04 - Statistical Testing

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Set-up

Load necessary packages

library(tidyverse)
library(survey)
library(srvyr)
library(srvyrexploR)

Load in data and preview it

glimpse(recs_2020)

Rows: 18,496
Columns: 100
$ DOEID            <dbl> 100001, 100002, 100003, 100004, 100005, 100006, 10000…
$ ClimateRegion_BA <fct> Mixed-Dry, Mixed-Humid, Mixed-Dry, Mixed-Humid, Mixed…
$ Urbanicity       <fct> Urban Area, Urban Area, Urban Area, Urban Area, Urban…
$ Region           <fct> West, South, West, South, Northeast, South, South, So…
$ REGIONC          <chr> "WEST", "SOUTH", "WEST", "SOUTH", "NORTHEAST", "SOUTH…
$ Division         <fct> Mountain South, West South Central, Mountain South, S…
$ STATE_FIPS       <chr> "35", "05", "35", "45", "34", "48", "40", "28", "11",…
$ state_postal     <fct> NM, AR, NM, SC, NJ, TX, OK, MS, DC, AZ, CA, TX, LA, M…
$ state_name       <fct> New Mexico, Arkansas, New Mexico, South Carolina, New…
$ HDD65            <dbl> 3844, 3766, 3819, 2614, 4219, 901, 3148, 1825, 4233, …
$ CDD65            <dbl> 1679, 1458, 1696, 1718, 1363, 3558, 2128, 2374, 1159,…
$ HDD30YR          <dbl> 4451, 4429, 4500, 3229, 4896, 1150, 3564, 2660, 4404,…
$ CDD30YR          <dbl> 1027, 1305, 1010, 1653, 1059, 3588, 2043, 2164, 1407,…
$ HousingUnitType  <fct> Single-family detached, Apartment: 5 or more units, A…
$ YearMade         <ord> 1970-1979, 1980-1989, 1960-1969, 1980-1989, 1960-1969…
$ TOTSQFT_EN       <dbl> 2100, 590, 900, 2100, 800, 4520, 2100, 900, 750, 760,…
$ TOTHSQFT         <dbl> 2100, 590, 900, 2100, 800, 3010, 1200, 900, 750, 760,…
$ TOTCSQFT         <dbl> 2100, 590, 900, 2100, 800, 3010, 1200, 0, 500, 760, 1…
$ SpaceHeatingUsed <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…
$ ACUsed           <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE…
$ HeatingBehavior  <fct> Set one temp and leave it, Turn on or off as needed, …
$ WinterTempDay    <dbl> 70, 70, 69, 68, 68, 76, 74, 70, 68, 70, 72, 74, 74, 7…
$ WinterTempAway   <dbl> 70, 65, 68, 68, 68, 76, 65, 70, 60, 70, 70, 74, 74, 7…
$ WinterTempNight  <dbl> 68, 65, 67, 68, 68, 68, 74, 68, 62, 68, 72, 74, 74, 6…
$ ACBehavior       <fct> Set one temp and leave it, Turn on or off as needed, …
$ SummerTempDay    <dbl> 71, 68, 70, 72, 72, 69, 68, NA, 72, 74, 77, 77, 74, 6…
$ SummerTempAway   <dbl> 71, 68, 68, 72, 72, 74, 70, NA, 76, 74, 77, 77, 74, 6…
$ SummerTempNight  <dbl> 71, 68, 68, 72, 72, 68, 70, NA, 68, 72, 77, 77, 74, 6…
$ NWEIGHT          <dbl> 3284.104, 9007.387, 5669.002, 5294.239, 9935.465, 724…
$ NWEIGHT1         <dbl> 3273.053, 9019.564, 5793.353, 5361.146, 10047.545, 73…
$ NWEIGHT2         <dbl> 3349.139, 9081.268, 5913.554, 5361.706, 10261.682, 74…
