Empirical Project 11 Working in R

Don’t forget to also download the data into your working directory by following the steps in this project.

Getting started in R

For this project you will need the following packages:

• tidyverse, to help with data manipulation
• readxl, to import an Excel spreadsheet
• knitr, to format tables
• psych, to compute Cronbach’s alpha.

If you need to install any of these packages, run the following code:

install.packages(c("tidyverse", "readxl", "knitr", "psych"))


You can import the libraries now, or when they are used in the R walk-throughs below.

library(tidyverse)
library(knitr)
library(psych)


Part 11.1 Summarizing the data

We will be using data collected from an internet survey sponsored by the German government.

• Download the data. Open the file in Excel and read the Data dictionary tab and make sure you know what each variable represents. (Later we will discuss exactly how some of these variables were coded.)
• The paper ‘Data in Brief’ gives a summary of how the survey was conducted. You may find it helpful to read it before starting on the questions below.
1. While contingent valuation methods can be useful, they also have shortcomings. Read Section 5 of the paper ‘Introduction to economic valuation methods’ (pages 16–19), and explain which limitations you think apply particularly to the survey we are looking at.
Likert scale
A numerical scale (usually ranging from 1–5 or 1–7) used to measure attitudes or opinions, with each number representing the individual’s level of agreement or disagreement with a particular statement.

Before comparing between question types, we will first compare the people assigned to each question type to see if they are similar in demographic characteristics and attitudes towards related topics (such as beliefs about climate change and need for government intervention). Attitudes were assessed using a 1–5 Likert scale, where 1 = strongly disagree, and 5 = strongly agree.

1. Re-code or create the variables as specified:
• Reverse-code the following variables (so that 1 is now 5, 2 is now 4, and so on): cog_2, cog_5, scepticism_6, scepticism_7.
• For the variables WTP_plmin and WTP_plmax, create new variables with the values replaced as shown in Figure 11.1 below (these are the actual amounts, in euros, that individuals selected in the survey, and will be useful for calculating summary measures later).
Original value New value
1 48
2 72
3 84
4 108
5 156
6 192
7 252
8 324
9 432
10 540
11 720
12 960
13 1,200
14 1,440

WTP survey categories (original value) and euro amounts (new value).

Figure 11.1 WTP survey categories (original value) and euro amounts (new value).

R walk-through 11.1 Importing data and re-coding variables

Before importing data in Excel or csv format, check it in a spreadsheet program (such as Excel) to ensure you understand the structure of the data and whether any additional options are required for the read_excel function. In this case the data is in a worksheet called ‘Data’, there are no missing values to worry about, and the first row contains the variable names. We can therefore import the data using the read_excel function without any additional options.

library(tidyverse)
library(knitr)


Reverse-code variables

The first task is to re-code variables related to the respondents’ views on certain aspects of government behaviour and attitudes about global warming. This coding makes the interpretation of high and low values consistent across all questions, since the survey questions do not have this consistency.

Note that even though the value of 3 for these variables will stay the same, for the recode function to work properly we have to include a mapping for every new value to a previous value.

WTP <- WTP %>%
mutate_at(c("cog_2","cog_5",
"scepticism_6","scepticism_7"),
funs(recode(.,"1"=5,"2"=4,
"3"=3,"4"=2,"5"=1)))


Create new variables containing WTP amounts

Although we could employ the same technique as above to re-code the value for the minimum and maximum willingness to pay variables, an alternative is to use the merge function. This function allows us to combine two dataframes via values given in a particular variable.

We start by creating a new dataframe (category_amount) that has two variables: the original category value and the corresponding new euro amount. We then apply the merge function to the WTP dataframe and the new dataframe, specifying the variable in WTP using by.x and in the new dataframe using by.y as the variables that link the data in each dataframe together. We also use the all.x=TRUE option, otherwise the merge function will drop any observations with missing values for the WTP_plmin and WTP_plmax variables. Finally we have to rename the column of the merged new values to something more meaningful.

wtp_euro_levels <- c(48,72,84,108,156,
192,252,324,432,
540,720,960,1200,1440)  # Vector containing the Euro amounts
category_amount <- data.frame(original = 1:14, new = wtp_euro_levels) # Create mapping dataframe

# Create a new variable for the minimum WTP
WTP <- merge(WTP,category_amount, by.x="WTP_plmin",by.y = "original", all.x = TRUE) %>%
rename(.,"WTP_plmin_euro"="new")

