Concept of Interest - Public Organisation - European Social Survey - Statistics Assignment Help

Assignment Task:

Assessment on Concept of Interest Public Organisation - European Social Survey

Part 1 Concept of Interest Public Organisation :

1. An introduction describing what the concept of interest for the measure is and how/why the measure is being been used.
2. A description of the way that the measure is constructed. This should capture the overall measurement strategy, not necessarily all of the details. For example, you should outline the major types of indicators, but you do not need to go into detail about how each one was coded.
3. A discussion of what you think are the most important potential problems with this measurement strategy. This should focus on explaining one or two problems in detail, although you are welcome to briefly list others.
4. A proposal for how we could use some ideas from this course to improve the existing measurement strategy or measure the same concept in a different way. It is ok if your alternative measurement strategy slightly changes the concept being interested, so long as you explain how it does and why it would be a better measurement strategy for the use that you described in section 1 of your report. 

This is a written task only. No statistical analysis is required. Please write an essay in continuous prose. Make sure that you summarise the concept of interest and the measure concisely enough that you have sufficient word count to explain potential problems with the measure and how you might address them. 

You can examine a measure of any concept of your choice from economics, human geography, political science, public health, public policy or other social science fields. 

You are free to propose any alternative measurement strategy to answer it, on three conditions: 

• You must use at least one method covered in this course. 
•  You must not plagiarise an existing study, published or not. You are going to be critically responding to an existing measure developed by someone else, but that critical response must be your own. You cannot present someone else’s proposed improvement of an existing measure as your own! Remember that we can very easily find out about plagiarism - don’t try it. 

Your proposed improvement should be achievable and realistic. The data required should exist or be collectible in principle. You should clearly state how the analysis of that data would proceed. You should also state accurately the extent to which your proposal actually improves on the existing measure, and if there are any tradeoffs involved. It is ok if your proposal improves some aspects of the measurement strategy at the expense of others, but you should explain why that tradeoff makes sense for some applications. 

It is possible to achieve high marks with a report about either a good existing measure or a bad existing measure. What I want to see is that you have carefully thought about the strengths and drawbacks of the existing measure and about ways that you might improve it. 

Part 2 European Social Survey:

Answer each of the following five questions. All of the questions are based on two datasets, final-assessment-ess2018extract.csv and final-assessment- ess2018country.csv, both of which are extracts from the 2018 European Social Survey. These datasets are available for download from the POLS0013 Moodle page. The file final-assessment-ess2018extract.csv has the following variables: 

The 0-10 scales for the seven trust variables run from 0 (“No trust at all”) to 10 (“Complete trust”). No labels were provided to survey respondents for the intermediate numerical values, only for 0 and 10. 

The file final-assessment-ess2018country.csv has the country-level averages for the seven trust_ variables. This can be constructed from the final-assessment- ess2018extract.csv, but I have saved you the trouble of figuring out how to do that with the survey weights. At a few points below, I will give instructions is if you have loaded the final- assessment-ess2018extract.csv data file into an R data frame called ess2018. You are not required to do this but may find it less confusing if you do so. Many of the questions below have more than one right answer. If I ask for you to identify/describe “one” or “two” of something, that does not mean that there are only that many good answers to the question.

Question 1.

Construct a set of histograms for the trust_ variables and calculate the means of all the variables as well. Use these to describe the general features of the distributions of these indicator variables.

Question 2.

What are the arguments for treating the trust_ variables as interval level indicators? What are the arguments for treating them as ordinal-level indicators? 

Question 3.

For the purposes of this analysis, the target concept that we would like to measure is “trust in institutions”, at the level of individuals. We will then make comparisons across individuals in the data with different values of the country, gender, age, and degree. Briefly describe one potential limitation of any measure based on these indicators for making such comparisons.

Question 4.

Identify an alternative concept that you might have measured with a subset of these indicators. What is the concept, and which indicators would you use to measure that concept? 

Question 5.

Construct an equal weight index using all seven trust_ variables. Fit a linear regression (lm()) with dummy variables for countries and include a weight=ess2018$weight argument so that you are using the survey weights. Describe general patterns in which countries’ citizens have higher and lower trust in institutions. 

Question 6.

Do an analysis to assess whether the trust index varies as a function of age, gender and whether someone has a university degree. There are many ways you could do this, but whichever analysis you do, state clearly what you have done and what we can conclude about the relationship between trust and these other variables.

Question 7.

Use cor() to examine the pairwise correlations between the trust_ variables. Describe any major patterns that you see.

Question 8.

Use promo() to do principal components analysis on the trust_ variables. Examine the coefficients and give an interpretation for the first principle component.

Question 9.

Examine the coefficients and give an interpretation for the second and third principal components, if you are able to. 

Question 10.

Create the scree plot for this principal component analysis. What do we learn from this?

Question 11.

Repeat the analysis from B, C, & D using only responses where ess2018$country == "United Kingdom". Does the within UK variation across individuals look similar to the across Europe variation in individuals? What is the same/different? 

Question 12.

If we used an ordinal IRT model to analyze these data, how would that change the assumptions that we are making from those made in the Principle Components Analysis above? How many difficulty and discrimination parameters would there be? 

Question 13.

If we applied the ordinal IRT model, we would then have three different scores of individual-level trust: one based on equal weighting, one based on the first principle component, and one based on the ordinal IRT model. Without actually doing the comparison, would you expect the correlation between the three measures of individual measures to be high or low, given what we have seen across these analyses? Explain why. If you have been unable to do some/all of the above analyses, you still may be able to answer this question. 

Question 14.

Load the country-level averages from final-assessment-ess2018country.csv. Use k- means clustering to identify two clusters of countries. What are the clusters that emerge? How would you label them? Repeat with three clusters, and describe how the countries are divided up differently into three groups versus two. 

Question 15.

Given what you observed in A, do you think clustering countries into classes make sense for these data? Why or why not? 

What do we learn from the fact that these clusters emerge in a country-level analysis? How does this relate to what we learned from the individual-level analysis? Explain.

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