$ NWEIGHT3         <dbl> 3344.876, 9020.409, 5762.743, 5371.011, 10036.522, 73…
$ NWEIGHT4         <dbl> 3437.284, 9213.074, 5870.261, 5392.846, 9960.953, 742…
$ NWEIGHT5         <dbl> 3415.582, 9117.337, 5720.669, 5327.617, 10107.863, 73…
$ NWEIGHT6         <dbl> 3354.813, 9178.697, 5662.939, 5353.957, 10298.428, 74…
$ NWEIGHT7         <dbl> 3372.342, 9095.936, 5699.536, 5325.316, 10064.709, 73…
$ NWEIGHT8         <dbl> 3364.035, 8920.480, 5704.027, 5375.732, 10096.509, 73…
$ NWEIGHT9         <dbl> 3361.912, 9188.981, 5667.670, 5391.379, 10321.424, 73…
$ NWEIGHT10        <dbl> 3301.569, 9060.009, 5793.325, 5500.628, 9943.547, 731…
$ NWEIGHT11        <dbl> 3211.291, 9127.404, 5806.321, 5427.320, 10266.593, 73…
$ NWEIGHT12        <dbl> 3500.495, 9264.304, 5650.394, 5384.442, 10127.061, 73…
$ NWEIGHT13        <dbl> 3313.754, 9222.011, 5648.461, 5302.085, 10240.975, 72…
$ NWEIGHT14        <dbl> 3359.110, 9199.014, 5828.712, 5362.226, 9871.649, 740…
$ NWEIGHT15        <dbl> 3423.682, 9143.214, 5641.887, 5383.136, 10275.303, 74…
$ NWEIGHT16        <dbl> 3383.601, 9042.382, 5717.847, 5380.916, 9921.199, 738…
$ NWEIGHT17        <dbl> 3312.112, 9416.815, 5968.713, 5418.300, 10311.952, 73…
$ NWEIGHT18        <dbl> 3324.383, 9162.681, 5828.370, 5356.271, 10004.213, 74…
$ NWEIGHT19        <dbl> 3366.644, 9191.950, 5814.049, 5343.187, 10437.297, 75…
$ NWEIGHT20        <dbl> 3326.643, 9091.550, 5697.447, 5360.409, 10100.730, 73…
$ NWEIGHT21        <dbl> 3339.910, 0.000, 5686.769, 5336.323, 9981.635, 7427.5…
$ NWEIGHT22        <dbl> 3292.197, 9097.877, 5738.946, 5389.830, 10000.278, 73…
$ NWEIGHT23        <dbl> 3277.697, 9319.896, 5944.649, 5397.093, 10179.723, 71…
$ NWEIGHT24        <dbl> 3340.406, 9080.729, 5819.996, 5448.089, 9825.700, 746…
$ NWEIGHT25        <dbl> 3386.445, 9406.487, 5823.075, 5382.111, 10149.386, 72…
$ NWEIGHT26        <dbl> 3300.574, 9255.867, 5650.188, 5386.710, 0.000, 7309.1…
$ NWEIGHT27        <dbl> 3311.546, 9318.078, 5862.116, 5351.082, 10140.604, 72…
$ NWEIGHT28        <dbl> 3347.637, 9154.189, 5706.909, 5371.439, 9948.403, 750…
$ NWEIGHT29        <dbl> 3355.638, 9371.695, 5618.615, 5361.572, 10064.708, 73…
$ NWEIGHT30        <dbl> 3322.423, 9137.197, 5795.544, 5381.218, 10082.927, 73…
$ NWEIGHT31        <dbl> 3255.840, 9233.363, 5994.544, 5319.728, 10132.977, 73…
$ NWEIGHT32        <dbl> 3317.937, 9114.608, 0.000, 5338.558, 9978.370, 7302.5…
$ NWEIGHT33        <dbl> 3401.811, 9176.872, 5637.872, 0.000, 10213.075, 7326.…
$ NWEIGHT34        <dbl> 3363.592, 9191.207, 5619.040, 5379.523, 9964.337, 724…
$ NWEIGHT35        <dbl> 3303.528, 9100.344, 5652.289, 5363.277, 10070.847, 0.…
$ NWEIGHT36        <dbl> 3333.027, 9071.530, 5834.171, 5476.866, 9987.947, 735…
$ NWEIGHT37        <dbl> 3389.869, 9263.141, 5712.198, 5386.333, 10120.314, 73…
