Week 4 Discussion
Jiwon Ban
2024-04-14
I. SAMPLING METHODOLOGIES
When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.
To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method.
There are many ways to think about a sampling methodology, but one popular taxonomy is to divide the sampling methods into -
Probability sampling (involves random selection, allowing you to make strong statistical inferences about the whole group).
Non-probability sampling. (involves non-random selection.) based on convenience or other criteria, allowing you to easily collect data).
Q1.
In your own words, after your online readings , describe what is probability sampling and non-probability sampling (max 5 lines each). You can even talk about the differences. between the sampling methodologies. Which probability sampling method should we use?
Q1. Solution:
Probability sampling, or random sampling, is when the sampling method is more representative of the general population of interest; in other words, all members of the population have an equal chance of being selected. Some examples of probability sampling are: simple random sampling, stratified random sampling, systematic sampling, and cluster sampling.
Non-probability sampling is when the sample population is limited by either an inclusion/exclusion criteria or due to other logistical constraints (e.g., research question specifically investigating a demographic group of interest); in other words, sample is not drawn by chance and thus limited in terms of ecological validity (i.e., how representative the findings are to the general population). Some examples of non-probability sampling are: purposive, convenience, quota, and snowball sampling.
Though probability sampling is more robust in terms of the inferences and validity of findings, it often is costly, labor-intensive, and difficult in terms of establishing what really is a representative sample of the population. In my field of developmental/educational psychology, both types of sampling are used, and which sampling method to use quite depends on the research question and study design. For example, if I’m curious about how much sleep US 4-year-old children get, I’d need to employ a probability sampling method to have a nationally representative sample. If I’m curious about how much sleep US 4-year-olds who live in group-home settings receive, I’d probably utilize a non-probability sampling method to specifically recruit from group-home settings that serve many four-year-old students.
Q2.
Also describe at least 2 survey designs from each of the two categorizations above(max 3 lines each). A more colorful chart here.. Note that survey designs are only broadly designed, so different articles will have different numbers of survey designs in the two taxonomies above. EG.Use your discretion and pick the breakup that you like and that makes sense to you. Post any useful links you may find.
Q2. Solution:
Purposive sampling is often used in my field of research — especially when investigating specific subset of population by some factor/characteristic of interest. Mainly, it is cost- and time-effective, as researchers can ensure that they are targeting the appropriate sample population of interest (rather than going through a process of random sampling, conducting a study with selected participants, then having to filter by exclusion criteria). However, the level of specificity on how to sample purposively creates limitations introduces biases and limitations to general inferences of study findings (i.e., a result from a purposively sampled dataset may not replicate, due to the lack of probability sampling.
For experimental design (i.e., intervention studies), and especially for applied work with high ecological validity, stratified random sampling is widely implemented. This method categorizes the population using categorical factors/variables of interest, and then take a random sample from the strata. It is mainly used to highlight differences between groups (i.e., strata) in a population. Stratified random sampling is quite useful when trying to capture how different characteristics of the general population influence, interact, and predict the outcome variables. However, it requires a lot of a priori insight as well as theoretical justification for how the stata are constructed.
II. CPS
The Current Population Survey (CPS) is a monthly survey of households conducted by the Bureau of Census for the Bureau of Labor Statistics. It provides a comprehensive body of data on the labor force, employment, unemployment, persons not in the labor force, hours of work, earnings, and other demographic and labor force characteristics.
In particular, CPS is a sample survey. of about 60,000 eligible households (in 2017) scientifically selected to reflect the entire U.S. civilian non-institutional population. On the basis of responses to a series of questions on work and job search activities, each person 16 years and over in a sample household is classified as employed, unemployed, or not in the labor force.
Explore the official Bureau of Labor Statistics (BLS) CPS website. and the IPUMS CPS website. .
Q1.
What is the population of interest in the Current Population Survey (CPS) ? Be precise – are there any age criteria, occupation criteria, and/or geographic criteria ?
Q1. Solution:
The Current Population Survey is interested in specifically generalizing the entire US civilian, non-institutional population to capture the civilian labor force trends in the United States; specifically, those that are of minimum legal working age (16 yo) were eligible to be selected as a sample participant. There’s no upper-bound for age in a household; however, those living in non-institutional settings (e.g., correctional or long-term care facilities) were not eligible.