# Create a new variable for the maximum WTP
WTP <- merge(WTP,category_amount, by.x="WTP_plmax",by.y = "original", all.x = TRUE) %>%
rename(.,"WTP_plmax_euro"="new")

1. Create the following indices, giving them an appropriate name in your spreadsheet (make sure to use the reverse-coded variable wherever relevant):
• Belief that climate change is a real phenomenon: Take the mean of scepticism_2, scepticism_6, and scepticism_7.
• Preferences for government intervention to solve problems in society: Take the mean of cog_1, cog_2, cog_3, cog_4, cog_5, and cog_6.
• Feeling of personal responsibility to act pro-environmentally: Take the mean of PN_1, PN_2, PN_3, PN_4, PN_6, and PN_7.

R walk-through 11.2 Creating indices

We can create all of the required indices in three steps using the rowMeans function. In each step we use the cbind function to join the required variables (columns) together as a matrix. As the data is stored as a single observation per row, the index value is the average of the values in each row of this matrix, which we calculate using the rowMeans function.

WTP <- WTP %>%
rowwise() %>%           # Ensure subsequent operations are applied by row
mutate(.,climate          = rowMeans(cbind(scepticism_2, scepticism_6, scepticism_7))) %>%
mutate(.,gov_intervention = rowMeans(cbind(cog_1, cog_2, cog_3, cog_4, cog_5, cog_6))) %>%
mutate(.,pro_environment  = rowMeans(cbind(PN_1, PN_2, PN_3, PN_4, PN_6, PN_7))) %>%
ungroup()               # Return the dataframe to the original format

Cronbach’s alpha
A measure used to assess the extent to which a set of items is a reliable or consistent measure of a concept. This measure ranges from 0–1, with 0 meaning that all of the items are independent of one another, and 1 meaning that all of the items are perfectly correlated with each other.

When creating indices, we may be interested to see if each item used in the index measures the same underlying concept of interest (known as reliability or consistency). There are two common ways to assess reliability: either look at the correlation between items in the index, or use a summary measure called Cronbach’s alpha (this measure is used in the social sciences).

Cronbach’s alpha is a way to summarize the correlations between many variables, and ranges from 0 to 1, with 0 meaning that all of the items are independent of one another, and 1 meaning that all of the items are perfectly correlated with each other. While higher values of this measure indicate that the items are closely related and therefore measure the same concept, with values that are very close to 1 (or 1), we could be concerned that our index contains redundant items (for example, two items that tell us the same information, so we would only want to use one or the other, but not both). You can read more about this in the paper ‘Using and interpreting Cronbach’s Alpha’.

1. Calculate correlation coefficients and interpret Cronbach’s alpha:
• For one of the indices you created in Question 3, create a correlation table to show the correlation between each of the items in the index. Figure 11.2 shows an example for Question 3(a). (Remember that the correlation between A and B is the same as the correlation between B and A, so you only need to calculate the correlation for each pair of items once). Are the items in that index strongly correlated?
• Use the alpha function (part of the psych package included with R) to compute the Cronbach’s alpha for these indices. Interpret these values in terms of index reliability.
scepticism_2 scepticism_6 scepticism_7
scepticism_2 1
scepticism_6   1
scepticism_7     1

Correlation table for items in Question 3(a).

Figure 11.2 Correlation table for items in Question 3(a).

R walk-through 11.3 Calculating correlation coefficients

Calculate correlation coefficients

We covered calculating correlation coefficients in R walk-through 10.1. In this case, since there are no missing values we can use the cor function without any additional options.

For the questions on climate change:

cor(cbind(WTP$scepticism_2,WTP$scepticism_6, WTP$scepticism_7))  ## [,1] [,2] [,3] ## [1,] 1.0000000 0.3904296 0.4167478 ## [2,] 0.3904296 1.0000000 0.4624211 ## [3,] 0.4167478 0.4624211 1.0000000  For the questions on government behaviour: cor(cbind(WTP$cog_1, WTP$cog_2, WTP$cog_3, WTP$cog_4, WTP$cog_5, WTP$cog_6))  ## [,1] [,2] [,3] [,4] [,5] [,6] ## [1,] 1.0000000 0.2509464 0.32358783 0.6823385 0.28925672 0.4141992 ## [2,] 0.2509464 1.0000000 0.11761093 0.2771883 0.40794667 0.0828661 ## [3,] 0.3235878 0.1176109 1.00000000 0.3347662 0.01818617 0.3128608 ## [4,] 0.6823385 0.2771883 0.33476619 1.0000000 0.27424993 0.4597244 ## [5,] 0.2892567 0.4079467 0.01818617 0.2742499 1.00000000 0.1045843 ## [6,] 0.4141992 0.0828661 0.31286082 0.4597244 0.10458434 1.0000000  For the questions on personal behaviour: cor(cbind(WTP$PN_1, WTP$PN_2, WTP$PN_3, WTP$PN_4, WTP$PN_6, WTP$PN_7))  ## [,1] [,2] [,3] [,4] [,5] [,6] ## [1,] 1.0000000 0.4824823 0.4282149 0.4226534 0.4138090 0.4584007 ## [2,] 0.4824823 1.0000000 0.6315015 0.4375971 0.4994126 0.6542377 ## [3,] 0.4282149 0.6315015 1.0000000 0.4596711 0.5219712 0.5894731 ## [4,] 0.4226534 0.4375971 0.4596711 1.0000000 0.5668642 0.3947270 ## [5,] 0.4138090 0.4994126 0.5219712 0.5668642 1.0000000 0.4551294 ## [6,] 0.4584007 0.6542377 0.5894731 0.3947270 0.4551294 1.0000000  Calculate Cronbach’s alpha It is straightforward to compute the Cronbach’s alpha using the alpha function. psych::alpha(WTP[c("scepticism_2", "scepticism_6", "worry")])$total$std.alpha  ##  0.663788  psych::alpha(WTP[c("cog_1", "cog_2", "cog_3", "cog_4", "cog_5", "cog_6")])$total$std.alpha  ##  0.7102249  psych::alpha(WTP[c("PN_1", "PN_2", "PN_3", "PN_4", "PN_6", "PN_7")])$total$std.alpha  ##  0.8543827  Now we will compare characteristics of people in the dichotomous choice (DC) group and two-way payment ladder (TWPL) group (the variable abst_format indicates which group an individual belongs to). Since the groups are of different sizes, we will use percentages instead of frequencies. 1. For each group, create separate tables to summarize the distribution of the following variables: • gender (sex) • age (age) • number of children (kids_nr) • household net income per month in euros (hhnetinc) • membership in environmental organization (member) • highest educational attainment (education). Without doing formal statistical tests, do these two groups of individuals (DC and TWPL) look similar in demographic characteristics? R walk-through 11.4 Using loops to obtain summary statistics The two different formats (DC and TWPL) are recorded in the abst_format variable, and take the values ref and ladder respectively. variables <- list(quo(sex),quo(age), quo(kids_nr),quo(hhnetinc), quo(member),quo(education)) for (i in seq_along(variables)){ WTP %>% group_by(abst_format,!!variables[[i]]) %>% summarize (n = n()) %>% mutate(freq = n / sum(n)) %>% select(-n) %>% spread(abst_format,freq) %>% print() }  ## # A tibble: 2 x 3 ## sex ladder ref ## <chr> <dbl> <dbl> ## 1 female 0.518 0.523 ## 2 male 0.482 0.477 ## # A tibble: 6 x 3 ## age ladder ref ## <chr> <dbl> <dbl> ## 1 18 - 24 0.0949 0.0964 ## 2 25 - 29 0.0830 0.0865 ## 3 30 - 39 0.178 0.172 ## 4 40 - 49 0.223 0.226 ## 5 50 - 59 0.241 0.239 ## 6 60 - 69 0.180 0.181 ## # A tibble: 5 x 3 ## kids_nr ladder ref ## <chr> <dbl> <dbl> ## 1 four or more children 0.00988 0.00895 ## 2 no children 0.646 0.657 ## 3 one child 0.204 0.176 ## 4 three children 0.0296 0.0348 ## 5 two children 0.111 0.123 ## # A tibble: 12 x 3 ## hhnetinc ladder ref ## <chr> <dbl> <dbl> ## 1 1100 bis unter 1500 Euro 0.142 0.132 ## 2 1500 bis unter 2000 Euro 0.150 0.146 ## 3 2000 bis unter 2600 Euro 0.115 0.148 ## 4 2600 bis unter 3200 Euro 0.107 0.107 ## 5 3200 bis unter 4000 Euro 0.111 0.0815 ## 6 4000 bis unter 5000 Euro 0.0514 0.0497 ## 7 500 bis unter 1100 Euro 0.134 0.142 ## 8 5000 bis unter 6000 Euro 0.0277 0.0169 ## 9 6000 bis unter 7500 Euro 0.00791 0.00398 ## 10 7500 und mehr 0.00395 0.00497 ## 11 bis unter 500 Euro 0.0296 0.0417 ## 12 do not want to answer 0.121 0.125 ## # A tibble: 2 x 3 ## member ladder ref ## <chr> <dbl> <dbl> ## 1 no 0.923 0.914 ## 2 yes 0.0771 0.0865 ## # A tibble: 6 x 3 ## education ladder ref ## <dbl> <dbl> <dbl> ## 1 1 0.0119 0.0129 ## 2 2 0.0198 0.0209 ## 3 3 0.342 0.328 ## 4 4 0.263 0.269 ## 5 5 0.0692 0.0686 ## 6 6 0.294 0.300  The output above gives the required tables, but is not easy to read. You may want to tidy up the results, for example by translating and re-ordering the options in the household net income variable (hhnetinc). 