$ NWEIGHT38        <dbl> 3381.503, 9077.901, 5765.422, 5326.402, 10023.636, 73…
$ NWEIGHT39        <dbl> 3328.893, 9011.009, 5887.338, 5420.540, 10023.919, 73…
$ NWEIGHT40        <dbl> 3292.829, 9166.222, 5649.809, 5370.189, 10184.527, 73…
$ NWEIGHT41        <dbl> 3295.089, 9091.334, 5957.748, 5339.323, 10069.084, 73…
$ NWEIGHT42        <dbl> 3413.593, 9193.664, 5592.619, 5328.788, 9958.721, 743…
$ NWEIGHT43        <dbl> 3263.710, 9215.337, 6035.472, 5409.435, 10352.485, 73…
$ NWEIGHT44        <dbl> 3342.446, 9048.092, 5732.384, 5416.488, 10091.807, 74…
$ NWEIGHT45        <dbl> 3275.274, 9258.580, 5876.696, 5453.165, 10227.529, 74…
$ NWEIGHT46        <dbl> 3364.248, 9170.518, 5653.511, 5449.444, 10069.384, 73…
$ NWEIGHT47        <dbl> 3336.066, 9260.064, 5763.458, 5375.551, 9995.686, 732…
$ NWEIGHT48        <dbl> 3329.151, 9105.220, 5928.968, 5407.834, 10197.608, 73…
$ NWEIGHT49        <dbl> 3348.061, 9116.891, 5772.333, 5399.779, 10093.620, 74…
$ NWEIGHT50        <dbl> 3357.231, 9261.127, 5785.452, 5359.408, 10196.251, 73…
$ NWEIGHT51        <dbl> 3335.188, 8955.288, 5635.561, 5447.619, 10017.094, 73…
$ NWEIGHT52        <dbl> 3240.132, 9000.296, 5944.330, 5344.453, 9954.142, 749…
$ NWEIGHT53        <dbl> 3429.728, 9290.375, 5683.500, 5437.803, 10050.961, 74…
$ NWEIGHT54        <dbl> 3294.084, 9199.326, 5735.631, 5377.898, 10018.844, 72…
$ NWEIGHT55        <dbl> 3397.713, 8958.782, 5674.564, 5357.470, 10309.728, 75…
$ NWEIGHT56        <dbl> 3292.610, 9232.597, 5660.854, 5421.025, 10142.763, 73…
$ NWEIGHT57        <dbl> 0.000, 9140.427, 5917.193, 5365.230, 10176.828, 7383.…
$ NWEIGHT58        <dbl> 3369.768, 9306.997, 5571.015, 5402.057, 10043.143, 75…
$ NWEIGHT59        <dbl> 3358.163, 9061.782, 5887.092, 5402.772, 10247.911, 73…
$ NWEIGHT60        <dbl> 3404.031, 8957.915, 5837.846, 5350.860, 10110.301, 75…
$ BTUEL            <dbl> 42723.28, 17889.29, 8146.63, 31646.53, 20027.42, 4896…
$ DOLLAREL         <dbl> 1955.06, 713.27, 334.51, 1424.86, 1087.00, 1895.66, 1…
$ BTUNG            <dbl> 101924.43, 10145.32, 22603.08, 55118.66, 39099.51, 36…
$ DOLLARNG         <dbl> 701.8300, 261.7348, 188.1400, 636.9100, 376.0400, 439…
$ BTULP            <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ DOLLARLP         <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ BTUFO            <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ DOLLARFO         <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,…
$ BTUWOOD          <dbl> 0, 0, 0, 0, 0, 3000, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ TOTALBTU         <dbl> 144647.71, 28034.61, 30749.71, 86765.19, 59126.93, 85…
$ TOTALDOL         <dbl> 2656.8900, 975.0048, 522.6500, 2061.7700, 1463.0400, …