“The CPS sample is selected to be representative of the entire population of the United States. In order to select the sample, all of the counties and independent cities in the country first are grouped into approximately 2,000 geographic areas (sampling units). The Census Bureau then designs and selects a sample of about 800 of these geographic areas to represent each state and the District of Columbia. The sample is a state-based design and reflects urban and rural areas, different types of industrial and farming areas, and the major geographic divisions of each state.” (Source)
Q2.
What is the sample used to estimate the population parameters/headline statistics, like unemployment rate, employment to population ratio ? Be precise - how many households and/or people are included ? Is there anything interesting/unique about the survey methodology that you found?
Q2. Solution:
The total sample included 60,000 households, which translated to about 110,000 individuals each month. For a given household, an eligible reference member (typically who owns/rents the housing unit) responded to the interview for the entire household. The survey employs a clever methodology to detect trends in the labor force; the questionnaires are conducted through telephone/in-person interviews, and for every household, data are collected in two waves— once a month for four consecutive months, then another follow-up a year after (with the same once a month interview for four months). Participants are asked to respond to the questions based on their previous week. I was impressed at how streamlined the data collection method is, while wondering why a phone/in-person interview is preferred over online surveys.
Q3.
Do you think CPS is a representative sample of the US entire population after reading about its methodology or your online reserach ?
Q3. Solution:
Yes, especially after reading about the way they conducted the sampling. Specifically, the researchers sectioned off the topography of the United States into 2000 geographical units, from which they sampled about 800 of the areas to be representative of the entire country.
Q4.
CPS data can be accessed from a few places. One easy way is to create an account. at IPUMS CPS to download the data. Go to IPUMS CPS and create an extract containing the variables INCWAGE. (Wage and salary income) and LABFORCE. (Labor force status) from January 2019 onwards.
Import the data into R.. Getting data from the IPUMS website section for screenshot.
You will need to install and load the ipums package (it helps make importing IPUMS data into R easy) - install.packages(‘ipumsr’) and library(ipumsr)
Then, download the data extract and the ddi. file in the same working directory.
See Grace Cooper, IPUMS staff’s response here on how to save the ddi file in the working directory. (right click -> save save -> ... )
Q4. Solution:
setwd("/Users/jiwonban/ADEC7301/Week 4")
library("ipumsr")
ddi_data <- read_ipums_ddi("cps_00001.xml")
cps_data <- read_ipums_micro(ddi_data)## Use of data from IPUMS CPS is subject to conditions including that users should cite the data appropriately. Use command `ipums_conditions()` for more details.summary(cps_data)## YEAR SERIAL MONTH HWTFINL
