DPE_RTest

Problem Description

You are given R_test_dataset.csv with the following description of the variables and labels of some of the variables.

Variable description Table:

Variable Description SbjNum Interview ID Start_Time Start Time Q_2 Date of Interview Interviewer Enumerator Name Ward Ward S2 What is your age?(Grouped) S3 What secondary school certificate do you have? S4 Do you have, or are you currently enrolled in, a tertiary school certificate? S5_a Do you intend to seek further education (e.g. diploma or bachelor's degree)? Social_Class Social class A1_A First Name A1_B Family Name A2 What is your age? (Specific) A5 What is your region of origin? Q_25_S What is your region of origin? [Other (specify)] A8 What is your current marital status? A9 Do you have any children? A10 How many people do you live with (both adults and children)? A12 What is your relationship to the head of the household? Q_32_S What is your relationship to the head of the household? [Other (specify)] B3 Which school did you attend for secondary school (S5-S6/A Levels)? B4 Was your school an all secondary girls school? B8 What level of education do you feel is next in your career path? B16 How much are you willing to pay for your next degree or diploma? (Average tution per semester) B17 Currently, how career ready are you? C1 Have you been employed in the last 12 months?

Value labels

Variable Values Value Label Interviewer 1 Menya Abdmajid 2 Bbale Denis 3 Muwonge Allan Joshua 4 Wambi Ken Paul 5 Wabwire Thomas 6 Muhindo Wilfred 7 Ahumuza Owen 8 Mirembe Mary 9 Arinitwe Mackline 10 Alum Maria 11 Kasule violet 12 Aweko Monica 13 Nabbumba Pennina S2 1 17 and below 2 18-25 3 25-30 4 31 and above S3 1 None/did not complete 2 Uganda Certificate of Education (UCE) 1-2 passes 3 Uganda Certificate of Education (UCE) 3-5 passes 4 Uganda Certificate of Education (UCE) 5 or more passes 5 Uganda Advanced Certificate of Education (UACE) – 1 principa 6 Uganda Advanced Certificate of Education (UACE) 2 principal 7 Uganda Advanced Certificate of Education (UACE) 3 principals S4 1 Yes 2 No S5_a 1 Yes 2 No 3 I don't know Social_Class 1 Social Class D 2 Social Class C2 3 Social Class C1 4 Social Class B 5 Social Class A A5 1 Central 2 Western 3 Eastern 4 Northern 5 Other A8 1 Single 2 Married 3 Divorced or Separated 4 Widowed A9 1 I do not have children 2 I have 1 child 3 I have 2 children 4 I have 3 children 5 I have more than 3 children A10 1 I live alone 2 I live with one other person 3 I live with 2-3 other people 4 I live with 4-6 other people 5 I live with 7 or more people A12 1 I am the head of the household 2 I am the wife of the head of household 3 I am the daughter of the head of household 4 I am a relative of the head of household 5 I am a friend of the head of household 6 Other: B4 1 Yes 2 No B8 1 Diploma 2 Technical certificate 3 Bachelor's degree B17 1 Extremely ready 2 Somewhat ready 3 Neutral 4 Not ready 5 Definitely not ready C1 1 I have been employed (for example in an office, restaurant, 2 I am self-employed (for example, on my own farm or family's 3 I have been employed as an unpaid intern 4 I have been engaged in household activities including agricu 5 I have not been employed

Using R statistical software and R_test_dataset.csv, answer the following questions:

1. Describe some of the checks you would do on a dataset in readiness for analysis (10mrks).

• Missing values: Check if there are any blank cell in the dataset to avoid the output NA in the R analysis. • Duplicates: Check if the cells or columns have been duplicated or there are any repeated values in the data set. • Type of data: Check whether the data is discrete or continuous so that it will be easier to identify the distribution of the data. • Distribution of the data: This helps identify which distribution will be used in analysis, for example binary is binomial distribution and so some tests such as t test will not be carried out on the data. • Outliers: These are values that are not in the range of the expected values, either they are too high or too low compared to the threshold.

2. Define a complete interview (2mkrs).

• Give a brief introduction of oneself. • Explain to the interviewee what the interview entails. Could be detailed or brief depending with the literacy of the interviewee and/or the familiarity of the topic in question. • Give clear instructions of what is required from the interviewee. • Allow the interviewee some time to answer the questions. Assist the interviewee (without bias) incase anything outlined in the questions is not clear. • Give a rough estimate period of time of when they should expect results from their answered questionnaires.

3. What is the difference between a raw and a ‘dirty’ dataset (4mrks)? Raw data is data collected by a researcher from the field regarding a topic or subject of their interest and then taken for analysis to give expected results whereas dirty data is already collected data that has not been cleaned to be fit a good statistical analysis.

4. You are doing Quality check on data and you have been asked to prepare a callback sheet to be sent to team leaders in the field for corrections. List some of the information you would have to include in the sheets (10mrks).

5. Import the dataset into r (4mrks).

6. How many cases in the dataset are duplicated (2mrks)?

7. In every set of duplicate interviews, drop a case. What’s the total number of unique interviews (2mrks)?

8. Assign value labels to the following variables: (12mrks)? a. Interviewer b. S2 c. Social_Class d. B4 e. B17 f. C1

9. Compute the number of interviews conducted by each interviewer (2mrks).

10. Clean up the ward variable and then compute the number of interviews per ward (2mrks).

11. How many interviews were conducted in every ward per interviewer (4mrks)?

12. Would you say older people are in high Social class (4mrks)?

13. What is the minimum, maximum, average and median age of the respondents (2mrks)?

14. Plot A2 against B16. Add title and axes labels (5mrks). a. Save the plot in a .pdf format (1mrks). b. What can you conclude from the graph (2mrks)?

15. What is the proportion of girls who are in non-girls only schools (2mrks)?

16. Save a copy of the dataset of the respondents who are aged between 18–25 years only (4mrks).

17. Conduct any additional analysis in the data (6mrks).

Save all your R codes in a .R file and make sure they are properly commented. All the best.