glimpse(anes_2020)

Rows: 7,453
Columns: 65
$ V200001                 <dbl> 200015, 200022, 200039, 200046, 200053, 200060…
$ CaseID                  <dbl> 200015, 200022, 200039, 200046, 200053, 200060…
$ V200002                 <hvn_lbll> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…
$ InterviewMode           <fct> Web, Web, Web, Web, Web, Web, Web, Web, Web, W…
$ V200010b                <dbl> 1.0057375, 1.1634731, 0.7686811, 0.5210195, 0.…
$ Weight                  <dbl> 1.0057375, 1.1634731, 0.7686811, 0.5210195, 0.…
$ V200010c                <dbl> 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2…
$ VarUnit                 <fct> 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2…
$ V200010d                <dbl> 9, 26, 41, 29, 23, 37, 7, 37, 32, 41, 22, 7, 3…
$ Stratum                 <fct> 9, 26, 41, 29, 23, 37, 7, 37, 32, 41, 22, 7, 3…
$ V201006                 <hvn_lbll> 2, 3, 2, 3, 2, 1, 2, 3, 2, 2, 2, 2, 2, 1,…
$ CampaignInterest        <fct> Somewhat interested, Not much interested, Some…
$ V201023                 <hvn_lbll> -1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1…
$ EarlyVote2020           <fct> NA, NA, NA, NA, NA, NA, NA, NA, Yes, NA, NA, N…
$ V201024                 <hvn_lbll> -1, -1, -1, -1, -1, -1, -1, -1, 2, -1, -1…
$ V201025x                <hvn_lbll> 3, 3, 3, 3, 3, 3, 3, 2, 4, 3, 3, 3, 2, 4,…
$ V201028                 <hvn_lbll> -1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1…
$ V201029                 <hvn_lbll> -1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1…
$ V201101                 <hvn_lbll> -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 2, …
$ V201102                 <hvn_lbll> 1, 1, 1, 1, 1, 2, 1, 2, -1, -1, -1, 1, 2,…
$ VotedPres2016           <fct> Yes, Yes, Yes, Yes, Yes, No, Yes, No, Yes, Yes…
$ V201103                 <hvn_lbll> 2, 5, 1, 1, 2, -1, 5, -1, 1, 1, -1, 1, -1…
$ VotedPres2016_selection <fct> Trump, Other, Clinton, Clinton, Trump, NA, Oth…
$ V201228                 <hvn_lbll> 2, 5, 3, 2, 3, 3, 2, 2, 3, 1, 1, 1, 2, 1,…
$ V201229                 <hvn_lbll> 1, -1, -1, 2, -1, -1, 2, 2, -1, 2, 1, 2, …
$ V201230                 <hvn_lbll> -1, 2, 3, -1, 2, 3, -1, -1, 2, -1, -1, -1…
$ V201231x                <hvn_lbll> 7, 4, 3, 6, 4, 3, 6, 6, 4, 2, 1, 2, 7, 2,…
$ PartyID                 <fct> Strong republican, Independent, Independent-de…
$ V201233                 <hvn_lbll> 5, 5, 4, 3, 5, 4, 4, 1, 3, 3, 2, 3, 4, 5,…
$ TrustGovernment         <fct> Never, Never, Some of the time, About half the…
$ V201237                 <hvn_lbll> 3, 4, 4, 2, 4, 2, 4, 1, 3, 2, 4, 3, 4, 3,…
$ TrustPeople             <fct> About half the time, Some of the time, Some of…
$ V201507x                <hvn_lbll> 46, 37, 40, 41, 72, 71, 37, 45, 70, 43, 3…
$ Age                     <dbl> 46, 37, 40, 41, 72, 71, 37, 45, 70, 43, 37, 55…
$ AgeGroup                <fct> 40-49, 30-39, 40-49, 40-49, 70 or older, 70 or…
$ V201510                 <hvn_lbll> 6, 3, 2, 4, 8, 3, 4, 2, 2, 4, 2, 2, 2, 7,…
$ Education               <fct> Bachelor's, Post HS, High school, Post HS, Gra…
$ V201546                 <hvn_lbll> 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2,…
$ V201547a                <hvn_lbll> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -…
$ V201547b                <hvn_lbll> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -…
$ V201547c                <hvn_lbll> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -…
$ V201547d                <hvn_lbll> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -…
$ V201547e                <hvn_lbll> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -…
$ V201547z                <hvn_lbll> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -…
$ V201549x                <hvn_lbll> 3, 4, 1, 4, 5, 1, 1, 1, 1, 3, 3, 1, 1, 4,…
$ RaceEth                 <fct> "Hispanic", "Asian, NH/PI", "White", "Asian, N…
$ V201600                 <hvn_lbll> 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1,…
$ Gender                  <fct> Male, Female, Female, Male, Male, Female, Fema…
$ V201607                 <hvn_lbll> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -…
$ V201610                 <hvn_lbll> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -…
$ V201611                 <hvn_lbll> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -…
$ V201613                 <hvn_lbll> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -…
$ V201615                 <hvn_lbll> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -…
$ V201616                 <hvn_lbll> -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -…
$ V201617x                <hvn_lbll> 21, 13, 17, 7, 22, 3, 4, 3, 10, 11, 9, 18…
$ Income                  <fct> "$175,000-249,999", "$70,000-74,999", "$100,00…
$ Income7                 <fct> $125k or more, $60k to < 80k, $100k to < 125k,…
$ V202051                 <hvn_lbll> -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1…
$ V202066                 <hvn_lbll> 1, 4, 4, 4, 4, 4, 4, 1, -1, 4, 4, 4, 4, -…
$ V202072                 <hvn_lbll> -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1,…
$ VotedPres2020           <fct> NA, Yes, Yes, Yes, Yes, Yes, Yes, NA, Yes, Yes…
$ V202073                 <hvn_lbll> -1, 3, 1, 1, 2, 1, 2, -1, -1, 1, 1, 1, 2,…
$ V202109x                <hvn_lbll> 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,…
$ V202110x                <hvn_lbll> -1, 3, 1, 1, 2, 1, 2, -1, 1, 1, 1, 1, 2, …
$ VotedPres2020_selection <fct> NA, Other, Biden, Biden, Trump, Biden, Trump, …