## Min. :2019 Min. : 1 Min. : 1.000 Min. : 0
## 1st Qu.:2021 1st Qu.:17983 1st Qu.: 3.000 1st Qu.: 1575
## Median :2022 Median :35715 Median : 5.000 Median : 3404
## Mean :2022 Mean :36370 Mean : 5.665 Mean : 3120
## 3rd Qu.:2023 3rd Qu.:54110 3rd Qu.: 9.000 3rd Qu.: 4330
## Max. :2024 Max. :94633 Max. :12.000 Max. :20133
## NA's :800468
## CPSID ASECFLAG ASECWTH PERNUM
## Min. :0.000e+00 Min. :1 Min. : 110 Min. : 1.000
## 1st Qu.:2.020e+13 1st Qu.:1 1st Qu.: 998 1st Qu.: 1.000
## Median :2.021e+13 Median :1 Median : 1923 Median : 2.000
## Mean :1.904e+13 Mean :1 Mean : 1982 Mean : 2.121
## 3rd Qu.:2.022e+13 3rd Qu.:2 3rd Qu.: 2627 3rd Qu.: 3.000
## Max. :2.024e+13 Max. :2 Max. :10925 Max. :16.000
## NA's :3592239 NA's :3896454
## WTFINL CPSIDP CPSIDV ASECWT
## Min. : 0 Min. :0.000e+00 Min. :0.000e+00 Min. : 86
## 1st Qu.: 1581 1st Qu.:2.020e+13 1st Qu.:2.020e+14 1st Qu.: 1018
## Median : 3402 Median :2.021e+13 Median :2.021e+14 Median : 1951
## Mean : 3200 Mean :1.904e+13 Mean :1.904e+14 Mean : 2048
## 3rd Qu.: 4422 3rd Qu.:2.022e+13 3rd Qu.:2.022e+14 3rd Qu.: 2749
## Max. :44748 Max. :2.024e+13 Max. :2.024e+14 Max. :17422
## NA's :800468 NA's :3896454
## LABFORCE INCWAGE
## Min. :0.000 Min. : 0
## 1st Qu.:1.000 1st Qu.: 0
## Median :1.000 Median : 33280
## Mean :1.308 Mean :20957460
## 3rd Qu.:2.000 3rd Qu.: 125000
## Max. :2.000 Max. :99999999
## NA's :3896454Q5.
Plot / summarize income wage variable by labor force status. You can revise your data extract easily as you decide what variables to add/discard/keep. Do you find any patterns in labor force statistics that make sense, such as income varying by labor force status?
Q5. Solution:
#income by labor force status, 2019 Jan~ 2020 March
summary(cps_data$INCWAGE)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 0 33280 20957460 125000 99999999 3896454na.omit(cps_data$INCWAGE)## <labelled<double>[800468]>: Wage and salary income
## [1] 18000 0 12000 0 12000 0 55000 0
## [9] 50002 0 0 38000 0 0 100000 0
## [17] 0 0 0 0 0 99999999 99999999 99999999
## [25] 99999999 99999999 99999999 99999999 99999999 0 32621 34560
## [33] 0 0 24002 0 99999999 99999999 23000 50000
## [41] 99999999 99999999 99999999 41500 52000 99999999 0 5000
## [49] 0 0 0 0 35000 50000 0 56000
## [57] 50000 0 46701 45001 10700 90000 0 90000
## [65] 99999999 99999999 0 0 99999999 25000 12000 99999999
## [73] 15000 0 0 3000 0 17240 0 99999999
## [81] 99999999 0 0 42000 40000 72000 36002 99999999
## [89] 64600 10000 63000 0 0 12002 5547 99999999
## [97] 55000 0 40000 0 51500 0 31000 0
## [105] 0 51488 18890 0 0 4000 6000 40000
## [113] 99999999 0 82000 99999999 99999999 0 10000 0
## [121] 99999999 99999999 39000 25000 0 25000 56000 0
## [129] 0 8967 53549 0 0 99999999 23000 0
## [137] 0 0 0 99999999 63000 99999999 99999999 0
## [145] 2000 0 99999999 99999999 99999999 57000 99999999 99999999
## [153] 99999999 43200 40560 0 99999999 99999999 52000 4000
## [161] 99999999 70000 80000 40000 145000 120000 1600 99999999
## [169] 99999999 0 25000 15000 0 0 0 10000
## [177] 15000 80000 60000 43000 29000 9500 25000 74000
## [185] 12000 17122 0 2000 99999999 99999999 0 27000
## [193] 12000 0 0 0 3500 0 0 45000
## [201] 45000 0 0 0 0 36000 10000 20000
## [209] 0 17000 26000 62000 23000 4800 77000 64000
## [217] 4000 99999999 0 99999999 0 0 0 15680
## [225] 28000 35000 30000 58300 45000 4410 23920 0
## [233] 86000 35000 0 0 0 0 