp fitted residual rstudent dffits cooks
## 397 999 2 4 1 0.1511 0.8489 1.9969 0.4079 0.054063
## 511 999 2 4 1 0.1511 0.8489 1.9969 0.4079 0.054063
## 728 999 2 4 1 0.1511 0.8489 1.9969 0.4079 0.054063
## 888 24 1 1 1 0.3054 0.6946 1.5480 0.1706 0.006110
## 887 26 1 1 1 0.3055 0.6945 1.5479 0.1706 0.006109
## 683 29 1 1 1 0.3055 0.6945 1.5478 0.1706 0.006107
## 889 25 2 1 1 0.3157 0.6843 1.5262 0.1700 0.005950
## 297 53 2 1 1 0.3161 0.6839 1.5254 0.1701 0.005948
## 934 53 2 1 1 0.3161 0.6839 1.5254 0.1701 0.005948
## 314 52 2 1 1 0.3161 0.6839 1.5254 0.1701 0.005948
## 343 47 2 1 1 0.3160 0.6840 1.5255 0.1700 0.005944
## 555 33 2 1 1 0.3158 0.6842 1.5260 0.1700 0.005943
## 317 46 2 1 1 0.3160 0.6840 1.5256 0.1700 0.005943
## 622 42 2 1 1 0.3160 0.6840 1.5257 0.1700 0.005942
## 618 41 2 1 1 0.3160 0.6840 1.5257 0.1700 0.005942
## 688 39 2 1 1 0.3159 0.6841 1.5258 0.1700 0.005942
## 515 40 2 1 1 0.3159 0.6841 1.5257 0.1700 0.005942
## 1134 999 1 2 0 0.2507 -0.2507 -0.7731 -0.2063 0.005406
## 335 71 1 2 1 0.2390 0.7610 1.6960 0.1209 0.003555
## 405 69 1 2 1 0.2390 0.7610 1.6960 0.1208 0.003550sjp.glm(chpfit)## Waiting for profiling to be done...
mhpfit<- Logit(mhp ~ AGE + sex.n + demonstration, pred= FALSE, data= cd)##
## Response Variable: mhp
## Predictor Variable 1: AGE
## Predictor Variable 2: sex.n
## Predictor Variable 3: demonstration
##
## Number of cases (rows) of data: 1509
## Number of cases retained for analysis: 1471
##
##
##
## BASIC ANALYSIS
##
## Model Coefficients
##
## Estimate Std Err z-value p-value Lower 95% Upper 95%
## (Intercept) -1.9447 0.6458 -3.011 0.003 -3.2105 -0.6790
## AGE -0.0058 0.0061 -0.955 0.340 -0.0178 0.0061
## sex.n -0.6182 0.2159 -2.863 0.004 -1.0415 -0.1950
## demonstration 0.1262 0.1474 0.856 0.392 -0.1627 0.4150
##
##
## Odds ratios and confidence intervals
##
## Odds Ratio Lower 95% Upper 95%
## (Intercept) 0.1430 0.0403 0.5071
## AGE 0.9942 0.9823 1.0062
## sex.n 0.5389 0.3529 0.8228
## demonstration 1.1345 0.8499 1.5144
##
##
## Model Fit
##
## Null deviance: 720.232 on 1470 degrees of freedom
## Residual deviance: 707.213 on 1467 degrees of freedom
##
## AIC: 715.2132
##
## Number of iterations to convergence: 8
##
##
## Collinearity
##
## Tolerance VIF
## AGE 0.996 1.004
## sex.n 0.999 1.001
## demonstration 0.996 1.004
##
##
##
## ANALYSIS OF RESIDUALS AND INFLUENCE
## Data, Fitted, Residual, Studentized Residual, Dffits, Cook's Distance
## [sorted by Cook's Distance]
## [res.rows = 20 out of 1471 cases (rows) of data]
## --------------------------------------------------------------------
## AGE sex.n demonstration mhp fitted residual rstudent dffits cooks
## 626 26 2 1 1 0.03890 0.9611 2.581 0.3042 0.04207
## 971 23 1 1 1 0.07101 0.9290 2.332 0.3544 0.03717
## 823 29 1 1 1 0.06873 0.9313 2.345 0.3473 0.03652
## 838 20 2 2 1 0.04540 0.9546 2.504 0.2304 0.02158
## 819 54 1 2 1 0.06747 0.9325 2.338 0.2468 0.01869
## 351 81 1 4 1 0.07366 0.9263 2.299 0.2466 0.01755
## 1372 75 1 3 1 0.06768 0.9323 2.335 0.2320 0.01647
## 595 38 1 2 1 0.07360 0.9264 2.298 0.2349 0.01593
## 281 77 1 4 1 0.07528 0.9247 2.288 0.2298 0.01501
## 196 59 2 3 1 0.04119 0.9588 2.536 0.1722 0.01293
## 818 57 2 3 1 0.04165 