Find codebooks here:

• RECS 2020
• ANES 2020

Create design objects for usage

anes_des <- anes_2020 %>%
  mutate(Weight = V200010b / sum(V200010b) * 231034125) %>%
  as_survey_design(
    weights = Weight,
    strata = V200010d,
    ids = V200010c,
    nest = TRUE
  )

recs_des <- recs_2020 %>%
  as_survey_rep(
    weights = NWEIGHT,
    repweights = NWEIGHT1:NWEIGHT60,
    type = "JK1",
    scale = 59 / 60,
    mse = TRUE
  )

Exercises - Part 1

1. For households that use thermostats, they are asked what temperature they set their home during the daytime in the winter (WinterTempDay) and in the summer (SummerTempDay). Test whether daytime winter and daytime summer temperatures of homes are set the same.

# Hint - you'll need the recs_des object

2. In 2015, the average household spent $1,856 on energy expenditure¹. Accounting for inflation, this would be $2,036 in 2020. Test whether energy costs (TOTALDOL) have increased in 2020 from 2015, accounting for inflation.

# Hint - you'll need the recs_des object

3. Advanced bonus exercise Look through the RECS dataset and develop your own hypothesis about a mean or difference of means and test it!

Exercises - Part 2

4. Is there a relationship between party identification (PartyID) and whether people voted early (EarlyVote2020)?

# Hint - you'll need the anes_des object

5. Is there a relationship between party identification (PartyID) and trust in the government (TrustGovernment)? Use Wald as the statistic option.

# Hint - you'll need the anes_des object

6. Is there a difference in the distribution of gender (Gender) across early voting status (EarlyVote2020)?

# Hint - you'll need the anes_des object

7. Advanced bonus exercise Look through the ANES dataset and develop your own hypothesis and test it!

Solutions

See the solutions

Exercises - Part 1

1. For households that use thermostats, they are asked what temperature they set their home during the daytime in the winter (WinterTempDay) and in the summer (SummerTempDay). Test whether daytime winter and daytime summer temperatures of homes are set the same.

recs_des %>%
  filter(!is.na(SummerTempDay), !is.na(WinterTempDay)) %>%
  svyttest(WinterTempDay - SummerTempDay ~ 0, design = .)

    Design-based one-sample t-test

data:  WinterTempDay - SummerTempDay ~ 0
t = -43.106, df = 58, p-value < 2.2e-16
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 -2.031155 -1.850883
sample estimates:
     mean 
-1.941019 

2. In 2015, the average household spent $1,856 on energy expenditure². Accounting for inflation, this would be $2,036 in 2020. Test whether energy costs (TOTALDOL) have increased in 2020 from 2015, accounting for inflation.

recs_des %>%
  svyttest(I(TOTALDOL - 2036) ~ 0, design = .)