16000 28000
## [241] 0 0 24000 0 99999999 0 10000 0
## [249] 99999999 99999999 99999999 99999999 55000 45300 99999999 99999999
## [257] 0 0 99999999 30000 0 34000 0 0
## [265] 0 50000 0 0 0 0 0 99999999
## [273] 0 50000 22700 66000 0 2748 24400 0
## [281] 60000 0 0 48000 53000 39000 99999999 0
## [289] 65000 0 99999999 0 0 0 0 0
## [297] 27002 17500 0 99999999 0 40000 130000 0
## [305] 0 47000 53000 36000 3500 0 0 0
## [313] 30000 26676 99999999 0 37000 0 40000 60000
## [321] 40000 99999999 99999999 30000 0 0 0 40000
## [329] 0 0 0 50000 15000 99999999 99999999 9500
## [337] 99999999 99999999 99999999 65000 0 60000 39534 14000
## [345] 99999999 99999999 99999999 84000 0 0 0 0
## [353] 0 0 0 59500 0 0 0 0
## [361] 0 0 40000 10000 99000 99000 7500 99999999
## [369] 70000 35000 99999999 28000 1500 99999999 0 0
## [377] 0 60582 0 0 5000 39000 12400 0
## [385] 0 96214 66000 0 40000 99999999 65000 30000
## [393] 2000 7200 67000 99999999 99999999 99999999 20000 80000
## [401] 33000 90000 21000 35000 99999999 99999999 99999999 0
## [409] 50000 85000 0 0 42000 32000 3500 60000
## [417] 80000 20000 800 55000 12000 0 0 66000
## [425] 0 99999999 76000 14400 0 0 99999999 21000
## [433] 39000 0 99999999 99999999 0 0 120000 0
## [441] 99999999 45000 100000 25000 10000 1000 99999999 32162
## [449] 45000 99999999 99999999 99999999 43000 36500 16400 99999999
## [457] 27000 51000 0 0 28000 14000 0 0
## [465] 0 0 0 0 0 83200 0 0
## [473] 99999999 54000 64000 99999999 99999999 0 11000 16000
## [481] 52500 27000 5000 99999999 56000 0 56000 130000
## [489] 16000 0 36000 42000 99999999 99999999 0 0
## [497] 12000 0 0 60000 43000 0 0 85000
## [505] 50000 0 17000 25000 8000 5000 8000 4000
## [513] 15000 40000 24000 0 20000 40000 18000 99999999
## [521] 40000 39000 0 24855 36000 0 130000 30000
## [529] 99999999 99999999 60000 45000 25600 4000 0 0
## [537] 60000 30000 0 99999999 0 8000 58000 99999999
## [545] 0 99999999 52000 60000 70000 99999999 99999999 0
## [553] 0 0 43500 0 99999999 65000 90000 50000
## [561] 99999999 20000 18000 200 0 54000 65000 300
## [569] 229000 77000 0 99999999 52000 80000 99999999 0
## [577] 99999999 99999999 99999999 47000 135000 11000 0 93000
## [585] 15360 99999999 52000 52000 15600 99999999 54000 52000
## [593] 0 99999999 0 54496 10000 11460 99999999 99999999
## [601] 34000 99999999 99999999 24000 82000 10000 19200 2000
## [609] 5000 99999999 99999999 99999999 0 32000 60000 6500
## [617] 8000 0 230843 0 0 0 0 60000
## [625] 45000 12002 0 0 0 99999999 99999999 99999999
## [633] 99999999 99999999 112000 85000 0 0 0 4800
## [641] 0 40000 54000 0 0 31000 0 0
## [649] 0 109000 0 99999999 99999999 99999999 12000 7000
## [657] 99999999 99999999 99999999 99999999 99999999 91520 62024 19000
## [665] 99999999 30000 50000 30000 50000 0 31000 0
## [673] 0 42000 44000 0 0 52000 30000 84000
## [681] 0 5000 17000 18000 4600 0 99999999 99999999
## [689] 27000 10914 20000 30000 36000 0 99999999 0
## [697] 85000 94000 0 80000 0 57000 40000 0
## [705] 0 500 99999999 30000 60000 99999999 99999999 5660
## [713] 28896 99999999 0 0 0 19000 99999999 60000
## [721] 19000 0 0 0 47000 99999999 99999999 7200
## [729] 85500 13000 1200 0 0 0 15000 50000
## [737] 4000 46000 50900 99999999 25000 0 0 0
## [745] 0 0 33000 6500 1400 9600 180 40000
## [753] 0 0 0 0 0 0 55000 0
## [761] 0 0 80000 125000 99999999 99999999 7500 0
## [769] 109000 0 36000 57000 1500 0 18000 