0.9583 2.531 0.1678 0.01218
## 826 56 2 3 1 0.04189 0.9581 2.528 0.1657 0.01183
## 842 61 2 4 1 0.04595 0.9540 2.491 0.1673 0.01128
## 821 54 2 3 1 0.04236 0.9576 2.523 0.1618 0.01118
## 505 69 1 4 1 0.07860 0.9214 2.265 0.1977 0.01078
## 360 59 2 4 1 0.04647 0.9535 2.486 0.1624 0.01055
## 740 59 2 4 1 0.04647 0.9535 2.486 0.1624 0.01055
## 183 58 2 4 1 0.04673 0.9533 2.483 0.1601 0.01021
## 583 58 2 4 1 0.04673 0.9533 2.483 0.1601 0.01021
## 1014 50 2 3 1 0.04332 0.9567 2.514 0.1550 0.01010sjp.glm(mhpfit)## Waiting for profiling to be done...
bdpfit<- Logit(bdp ~ AGE + sex.n + demonstration, pred= FALSE, data= cd)##
## Response Variable: bdp
## Predictor Variable 1: AGE
## Predictor Variable 2: sex.n
## Predictor Variable 3: demonstration
##
## Number of cases (rows) of data: 1509
## Number of cases retained for analysis: 1471
##
##
##
## BASIC ANALYSIS
##
## Model Coefficients
##
## Estimate Std Err z-value p-value Lower 95% Upper 95%
## (Intercept) -1.3514 0.5122 -2.638 0.008 -2.3553 -0.3475
## AGE -0.0020 0.0018 -1.120 0.263 -0.0055 0.0015
## sex.n -0.4643 0.1959 -2.371 0.018 -0.8482 -0.0805
## demonstration -0.0902 0.1194 -0.755 0.450 -0.3243 0.1439
##
##
## Odds ratios and confidence intervals
##
## Odds Ratio Lower 95% Upper 95%
## (Intercept) 0.2589 0.0949 0.7065
## AGE 0.9980 0.9945 1.0015
## sex.n 0.6285 0.4282 0.9227
## demonstration 0.9138 0.7231 1.1548
##
##
## Model Fit
##
## Null deviance: 816.814 on 1470 degrees of freedom
## Residual deviance: 807.853 on 1467 degrees of freedom
##
## AIC: 815.8531
##
## Number of iterations to convergence: 6
##
##
## Collinearity
##
## Tolerance VIF
## AGE 0.996 1.004
## sex.n 0.999 1.001
## demonstration 0.996 1.004
##
##
##
## ANALYSIS OF RESIDUALS AND INFLUENCE
## Data, Fitted, Residual, Studentized Residual, Dffits, Cook's Distance
## [sorted by Cook's Distance]
## [res.rows = 20 out of 1471 cases (rows) of data]
## --------------------------------------------------------------------
## AGE sex.n demonstration bdp fitted residual rstudent dffits cooks
## 1265 999 2 4 1 0.009673 0.9903 3.495 0.7089 0.754976
## 1487 21 2 1 1 0.082260 0.9177 2.257 0.2826 0.024421
## 1043 25 2 1 1 0.081661 0.9183 2.260 0.2818 0.024395
## 1252 29 2 1 1 0.081066 0.9189 2.263 0.2810 0.024391
## 817 59 1 1 1 0.116777 0.8832 2.093 0.2981 0.021442
## 1484 43 1 1 1 0.120101 0.8799 2.079 0.2974 0.020948
## 1250 29 1 1 1 0.123077 0.8769 2.067 0.2986 0.020783
## 1085 20 2 2 1 0.075843 0.9242 2.282 0.1940 0.012174
## 820 22 2 2 1 0.075564 0.9244 2.283 0.1935 0.012139
## 798 31 2 2 1 0.074323 0.9257 2.291 0.1917 0.012046
## 1495 65 1 2 1 0.106650 0.8933 2.125 0.1998 0.010233
## 1474 18 1 2 1 0.115895 0.8841 2.085 0.2011 0.009804
## 1078 21 1 2 1 0.115284 0.8847 2.088 0.2001 0.009749
## 1097 23 1 2 1 0.114879 0.8851 2.090 0.1996 0.009718
## 1156 45 1 2 1 0.110502 0.8895 2.108 0.1969 0.009707
## 1074 24 1 2 1 0.114677 0.8853 2.090 0.1993 0.009704
## 1161 42 1 2 1 0.111090 0.8889 2.106 0.1969 0.009673
## 808 27 1 2 1 0.114072 0.8859 2.093 0.1986 0.009671
## 784 38 1 2 1 0.111879 0.8881 2.102 0.1971 0.009644
## 1491 38 1 2 1 0.111879 0.8881 2.102 0.1971 0.009644sjp.glm(bdpfit)## Waiting for profiling to be done...