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Making Social Work Count Lecture 7

Making Social Work Count Lecture 7. An ESRC Curriculum Innovation and Researcher Development Initiative. Using Numbers to Describe a Sample. How to tell a story with data. Learning outcomes. Why gather information?. To identify similarities To identify differences

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Making Social Work Count Lecture 7

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  1. Making Social Work Count Lecture 7 • An ESRC Curriculum Innovation and Researcher Development Initiative

  2. Using Numbers to Describe a Sample How to tell a story with data

  3. Learning outcomes

  4. Why gather information? • To identify similarities • To identify differences • To establish patterns in characteristics or behaviours • To predict patterns in characteristics or behaviours in the future • To describe a particular phenomenon • To make sense of a particular phenomenon

  5. Why gather information? • What kind of information might we want to know about a group of: • Looked after children? • Carers? • Young offenders? • Adults with direct payments?

  6. Collect information about individuals

  7. The information is transformed into numbers and aggregated into a database

  8. The aggregate data is analysed to tell a story

  9. Our data could tell us a story of: • The educational outcomes of looked after children • The average number of hours a carer spends caring a week • The relationship between offending and educational attainment • Adults’ satisfaction levels regarding using direct payments

  10. How to tell a story with data: Chapter 1

  11. In order to tell the story, we need to collect the information (data)

  12. What are the different forms of collecting information (data)? • The level of measurement used in collecting data determines the statistical techniques which can be used in analysis. • Levels of measurement: • Nominal • Ordinal • Interval/Ratio

  13. The story of the drinking glasses An illustration of the different levels of measurement

  14. Levels of measurement: Nominal • Nominal – classifies variables into categories; the number of cases within each category is counted. • Yes/No • Sex • Religious affiliation • Type of glass

  15. Levels of measurement: Ordinal • Ordinal – ranks variables according to a particular characteristic or criteria. The order matters, but not the difference between values. • Degree classifications: First, Upper second, Lower second, Third, Fail • Order of preference: First, second, third, fourth, fifth • Amount of pain on a scale of 1 to 10 • Tallest to shortest

  16. Levels of measurement: Interval and ratio • Interval – Equal units of measurement between variables. Can interpret the order and distance between variables • Temperature, expressed in Fahrenheit or Celsius • Ratio - Has a true zero in that when the variable equals 0, there is none of that variable • Length; time; age; weight • Liquid capacity as measured my ml 650ml 400ml 325ml 300ml 200ml

  17. The story of the drinking glasses • There are 4 types of drinking glasses: • 2 mugs • 4 wine glasses • 4 champagne flutes • 3 water glasses. • The glass height decreases: • champagne flute • first wine glass • wine glass • water glass • mug • Despite the champagne flute being the tallest, it holds the least amount of liquid at 200ml with the water glass holding the most at 650ml. The average amount of liquid all of the glasses can hold is 375 ml.

  18. How to tell a story with data: Chapter 2

  19. Once we have collected the information, we need to analyse the data

  20. What are the different ways of analysing the data? • Frequency • Proportion • Percentage • Measures of central tendency –An indication of the middle point of distribution for a particular group or sample • Mean • Median • Mode

  21. The story of the reablement service: An illustration of analysing different types of data

  22. Form of analysis: Frequency • Frequency –combining like variables with like and counting the number within each category (f) • There are 311 service users (N) of the reablement service. We want to summarise the three main reasons why people access the service and the frequency of males and females Reason f Physical disability 160 Temp. ill 144 Mental health 7 Total N = 311 Sex f Male 112 Female 199 Total N = 311

  23. Form of analysis: Proportion Proportion (P) Sex f Male 112 Female 199 Total N = 311 • Counting the number of variables in one category (f) and then dividing by the total number of variables (N). • P = f/N • What is the P of males to females in the reablement service?

  24. Form of analysis: Proportion Proportion (P) Sex f P Male 112 0.36 Female 199 0.64 Total N = 311 • Counting the number of variables in one category (f) and then dividing by the total number of variables (N). • P = f/N • What is the P of males to females in the reablement service?

  25. Form of analysis: Percentage Percentage (%) Reason f Physical disability 160 Temp. ill 144 Mental health 7 Total N = 311 Sex f Male 112 Female 199 Total N = 311 • A percentage standardises for size by multiplying the proportion (P) by 100 to indicate the frequency as per 100 cases. • % = (100) f/N • What is the % of service users that enter the service due to temporary illness? • What is the % of males and females in the reablement service?

  26. Form of analysis: Percentage Percentage (%) Reason f % Physical disability 160 51.4% Temp. ill 144 46.3% Mental health 7 2.3% Total N = 311 100% Sex f % Male 112 36% Female 199 64% Total N = 311 100% • A percentage standardises for size by multiplying the proportion (P) by 100 to indicate the frequency as per 100 cases. • % = (100) f/N • What is the % of service users that enter the service due to temporary illness? • What is the % of males and females in the reablement service?