18000
## [777] 0 0 0 41000 40000 43690 99999999 100000
## [785] 0 99999999 99999999 0 0 0 0 24000
## [793] 30000 27500 0 0 0 53596 80000 0
## [801] 0 0 20000 7200 5000 350000 0 158000
## [809] 55000 45000 0 99999999 0 0 0 16000
## [817] 0 0 0 75000 100000 0 30000 25000
## [825] 45000 130000 99999999 99999999 0 60000 0 20000
## [833] 32000 70000 9300 0 0 60000 18001 48200
## [841] 70000 48000 27000 0 12000 99999999 0 0
## [849] 78000 93000 0 75000 0 0 19000 35000
## [857] 17886 0 0 99999999 0 0 180000 50000
## [865] 0 0 0 72999 88000 99999999 0 20800
## [873] 58000 56000 75000 0 200 245000 135000 0
## [881] 0 0 19000 35000 0 45000 20000 750
## [889] 7000 0 0 99999999 38100 99999999 99999999 41000
## [897] 20000 0 0 72400 70000 99999999 99999999 75000
## [905] 0 100000 0 25000 60000 110000 65000 1651
## [913] 5000 0 99999999 99999999 30000 158000 2500 0
## [921] 0 0 125000 60000 6000 0 29000 122000
## [929] 70000 50000 8000 0 0 99999999 115000 17000
## [937] 0 59000 30000 150000 31000 100000 30000 5913
## [945] 95000 0 0 60000 86000 4500 0 99999999
## [953] 6000 99999999 20000 52000 99999999 56000 50000 99999999
## [961] 99999999 25600 50000 40000 10000 50000 85000 400
## [969] 100 15000 127000 0 99999999 77002 67002 99999999
## [977] 99999999 99999999 27500 65000 6000 0 38000 39700
## [985] 99999999 99999999 15000 99999999 99999999 21040 120000 30000
## [993] 15000 20000 43000 99999999 44500 0 52000 99999999
## [1001] 52000 24000 16000 2500 99999999 27500 54000 20000
## [1009] 99999999 12400 30000 4000 0 99999999 0 48000
## [1017] 0 99999999 99999999 46000 25000 130000 30000 0
## [1025] 0 0 32000 0 99999999 0 0 30000
## [1033] 0 99999999 0 65000 0 40000 40000 43000
## [1041] 0 60000 20000 0 0 0 0 100750
## [1049] 60150 0 0 40000 68000 0 0 123000
## [1057] 34500 0 48000 0 99999999 30000 70000 68000
## [1065] 99999999 0 0 0 0 37000 99999999 99999999
## [1073] 99999999 28000 42000 2100 3000 47000 0 5000
## [1081] 125000 42000 45000 0 32202 15000 0 64000
## [1089] 58000 0 0 0 140000 55000 99999999 0
## [1097] 0 0 0 0 0 0 0 85000
## [1105] 0 0 101680 35000 91000 45000 15000 0
## [1113] 0 30000 0 0 100000 0 0 1200
## [1121] 36800 40000 0 0 45000 0 25000 22000
## [1129] 26500 0 29000 80000 0 0 0 0
## [1137] 20000 0 99999999 0 0 0 10800 0
## [1145] 0 33000 76 99999999 99999999 0 10000 56000
## [1153] 99999999 20000 83000 2000 68000 15000 100002 30002
## [1161] 99999999 219000 0 60000 99999999 99999999 0 27000
## [1169] 69000 0 20000 20800 185000 40000 51000 0
## [1177] 0 39000 45240 99999999 99999999 120000 0 6860
## [1185] 11000 6552 55000 1800 0 25000 50000 0
## [1193] 2500 0 99999999 75000 130000 99999999 99999999 60000
## [1201] 30000 99999999 99999999 0 62000 20000 0 99999999
## [1209] 99999999 99999999 25000 99999999 17800 120000 150000 3900
## [1217] 1856 67000 47000 28000 11000 48000 99999999 22600
## [1225] 72000 99999999 99999999 122000 20000 4500 3000 0
## [1233] 24000 99999999 99999999 57000 0 48000 99999999 99999999
## [1241] 8000 0 99999999 0 0 95000 32000 99999999
## [1249] 99999999 31000 80000 0 70000 46000 3800 0
## [1257] 99999999 134659 20000 6600 4500 3800 0 0
## [1265] 0 7800 68000 14000 10000 0 0 0
## [1273] 40576 0 99999999 1800 99999999 0 0 0
## [1281] 50000 99999999 99999999 99999999 78000 0 10000 99999999
## [1289] 99999999 0 50000 0 0 30000 8000 82000
## [1297] 0 0 32281 99999999 36400 36000 99999999 99999999
## [1305] 