1. Create summary tables as shown in Figure 11.3 for each index you created in Question 3. Without doing formal statistical tests, do the two groups of individuals look similar in the attitudes specified? Mean Standard deviation Min Max DC format TWPL format Summary table for indices. Figure 11.3 Summary table for indices. R walk-through 11.5 Calculating summary statistics The summarize_at function can provide multiple statistics on a number of variables in one command. Simply provide a list of the variables you want to summarize and then use the funs() option to specify the summary statistics you need. WTP %>% group_by(abst_format) %>% summarise_at(c("climate", "gov_intervention", "pro_environment"), funs(mean,sd,min,max)) %>% gather(index,value, climate_mean:pro_environment_max) %>% # Use gather and spread functions to reformat output spread(abst_format,value) %>% # for aesthetic reasons kable(.,format = "markdown", digits=2)  |index | ladder| ref| |:---------------------|------:|----:| |climate_max | 5.00| 5.00| |climate_mean | 2.29| 2.37| |climate_min | 1.00| 1.00| |climate_sd | 0.84| 0.85| |gov_intervention_max | 5.00| 5.00| |gov_intervention_mean | 3.15| 3.19| |gov_intervention_min | 1.00| 1.00| |gov_intervention_sd | 0.70| 0.66| |pro_environment_max | 5.00| 5.00| |pro_environment_mean | 3.03| 3.01| |pro_environment_min | 1.00| 1.00| |pro_environment_sd | 0.79| 0.82|  Part 11.2 Comparing willingness to pay across methods and individual characteristics Before comparing WTP across question formats, we will summarize the distribution of WTP within each question format. 1. For individuals who answered the TWPL question: • Use the variables WTP_plmin and WTP_plmax to create column charts (one for each variable) with frequency on the vertical axis and category (the numbers 1–14) on the horizontal axis. Describe characteristics of the distributions shown on the charts. • Using the variables you created in Question 2(c) in Part 11.1, make a new variable that contains the average of the two variables (the average of the minimum and maximum willingness to pay). • Using the variables you created in Question 2(c) in Part 11.1 and Question 1(b) here, calculate the mean and median willingness to pay. • Using the variable from Question 1(b), calculate the correlation between the average WTP and the demographic and attitudinal variables. Interpret the relationships implied by the coefficients. R walk-through 11.6 Summarizing willingness to pay variables Create column charts for minimum and maximum WTP Before we can plot a column chart, we need to compute frequencies (number of observations) for each value of the willingness to pay (1–14). We do this separately for the minimum and maximum willingness to pay. In each case we select the relevant variable and remove any observations with missing values using the na.omit function. We can then separate the data by level of the WTP_plmin_euro or WTP_PLmax_euro variables (using group_by), then obtain a frequency count using the summarize function. We also set this variable’s type to factor, for the horizontal axis labels in the column chart. Once we have the frequency count stored as a dataframe, we can plot the column charts. For the minimum willingness to pay: df.plmin <- WTP %>% select(WTP_plmin_euro) %>% na.omit() %>% group_by(WTP_plmin_euro) %>% summarize(n = n()) %>% mutate(WTP_plmin_euro = factor(WTP_plmin_euro, levels = wtp_euro_levels)) ggplot(df.plmin, aes(WTP_plmin_euro, n)) + geom_bar(stat="identity", position = "identity") + xlab("Minimum WTP (euros)") + ylab("Frequency") + theme_bw()  Minimum WTP (euros). Figure 11.4 Minimum WTP (euros). For the maximum willingness to pay: df.plmax <- WTP %>% select(WTP_plmax_euro) %>% na.omit() %>% group_by(WTP_plmax_euro) %>% summarize(n = n()) %>% mutate(WTP_plmax_euro = factor(WTP_plmax_euro, levels = wtp_euro_levels)) ggplot(df.plmax, aes(WTP_plmax_euro, n)) + geom_bar(stat="identity", position = "identity") + xlab("Maximum WTP (euros)") + ylab("Frequency") + theme_bw()  Maximum WTP (euros). Figure 11.5 Maximum WTP (euros). Calculate average WTP for each individual We can use the rowMeans function to obtain the average of the minimum and maximum willingness to pay. WTP <- WTP %>% rowwise() %>% mutate(.,WTP_average = rowMeans(cbind(WTP_plmin_euro,WTP_plmax_euro))) %>% ungroup()  Calculate mean and median WTP across individuals The mean and median of this average value can be obtained using the mean and median functions, although we have to use the na.rm=TRUE option to handle missing values correctly. mean(WTP$WTP_average, na.rm = TRUE)

