- One of the dependent variables for one of my statistical tests, 'distvol' is a variable measuring the ideological distance of voter party preference shifts during an election campaign. 'Distvol' can take any integer value from 0 to 7.
- For example, a respondent with a 'distvol' score of 7 indicates that they switched their vote preference from the farthest left party to the farthest right party, while a score of 1 indicates they switched their preference between two ideologically close parties.
- Most respondents, however, did not switch their preference, meaning that they got a 'distvol' value of 0. The distribution of 'distvol' therefore has a lot of zeros in it (see below).
- For some context, my research is looking at the impact of voters' online media consumption on changes in their party preference ("electoral volatility") during an election campaign. I hypothesise that those who consume more online media will switch to ideologically closer parties than those who consume less online media.
- Which type of regression model do you think would work best here? Below are the options I'm considering
- Both poisson and negative binomial regression would work with this distribution, I'm fairly sure, even though the data is not true count data, as the only other similar study I've seen using this data used a negative binomial.
- Generalised linear model with family(bin 10) and link(logit) - This is very similar to negative binomial, and is bounded at the high end (unlike negative binomial).
- Ordinal logit - Some have suggested that the distribution would better suit an ologit, since it's not technically count data.
- (Incidentally, all of the different types of models do give very similar results, shown below).
- Second, would you be able to give me any advice on how to go about comparing the performance of the different models/tests to justify which model to choose.
Code:
tab distvol
distvol | Freq. Percent Cum.
------------+-----------------------------------
0 | 1,304 89.38 89.38
1 | 33 2.26 91.64
2 | 57 3.91 95.54
3 | 29 1.99 97.53
4 | 14 0.96 98.49
5 | 14 0.96 99.45
6 | 5 0.34 99.79
7 | 3 0.21 100.00
------------+-----------------------------------
Total | 1,459 100.00Code:
Fitting Poisson model:
Iteration 0: log pseudolikelihood = -829.75972
Iteration 1: log pseudolikelihood = -829.64878
Iteration 2: log pseudolikelihood = -829.64845
Iteration 3: log pseudolikelihood = -829.64845
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -743.19607 (not concave)
Iteration 1: log pseudolikelihood = -606.19316
Iteration 2: log pseudolikelihood = -599.21043
Iteration 3: log pseudolikelihood = -599.19066
Iteration 4: log pseudolikelihood = -599.19066
Fitting full model:
Iteration 0: log pseudolikelihood = -587.16259
Iteration 1: log pseudolikelihood = -584.36333
Iteration 2: log pseudolikelihood = -584.11853
Iteration 3: log pseudolikelihood = -584.11808
Iteration 4: log pseudolikelihood = -584.11808
Negative binomial regression Number of obs = 1,344
Wald chi2(13) = 48.32
Dispersion = mean Prob > chi2 = 0.0000
Log pseudolikelihood = -584.11808 Pseudo R2 = 0.0252
-----------------------------------------------------------------------------------------------
| Robust
distvol | Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
onlinemed |
high exposure | .1395064 .3363322 0.41 0.678 -.5196926 .7987054
|
highknow |
high knowledge | .2282583 .3450022 0.66 0.508 -.4479336 .9044502
|
onlinemed#highknow |
high exposure#high knowledge | -1.243398 .5446583 -2.28 0.022 -2.310909 -.1758877
|
socialmedia | -.0270818 .0775339 -0.35 0.727 -.1790455 .1248819
age_i | -.0141096 .008021 -1.76 0.079 -.0298306 .0016113
female | -.6162776 .223444 -2.76 0.006 -1.05422 -.1783354
highincome | -.0716774 .2397122 -0.30 0.765 -.5415048 .3981499
partyclose_binary | -1.497653 .4460845 -3.36 0.001 -2.371963 -.6233437
leftright_i | -.0361103 .0507479 -0.71 0.477 -.1355744 .0633539
farlr | -.5992872 .3480854 -1.72 0.085 -1.281522 .0829477
political_mood | .008102 .0098578 0.82 0.411 -.011219 .027423
networkhet12_i | .1115985 .0556922 2.00 0.045 .0024438 .2207531
nptvnews | -.0467189 .0517432 -0.90 0.367 -.1481338 .054696
_cons | -.484885 .5823316 -0.83 0.405 -1.626234 .656464
------------------------------+----------------------------------------------------------------
/lnalpha | 2.487341 .1500471 2.193254 2.781428
------------------------------+----------------------------------------------------------------
alpha | 12.02925 1.804953 8.964335 16.14205
-----------------------------------------------------------------------------------------------Code:
Iteration 0: log pseudolikelihood = -829.75972
Iteration 1: log pseudolikelihood = -829.64878
Iteration 2: log pseudolikelihood = -829.64845
Iteration 3: log pseudolikelihood = -829.64845
Poisson regression Number of obs = 1,344
Wald chi2(13) = 42.61
Log pseudolikelihood = -829.64845 Prob > chi2 = 0.0001
-----------------------------------------------------------------------------------------------