26000 0 0 4860 0 99999999 30000 0
## [1313] 45000 137000 0 39396 481 99999999 99999999 99999999
## [1321] 0 6000 0 41500 59000 8700 3200 34227
## [1329] 0 0 0 0 111000 99999999 99999999 30000
## [1337] 99999999 99999999 0 0 56000 13000 0 99999999
## [1345] 99999999 0 0 0 0 99999999 99999999 38000
## [1353] 36000 50000 21000 30000 0 0 120000 0
## [1361] 50000 220000 0 0 0 0 0 0
## [1369] 0 0 45000 0 0 0 0 90000
## [1377] 200 83000 44000 99999999 99999999 29000 0 39520
## [1385] 44000 7100 99999999 0 0 0 45000 45000
## [1393] 50000 55000 99999999 0 0 0 0 0
## [1401] 25000 56000 50000 100000 40000 99999999 99999999 0
## [1409] 40000 44000 4000 80000 128000 99999999 99999999 0
## [1417] 42000 85000 0 50000 45000 3000 200 0
## [1425] 55000 0 65000 46500 21000 120000 100000 99999999
## [1433] 99999999 0 69420 99999999 99999999 99999999 55000 41000
## [1441] 40000 24000 50000 99999999 10000 75000 99999999 0
## [1449] 27000 99999999 99999999 48400 10000 99999999 0 48250
## [1457] 0 0 99999999 58000 21000 99999999 99999999 40000
## [1465] 56000 99999999 99999999 0 29000 48000 0 0
## [1473] 50108 48000 4000 75000 99999999 0 75000 99999999
## [1481] 99999999 0 55000 0 0 36000 0 0
## [1489] 3000 99999999 99999999 0 46000 60000 23000 52000
## [1497] 46000 12000 0 6000 0 0 150000 24000
## [1505] 0 45000 2500 2000 0 0 0 67000
## [1513] 62000 50000 250 45760 30000 0 71360 99999999
## [1521] 99999999 99999999 0 18000 50000 25000 0 48000
## [1529] 150000 0 4000 0 25000 72000 36000 50000
## [1537] 846 78000 34000 99999999 0 0 70064 0
## [1545] 35000 50000 0 0 0 0 0 35685
## [1553] 99999999 99999999 37000 99999999 0 0 0 90000
## [1561] 50000 35000 0 0 70000 86000 0 99999999
## [1569] 99999999 99999999 99999999 32350 117293 0 0 0
## [1577] 4200 0 200000 0 0 99999999 99999999 99999999
## [1585] 99999999 90000 30000 18000 0 99999999 53000 50000
## [1593] 40000 6400 36997 3921 66000 89000 512 99999999
## [1601] 0 75000 0 99999999 45000 62000 99999999 0
## [1609] 40000 0 72000 105000 99999999 99999999 0 68000
## [1617] 17100 2425 0 0 35000 0 44000 4400
## [1625] 0 64200 32000 96000 45000 0 0 0
## [1633] 96000 55000 75000 52000 25000 99999999 13260 36000
## [1641] 0 70000 65000 99999999 51283 48000 22500 0
## [1649] 90000 0 0 2273 99999999 80000 99999999 99999999
## [1657] 99999999 26000 32000 99999999 30000 30000 75000 50000
## [1665] 17000 0 25000 0 99999999 99999999 99999999 28000
## [1673] 57000 35000 12900 16000 99999999 99999999 0 0
## [1681] 65000 37000 0 38000 99999999 99999999 35000 99999999
## [1689] 99999999 34500 75000 3500 0 14000 12000 100000
## [1697] 99999999 99999999 40000 92000 99999999 99999999 40529 89086
## [1705] 100000 125000 99999999 6500 99999999 0 99999999 99999999
## [1713] 50000 32000 99999999 99999999 22500 99999999 135000 14500
## [1721] 500 0 99999999 84000 32000 99999999 26000 25000
## [1729] 100000 0 66000 50600 0 45000 300 0
## [1737] 66500 0 52000 0 9000 0 0 0
## [1745] 0 0 0 0 0 0 45000 30000
## [1753] 250000 0 6000 0 11000 45000 33000 36000
## [1761] 99999999 99999999 52000 0 17000 58000 4298 24254
## [1769] 116769 2056 4733 80000 0 0 36000 0
## [1777] 6200 1800 0 0 42000 0 49000 16000
## [1785] 0 0 29000 0 0 10000 99999999 99999999
## [1793] 99999999 99999999 35000 99999999 99999999 35000 35000 99999999
## [1801] 99999999 99999999 30000 11000 0 0 0 40000
## [1809] 0 99999999 0 45000 99999999 99999999 99999999 99999999