  27. Form of analysis: Mean • Mean – summing all the scores in a dataset and dividing by the total number of scores. Provides an average score. • What is the mean hours of care for service users when entering the reablement service? • What is the mean hours of care when exiting the service?

  28. Form of analysis: Mean • Mean – summing all the scores in a dataset and dividing by the total number of scores. Provides an average score. • What is the mean hours of care for service users when entering the reablement service? • What is the mean hours of care when exiting the service?

  29. Form of analysis: Median & Mode • Median – The middlemost score in a list of scores • Mode – The most frequent or common score in a list of scores • What is the median and mode number of hours of care at entry and exit? entry: 10 7 5 15 5 6 7 10 4 7 4 7 exit: 5 0 0 7 0 0 0 7 0 4 0 2

  30. Form of analysis: Median • Median – The middlemost score in a list of scores • Mode – The most frequent or common score in a list of scores • What is the median and mode number of hours of care at entry and exit? entry: 4 4 5 5 6 7 7 7 7 10 10 15 exit: 0 0 0 0 0 0 0 2 4 5 7 7

  31. Form of analysis: Mode • Median – The middlemost score in a list of scores • Mode – The most frequent or common score in a list of scores • What is the median and mode number of hours of care at entry and exit? entry: 4 4 5 5 6 7 7 7 7 10 10 15 exit: 0 0 0 0 0 0 0 2 4 5 7 7

  32. Form of analysis: Range • Range – The difference between the highest score and lowest score in a list of scores • What is the range of number of hours of care at entry and exit? entry: 4 4 5 5 6 7 7 7 7 10 10 15 exit: 0 0 0 0 0 0 0 2 4 5 7 7

  33. Form of analysis: Range • Range – The difference between the highest score and lowest score in a list of scores • What is the range of number of hours of care at entry and exit? entry: 4 4 5 5 6 7 7 7 7 10 10 15 exit: 0 0 0 0 0 0 0 2 4 5 7 7

  34. How do we know which analysis to perform? • The level of measurement used in collecting data determines the statistical techniques which can be used in analysis.

  35. Level of measurement and type of data analysis

  36. The story of the reablement service (1) Users Reasons for entering the service

  37. The story of the reablement service (2) • The mean number of hours of care at entry was 7.25, which declined to 2.08 at exit. • The median number of hours of care at entry was 7, yet declined to 0 at exit. • The mode number of hours of care at entry was 7, yet declined to 0 at exit.

  38. Assessment Scores of Students

  39. Assessment Scores of Students: Levels of Measurement • What level of measurement are each of the following variables and how would you suggest analysing them? • Sex • Age • Satisfaction with module • Assessment Score • Classification

  40. How to tell a story with data: Chapter 3

  41. Once we have collected the information and analysed the data, we need to present the findings (tell a story)

  42. Assessment Scores of Students Code: Sex (1 = M; 2 = F); Satisfaction (1 = very unsatisfied; 2 = unsatisfied; 3 = satisfied; 4 = very satisfied; Classification (1 = fail; 2 = third; 3 = lower second; 4 = upper second; 5 = first).

  43. Example storytelling • Analyse the data of the assessment scores of students • Justify your choice of analysis • Summarise your findings to tell a story about the students

  44. Learning outcomes Are you able to: • Identify and define levels of measurement and measures of central tendency? • Describe situations in which different levels of measurement and measures of central tendency are useful and appropriate? • Calculate frequency, percentage, range, measures of central tendency and be able to critique and justify their use for the exercise problems?

  45. Activity

  46. Activity – Part A The students should read the following research report: “Evaluation of the Southwark Reablement Service” available from http://www.york.ac.uk/media/spsw/documents/cmhsr/Southwark%20Reable ment%20Service%20Evaluation%2021.6.13.pdf. Ask the students to complete the following tasks: • Identify an example of a frequency, percentage, mean and range. • Calculate the percentage of the 81 clients who engaged with the Reablement service that were male and the percentage that were female (page 4). • Create an argument for why the authors should have included the median and mode when reporting the “mean age of the sample” (page 6). • Create an argument for why the authors included the sample size alongside the percentages when reporting the “Payment by Results Clusters” (page 7). • Based on the analysis of the quantitative date, ask the students to “tell a story” of the Reablement program in Southwark.

  47. Activity – Part B • Ask the students to download a copy of the RAND 36-Item Short Form Health • Survey available from http://www.rand.org/health/surveys_tools/mos/mos_core_36item_survey_print.html. Ask the students to look at questions 1-20 and answer the following questions: • What is the level of measurement for items 1-20 on the questionnaire? • How would you propose analysing items 1-20 and why? In particular, consider percentage, frequency, mean, median, mode and range.

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