##  268.5345

median(WTP$WTP_average, na.rm = TRUE)  ##  132  Calculate correlation coefficients We showed how to obtain a matrix of correlation coefficients for a number of variables in R walk-through 8.8. We use the same process here. WTP %>% mutate(gender = as.numeric(ifelse(sex=="female",0,1))) %>% # Create the gender variable select(WTP_average,education,gender, climate,gov_intervention,pro_environment) %>% cor(., use = "pairwise.complete.obs") -> M M[,"WTP_average"]  ## WTP_average education gender climate ## 1.00000000 0.13817368 0.03694972 -0.14462072 ## gov_intervention pro_environment ## -0.18845205 0.18750331  1. For individuals who answered the DC question: • Each individual was given an amount and had to decide ‘yes’, ‘no’, or ‘no vote/abstain from deciding’. Make a table showing the frequency of DC_ref_outcome, with costs as the row variable and DC_ref_outcome as the column variable. • Use this table to calculate the percentage of individuals who voted ‘no’ and ‘yes’ for each amount (in other words as a percentage of the row total, not the overall total). Count individuals who chose ‘abstain’ as voting ‘no’. • Make a line chart showing the ‘demand curve’, with percentage of individuals who voted ‘yes’ as the vertical axis variable and amount (in euros) as the horizontal axis variable. Describe features of this ‘demand curve’ that you find interesting. • Repeat Question 2(b), this time excluding individuals who chose ‘abstain’ from the calculations. Plot this new ‘demand curve’ on the chart created for Question 2(c). Do your results change qualitatively depending on how you count individuals who did not vote? R walk-through 11.7 Summarizing Dichotomous Choice (DC) variables Create frequency table for DC_ref_outcome We can group by costs and DC_ref_outcome to obtain a count for each combination of amount and vote response. We can also recode the voting options to ‘Yes’, ‘No’, and ‘Abstain’. WTP_DC <- WTP %>% group_by(costs,DC_ref_outcome) %>% summarize(n=n()) %>% na.omit() %>% mutate_at("DC_ref_outcome", funs(recode(.,"do not support referendum and no pay"="No","support referendum and pay"="Yes","would not vote"="Abstain"))) %>% spread(DC_ref_outcome,n) kable(WTP_DC,format="markdown", digits=2)  | costs| Abstain| No| Yes| |-----:|-------:|--:|---:| | 48| 12| 21| 32| | 72| 11| 30| 40| | 84| 12| 24| 45| | 108| 7| 35| 31| | 156| 13| 31| 40| | 192| 11| 25| 25| | 252| 9| 32| 28| | 324| 16| 41| 27| | 432| 11| 35| 29| | 540| 9| 31| 22| | 720| 12| 39| 13| | 960| 14| 28| 15| | 1200| 11| 42| 21| | 1440| 19| 42| 15|  Add column showing proportion voting yes or no We can extend the table from Question 2(a) to include the proportion voting yes or no (to obtain percentages, multiply the values by 100). WTP_DC <- WTP_DC %>% mutate(total = Abstain + No + Yes, prop_no = (Abstain + No)/total, prop_yes = Yes / total) %>% mutate_if(is.numeric, funs(round(., 2))) # Round all numbers to 2 decimal places kable(WTP_DC,format="markdown", digits=2)  Make a line chart of WTP Using the dataframe generated for Questions 2(a) and (b), we can plot the ‘demand curve’ as a scatterplot with connected points by using the geom_point and geom_line options for ggplot. Adding the extra option scale_x_continuous changes the default labeling on the horizontal axis to display ticks at every 100 euros. p <- ggplot(WTP_DC,aes(y=prop_yes,x=costs)) +geom_point() + geom_line(size=1) + ylab("% Voting 'Yes'") + xlab("Amount (euros)") + scale_x_continuous(breaks = seq(0,1500,100)) + theme_bw() print(p)  Demand curve (in euros), DC method. Figure 11.6 Demand curve (in euros), DC method. Calculate new proportions and add them to the table and chart It is