## [1817] 0 30000 70000 0 13585 62000 35000 0
## [1825] 31000 32000 0 10000 0 30000 35000 0
## [1833] 0 61000 38000 0 0 7800 0 0
## [1841] 0 53000 99999999 115000 0 20000 4000 3000
## [1849] 99999999 0 0 32000 48000 0 0 70000
## [1857] 3000 6240 53000 0 0 0 0 35000
## [1865] 58000 24001 10900 95000 0 48000 99999999 17000
## [1873] 100000 99999999 99999999 16000 99999999 99999999 99999999 14000
## [1881] 6000 0 0 0 99999999 99999999 99999999 4000
## [1889] 76000 27000 0 99999999 25000 99999999 99999999 57571
## [1897] 159824 5913 0 940000 2600 99999999 90000 75000
## [1905] 16000 0 122000 0 0 40000 60000 15000
## [1913] 0 0 15000 66909 0 0 0 54000
## [1921] 152000 99999999 99999999 99999999 0 0 4000 0
## [1929] 9000 0 0 99999999 99999999 157000 92000 4500
## [1937] 0 62000 65000 140000 0 0 70100 0
## [1945] 0 0 0 0 0 0 30000 0
## [1953] 0 85000 70000 30000 35000 45000 72000 2500
## [1961] 99999999 99999999 99999999 81000 20000 75000 17000 25000
## [1969] 2000 3000 80000 0 0 48000 70000 29000
## [1977] 0 99999999 0 0 47000 59000 99999999 99999999
## [1985] 110000 130000 25000 14000 4500 0 40000 45000
## [1993] 0 5000 52000 15006 26000 0 28000 21000
## [2001] 5252 0 0 60000 70000 0 23000 100000
## [2009] 115000 86000 99999999 90000 40000 99999999 99999999 72000
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## [99329] 99999999 45000 185000 0 0 0 0 0
## [99337] 0 99999999 99999999 99999999 28080 36400 0 3744
## [99345] 0 48000 400 35000 30000 99999999 99999999 81000
## [99353] 19000 20000 15000 76000 0 0 0 0
## [99361] 14560 0 99999999 99999999 25000 92000 29120 0
## [99369] 52000 34002 75000 0 0 0 0 45000
## [99377] 0 99999999 99999999 99999999 30000 12000 99999999 99999999
## [99385] 99999999 24000 40000 99999999 99999999 0 18500 17000
## [99393] 6000 99999999 0 0 0 99999999 99999999 0
## [99401] 0 99999999 16000 0 0 0 35600 59000
## [99409] 99999999 99999999 99999999 99999999 0 0 0 15000
## [99417] 10000 99999999 73000 30000 3800 40000 120000 60000
## [99425] 120000 34000 10000 31000 20000 29000 65000 0
## [99433] 90000 0 99999999 99999999 19500 55000 105000 110000
## [99441] 50000 55000 0 70000 36000 99999999 30000 0
## [99449] 0 0 30000 0 0 0 0 75000
## [99457] 0 85000 0 120000 85000 80000 65000 0
## [99465] 0 0 125000 0 99999999 72000 90000 0
## [99473] 65000 55000 50000 18000 0 82000 91000 0
## [99481] 0 54000 72000 0 100000 0 99999999 99999999
## [99489] 99999999 55000 135000 15000 85000 68000 0 8700
## [99497] 0 0 29281 95000 0 99999999 44500 50000
## [99505] 99999999 99999999 99999999 0 0 59400 69000 0
## [99513] 45000 10000 99999999 99999999 110000 0 99999999 50000
## [99521] 42000 48000 32000 42000 42000 37700 60000 52000
## [99529] 99999999 45000 52000 0 25000 99999999 99999999 0
## [99537] 0 40552 30000 26000 0 35000 45000 25000
## [99545] 30000 85000 0 99999999 80000 75000 0 99999999
## [99553] 99999999 0 0 0 87820 99999999 99999999 105000
## [99561] 25000 25000 99999999 99999999 0 0 11000 8000
## [99569] 98000 193000 15000 2500 0 30000 18000 28000
## [99577] 35001 99999999 99999999 30000 25000 0 50000 99999999
## [99585] 99999999 0 24000 0 42000 14000 99999999 99999999
## [99593] 40000 31200 0 36000 0 30000 31200 0
## [99601] 99999999 77000 0 99999999 99999999 62000 0 99999999
## [99609] 99999999 60000 0 99999999 60000 0 99999999 99999999
## [99617] 24000 40000 99999999 99999999 50000 30000 52000 99999999
## [99625] 