| Robust
distvol | Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
onlinemed |
high exposure | .1520361 .3637021 0.42 0.676 -.560807 .8648792
|
highknow |
high knowledge | .1991359 .3109782 0.64 0.522 -.4103702 .8086421
|
onlinemed#highknow |
high exposure#high knowledge | -1.217585 .5514483 -2.21 0.027 -2.298404 -.1367663
|
socialmedia | -.0105604 .0821744 -0.13 0.898 -.1716192 .1504983
age_i | -.0106576 .0081525 -1.31 0.191 -.0266363 .005321
female | -.4278774 .258046 -1.66 0.097 -.9336382 .0778834
highincome | .0076763 .2400212 0.03 0.974 -.4627566 .4781092
partyclose_binary | -1.3393 .5511951 -2.43 0.015 -2.419622 -.2589773
leftright_i | -.0679288 .0469811 -1.45 0.148 -.16001 .0241524
farlr | -.5089138 .3878363 -1.31 0.189 -1.269059 .2512312
political_mood | .0072836 .0089259 0.82 0.414 -.0102109 .024778
networkhet12_i | .080292 .0549722 1.46 0.144 -.0274516 .1880356
nptvnews | -.0447907 .041423 -1.08 0.280 -.1259783 .036397
_cons | -.4876739 .6399943 -0.76 0.446 -1.74204 .766692
-----------------------------------------------------------------------------------------------Code:
Iteration 0: log pseudolikelihood = -928.17693
Iteration 1: log pseudolikelihood = -861.78275
Iteration 2: log pseudolikelihood = -860.77761
Iteration 3: log pseudolikelihood = -860.76821
Iteration 4: log pseudolikelihood = -860.76821
Generalized linear models Number of obs = 1,344
Optimization : ML Residual df = 1,330
Scale parameter = 1
Deviance = 1437.49301 (1/df) Deviance = 1.080822
Pearson = 3385.188563 (1/df) Pearson = 2.545255
Variance function: V(u) = u*(1-u/10) [Binomial]
Link function : g(u) = ln(u/(10-u)) [Logit]
AIC = 1.301738
Log pseudolikelihood = -860.7682062 BIC = -8143.036
-----------------------------------------------------------------------------------------------
| Robust
distvol | Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
onlinemed |
high exposure | .157355 .3766299 0.42 0.676 -.580826 .8955359
|
highknow |
high knowledge | .2063968 .321822 0.64 0.521 -.4243627 .8371564
|
onlinemed#highknow |
high exposure#high knowledge | -1.247359 .5654925 -2.21 0.027 -2.355704 -.1390137
|
socialmedia | -.0108449 .0848111 -0.13 0.898 -.1770717 .1553818
age_i | -.0109973 .0084091 -1.31 0.191 -.0274789 .0054843
female | -.4410633 .2655512 -1.66 0.097 -.961534 .0794074
highincome | .0072811 .2479066 0.03 0.977 -.478607 .4931691
partyclose_binary | -1.363248 .5564878 -2.45 0.014 -2.453944 -.2725517
leftright_i | -.0700498 .0484818 -1.44 0.148 -.1650723 .0249728
farlr | -.5202852 .3958464 -1.31 0.189 -1.29613 .2555595
political_mood | .0075074 .0092249 0.81 0.416 -.010573 .0255878
networkhet12_i | .0829316 .0567113 1.46 0.144 -.0282206 .1940837
nptvnews | -.0463583 .0428227 -1.08 0.279 -.1302892 .0375726
_cons | -2.734244 .6614316 -4.13 0.000 -4.030626 -1.437862
-----------------------------------------------------------------------------------------------Code:
Iteration 0: log pseudolikelihood = -564.33681
Iteration 1: log pseudolikelihood = -546.23464
Iteration 2: log pseudolikelihood = -544.42317
Iteration 3: log pseudolikelihood = -544.39738
Iteration 4: log pseudolikelihood = -544.39733
Iteration 5: log pseudolikelihood = -544.39733
Ordered logistic regression Number of obs = 1,344
Wald chi2(13) = 36.22
Prob > chi2 = 0.0005
Log pseudolikelihood = -544.39733 Pseudo R2 = 0.0353
-----------------------------------------------------------------------------------------------
| Robust
distvol | Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
onlinemed |
high exposure | .0758672 .3551337 0.21 0.831 -.620182 .7719163
|
highknow |
high knowledge | .2898168 .3289096 0.88 0.378 -.3548342 .9344678
|
onlinemed#highknow |
high exposure#high knowledge | -1.119558 .560211 -2.00 0.046 -2.217552 -.021565
|
socialmedia | -.0388585 .0819758 -0.47 0.635 -.1995281 .1218111
age_i | -.0161103 .0093542 -1.72 0.085 -.0344442 .0022236
female | -.4300516 .2629844 -1.64 0.102 -.9454915 .0853883
highincome | -.0384396 .2422341 -0.16 0.874 -.5132098 .4363305
partyclose_binary | -1.346552 .6240141 -2.16 0.031 -2.569597 -.1235066
leftright_i | -.0849347 .053184 -1.60 0.110 -.1891734 .0193041
farlr | -.5247534 .3937558 -1.33 0.183 -1.296501 .2469938
political_mood | .0027973 .0085472 0.33 0.743 -.0139549 .0195495
networkhet12_i | .0695643 .0470031 1.48 0.139 -.0225601 .1616888
nptvnews | -.0395345 .046652 -0.85 0.397 -.1309707 .0519016
------------------------------+----------------------------------------------------------------
/cut1 | .6980686 .7310941 -.7348496 2.130987
/cut2 | .9451905 .7192096 -.4644345 2.354816
/cut3 | 1.757095 .7208377 .3442787 3.169911
/cut4 | 2.558683 .7413626 1.105639 4.011727
/cut5 | 3.153227 .7592492 1.665126 4.641328
/cut6 | 4.411359 .8810093 2.684612 6.138105
/cut7 | 6.057117 1.033805 4.030896 8.083338
-----------------------------------------------------------------------------------------------
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