    Design-based one-sample t-test

data:  I(TOTALDOL - 2036) ~ 0
t = -23.079, df = 58, p-value < 2.2e-16
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 -164.7926 -138.4885
sample estimates:
     mean 
-151.6406 

Exercises - Part 2

4. Is there a relationship between party identification (PartyID) and whether people voted early (EarlyVote2020)?

anes_des %>%
  filter(!is.na(PartyID), !is.na(EarlyVote2020)) %>%
  group_by(PartyID, EarlyVote2020) %>%
  summarize(
    p = survey_mean()
  )

# A tibble: 14 × 4
# Groups:   PartyID [7]
   PartyID                    EarlyVote2020     p   p_se
   <fct>                      <fct>         <dbl>  <dbl>
 1 Strong democrat            Yes           0.777 0.0519
 2 Strong democrat            No            0.223 0.0519
 3 Not very strong democrat   Yes           0.672 0.134 
 4 Not very strong democrat   No            0.328 0.134 
 5 Independent-democrat       Yes           0.665 0.0608
 6 Independent-democrat       No            0.335 0.0608
 7 Independent                Yes           0.695 0.0760
 8 Independent                No            0.305 0.0760
 9 Independent-republican     Yes           0.776 0.0936
10 Independent-republican     No            0.224 0.0936
11 Not very strong republican Yes           0.641 0.112 
12 Not very strong republican No            0.359 0.112 
13 Strong republican          Yes           0.703 0.0678
14 Strong republican          No            0.297 0.0678

anes_des %>%
  filter(!is.na(PartyID), !is.na(EarlyVote2020)) %>%
  svychisq(~ EarlyVote2020 + PartyID, design = .)

    Pearson's X^2: Rao & Scott adjustment

data:  NextMethod()
F = 0.46943, ndf = 4.9476, ddf = 232.5385, p-value = 0.797

5. Is there a relationship between party identification (PartyID) and trust in the government (TrustGovernment)? Use Wald as the statistic option.

anes_des %>%
  filter(!is.na(PartyID), !is.na(TrustGovernment)) %>%
  group_by(PartyID, TrustGovernment) %>%
  summarize(
    p = survey_mean()
  )

# A tibble: 35 × 4
# Groups:   PartyID [7]
   PartyID                  TrustGovernment          p    p_se
   <fct>                    <fct>                <dbl>   <dbl>
 1 Strong democrat          Always              0.0144 0.00452
 2 Strong democrat          Most of the time    0.124  0.00977
 3 Strong democrat          About half the time 0.353  0.0171 
 4 Strong democrat          Some of the time    0.420  0.0175 
 5 Strong democrat          Never               0.0888 0.0126 
 6 Not very strong democrat Always              0.0207 0.00874
 7 Not very strong democrat Most of the time    0.101  0.0154 
 8 Not very strong democrat About half the time 0.296  0.0248 
 9 Not very strong democrat Some of the time    0.466  0.0228 
10 Not very strong democrat Never               0.116  0.0182 
# ℹ 25 more rows

anes_des %>%
  filter(!is.na(PartyID), !is.na(TrustGovernment)) %>%
  svychisq(~ TrustGovernment + PartyID, design = ., statistic = "Wald")

    Design-based Wald test of association

data:  NextMethod()
F = 15.134, ndf = 24, ddf = 51, p-value = 1.142e-15

6. Is there a difference in the distribution of gender (Gender) across early voting status (EarlyVote2020)?

# Hint - you'll need the anes_des object

anes_des %>%
  filter(!is.na(Gender), !is.na(EarlyVote2020)) %>%
  group_by(Gender, EarlyVote2020) %>%
  summarize(
    p = survey_mean()
  )

# A tibble: 4 × 4
# Groups:   Gender [2]
  Gender EarlyVote2020     p   p_se
  <fct>  <fct>         <dbl>  <dbl>
1 Male   Yes           0.781 0.0345
2 Male   No            0.219 0.0345
3 Female Yes           0.679 0.0331
4 Female No            0.321 0.0331

anes_des %>%
  filter(!is.na(Gender), !is.na(EarlyVote2020)) %>%
  svychisq(~ Gender + EarlyVote2020, design = ., statistic = "Wald")

    Design-based Wald test of association

data:  NextMethod()
F = 4.6322, ndf = 1, ddf = 47, p-value = 0.03655

Footnotes

1. https://www.eia.gov/consumption/residential/data/2015/c&e/pdf/ce1.1.pdf

2. https://www.eia.gov/consumption/residential/data/2015/c&e/pdf/ce1.1.pdf