straightforward to calculate the new proportions and add them to the existing dataframe, however, we will need to reshape the data to plot multiple lines on the same scatterplot. WTP_DC <- WTP_DC %>% mutate(total_ex = No + Yes, prop_no_ex = No/total_ex, prop_yes_ex = Yes / total_ex) %>% mutate_if(is.numeric, funs(round(., 2))) # Round all numbers to 2 decimal places kable(WTP_DC,format="markdown", digits=2)  WTP_DC %>% select(costs,prop_yes,prop_yes_ex) %>% gather(Vote,value,prop_yes:prop_yes_ex) %>% ggplot(.,aes(y=value,x=costs,color=Vote)) + geom_line(size=1) + geom_point() + ggtitle("'Demand curve' from DC respondents, under different treatments for 'Abstain' responses.") + scale_color_manual(values = c("blue", "red"), labels=c("counted as no","excluded")) + ylab("% voting 'yes'") + xlab("Costs (euros)") + theme_bw()  Demand curve from DC respondents, under different treatments for ‘Abstain’ responses. Figure 11.7 Demand curve from DC respondents, under different treatments for ‘Abstain’ responses. 1. Compare the mean and median WTP under both question formats: • Complete Figure 11.8 and use it to calculate the difference in means (DC minus TWPL), the standard deviation of these differences, and the number of observations. (The mean of DC is the mean of DC_ref_outcome for individuals who voted yes.) • Obtain 95% confidence intervals for the difference of means for each question format. Discuss the statistical significance of your findings. • Does the median WTP look different across question formats? (You do not need to do any formal statistical testing.) • Using your answers to Questions 3(a)(c), would you recommend that governments use the mean or median WTP in policy making decisions? (That is, which measure is more robust to changes in the question format?) Format Mean Standard deviation Number of observations DC TWPL Summary table for WTP. Figure 11.8 Summary table for WTP. R walk-through 11.8 Calculating confidence intervals for differences in means Calculate the difference in means, standard deviations, and number of observations We first create two vectors that will contain the WTP values for each of the two question methods. For the DC format, willingness to pay is recorded in the costs variable, so we select all observations where the DC_ref_outcome variable indicates the individual voted ‘yes’ and drop any missing observations. For the TWPL format we use the WTP_average variable that we created in R walk-through 11.6. DC_WTP <- WTP %>% subset(DC_ref_outcome == "support referendum and pay") %>% select(costs) %>% filter(!is.na(costs)) %>% as.matrix() # Print out the mean, sd, and count cat(sprintf("DC Format - mean: %.1f, standard deviation %.1f, count %d\n", mean(DC_WTP), sd(DC_WTP),length((DC_WTP))))  ## DC Format - mean: 348.2, standard deviation 378.6, count 383  TWPL_WTP <- WTP %>% select(WTP_average) %>% filter(!is.na(WTP_average)) %>% as.matrix() cat(sprintf("TWPL Format - mean: %.1f, standard deviation %.1f, count %d\n", mean(TWPL_WTP), sd(TWPL_WTP),length((TWPL_WTP))))  ## TWPL Format - mean: 268.5, standard deviation 287.7, count 348  Calculate 95% confidence intervals Using the t.test function to obtain 95% confidence intervals was covered in R walk-throughs 8.10 and 10.6. As we have already separated the data for the two different question formats in Question 3(a), we can obtain the confidence interval directly. t.test(DC_WTP,TWPL_WTP,conf.level = 0.05)$conf.int

##  78.10141 81.20560
## attr(,"conf.level")
##  0.05


Calculate median WTP for the DC format

In R walk-through 11.6 we obtained the median WTP for the TWPL format (132). We now obtain the WTP using the DC format.

median(DC_WTP)

##  192