2000 54600 5000 325 66000 31000 0 99999999
## [99633] 99999999 99999999 99999999 30000 40000 0 0 0
## [99641] 27000 99999999 35000 55000 0 15000 25000 0
## [99649] 0 25000 25000 50000 50000 99999999 99999999 99999999
## [99657] 64000 70000 99999999 99999999 99999999 1500 36000 99999999
## [99665] 99999999 99999999 0 0 0 25000 0 0
## [99673] 0 250000 50000 0 0 0 2000 138000
## [99681] 40000 2500 2500 0 0 45000 0 0
## [99689] 90000 23000 25000 20000 8300 40000 40000 0
## [99697] 0 99999999 92000 38000 99999999 99999999 0 20000
## [99705] 8000 35000 0 50000 99999999 99999999 0 0
## [99713] 34000 99999999 99999999 0 28000 0 30000 0
## [99721] 65300 300 19250 13104 0 0 99999999 99999999
## [99729] 99999999 0 60000 40000 20000 45000 4500 99999999
## [99737] 99999999 130000 15000 99999999 99999999 99999999 32000 30000
## [99745] 99999999 50000 0 0 30000 32000 0 99999999
## [99753] 40000 59000 35000 0 12000 99999999 80000 0
## [99761] 84000 99999999 55000 48000 0 0 99999999 99999999
## [99769] 0 0 0 57000 120000 120000 99999999 99999999
## [99777] 99999999 18000 52000 99999999 99999999 0 0 53000
## [99785] 30000 0 0 35000 99999999 0 0 10000
## [99793] 113000 99999999 99999999 0 0 70000 50000 1800
## [99801] 0 20000 22000 23000 51000 0 20800 0
## [99809] 40000 50000 150000 0 0 0 117000 25000
## [99817] 0 0 0 0 0 99999999 99999999 99999999
## [99825] 99999999 105000 0 99999999 99999999 99999999 0 0
## [99833] 99999999 99999999 99999999 52000 235000 400000 62000 296000
## [99841] 0 50000 105000 50000 99999999 99999999 99999999 100000
## [99849] 40000 0 99999999 99999999 0 100000 99999999 99999999
## [99857] 99999999 1099999 89000 0 5000 106000 48600 550000
## [99865] 0 99999999 99999999 35000 99999999 35000 31000 20000
## [99873] 0 20000 0 0 99999999 99999999 110000 0
## [99881] 99999999 99999999 99999999 25000 170000 99999999 66000 0
## [99889] 99999999 99999999 170000 80000 99999999 0 202222 99999999
## [99897] 99999999 111000 0 99999999 99999999 99999999 0 200000
## [99905] 99999999 99999999 99999999 60000 105000 99999999 99999999 85000
## [99913] 350000 45000 40000 24000 23000 8000 2000 0
## [99921] 0 0 0 30000 99999999 0 40000 99999999
## [99929] 99999999 52500 31200 15000 40000 0 50000 0
## [99937] 0 0 8000 5000 99999999 0 0 54200
## [99945] 18500 49000 33000 10000 35501 99999999 99999999 34320
## [99953] 51000 0 0 120000 70000 1000 400 100000
## [99961] 31200 0 22000 0 45000 99999999 40000 74000
## [99969] 138000 0 99999999 50000 75000 99999999 99999999 42617
## [99977] 43003 99999999 99999999 43000 6000 99999999 30700 0
## [99985] 54000 99999999 99999999 99999999 0 47200 50000 110000
## [99993] 96000 0 99999999 39000 55000 99999999 99999999
## [ reached getOption("max.print") -- omitted 700469 entries ]
##
## Labels:
## value label
## 1e+08 Missing (1962-1966 only)
## 1e+08 N.I.U. (Not in Universe)plot(cps_data$LABFORCE, cps_data$INCWAGE)
boxplot(cps_data$LABFORCE, cps_data$INCWAGE)
The data suggests that there is a difference of income wage based on labor force status. Specifically, those who are in the labor force (i.e., 2) were shown to more than not.
In real life, you would be using non-base R commands like dplyr. For example, see CPS IPUMS R exercises. 1 and 2, or how researchers clean the data.. Potentially, you could create tables like these in the BLS reports which are very closely followed by business people and policymakers alike.