1. CBR221 Introduction to Survey Data Analysis with Excel

2. 2 Workshop Objectives Use Excel to help you: –Organize data for analysis –Systematically work with data –Analyze data –Graphically display analysis

3. Well Examine –Types of variables used in analysis –Types of measurement scales used in analysis –How to describe data with Frequency counts, Descriptive Statistics, Histograms, and Pivot Tables –How to create charts

4. Survey Questions: 1.Area of the city child lives in? 2.Number of colds child had last year? 3.Gender: Male_____ Female _____ 4.Age 5.Describe cold symptoms __________

5. 5 Analysis Helps describe, conclude, recommend Systematic exploration for interpreting data

6. Survey Data Answers can be in text or numbered formats

7. 7 Statistics Systematic method of converting and analyzing data by using numbers

8. 8 Excel Support tool for statistical methods

9. © The Wellesley Institute www.wellesleyinstitute.co m 9 Start Excel

10. 10 Moving from Model to Excel Data Analysis Assumption, hypothesis, or model Collect data Organize data for analysis

11. Example Model or Assumption West area children get fewer colds than central area children. Want proof Analysis: Find mean (average) number of colds by area

12. When Data Fit Model Data cluster as expected Findings support assumption or model For 33 west area children, the mean is 5 colds. For 31 central area children, the mean is 6 colds. The results indicate west area children do have fewer colds than central area children.

13. Table Illustrates ZoneParticipants n Colds West335 Central316 Total64 Table 1. Colds by City Area

14. Graph Illustrates Central Mean Colds 5 Figure 1. West area children had a lower mean number of colds than central area children. 6 West

15. Pie Chart Illustrates Figure 1. West area c hildren had a lower mean of five (5) colds than central area children who had a mean value of six (6).

16. 16 Data Organizing Tips As required, ensure: All questions answered Questions needing to be skipped, were skipped Split multiple choice into 1 answer per column Open File 1

17. 17 Coding Open Ended Questions What cold symptoms did your child have? Take first 100 answers Group similar answers together You define what is similar Reduce to 10-20 codes or fewer if useful Pilot test

18. 18 Exercise: Create 3 codes Question: Describe childs symptoms? Response 1 Stuffy nose Response 2 Sinus congestion, Runny nose Response 3 Difficulty breathing through nose Response 4 Phlegm, Body ache, Runny nose Response 5 No energy, Cough Response 6 No energy Response 7 Sore throat Response 8 Cough Response 9 No energy

19. 19 Exercise: Code Symptoms Code Block Expel Pain Description Stuffy nose, Sinus congestion, Difficulty breathing through nose, No energy Cough, Phlegm, Runny nose Body Ache, Headache, Sore throat

20. 20 Assign Values to Codes Code Block Expel Pain Description Stuffy nose, Sinus congestion, Difficulty breathing through nose, No energy Cough, Phlegm, Runny nose Body Ache, Headache, Sore throat Value Yes=1 No=0 Yes=1 No=0 Yes=1 No=0

21. 21 I entered Code Variables and Values on new Excel sheet

22. 22 I entered Responses with values on new Excel Sheet

23. 23 To Enter Data ABC row 1IDLocationBlock row 2110 row 3221 row 4310 Row 1 has label for each variable Enter data 1 survey at a time 1 row per ID, work left to right 1 answer per column

24. 24 To Enter Data Enter 1 survey at a time For each question, work left to right across single Excel row Look at File 1b

25. 25 Practice Enter answers on File 1b, sheet 3 Tip: Split answers across 3 columns Question 2 ID#1 Answer a)Block X b)Expel c)Pain ID#2 Answer a)Block X b)Expel X c)Pain ID#3 Answer a)Block X b)Expel c)Pain ID#4 Answer a)Block X b)Expel X c)Pain X ID#5 Answer a)Block X b)Expel X c)Pain Compare your results with Data sheet

26. 26 Other Possible Open-Ended Question Codes 1 = positive comment 2 = negative comment 3 = neutral comment, positive and negative Verbatim codes: e.g., ache, congested, cough Code only those related to research question

27. 27 Text or Numbers Text codes often easier to remember, fewer entry errors e.g. M for male and F for female But numbers often faster entry Easier to work with numbers in Excel

28. 28 Check for Response Accuracy See File 1b, Accuracy Sheet Take 1 question at a time - i.e. pick out single column and check answers ID numbers at left For unanswered question, create blank cell (pivot tables count blanks)

29. 29 Use Excel When have larger number of respondents Makes manual calculations easier

30. 30 Organize Data Make sure data are entered into Excel in such a way that mathematical transactions can be performed on them E.g., If studying gender, let male equal 1 and female equal 2. Can then count the 1s and 2s in your study.

31. To Explain Findings Use common terms e.g., Variable Use accepted methods of analyses (Certain variables and measurements scales use certain tests)

32. Variable An object or human characteristic that: Is observable Can be subject to variation Can be classified according to a type (discrete, continuous as well as dependent, independent)

33. Variable and Measurement Scale Certain variables also use certain scales Can do frequency test for all variables But variable and scale type may also further inform with additional statistical test e.g., descriptive statistics

34. Discrete vs Continuous Variable Continuous Infinite values Valid values in-between E.g., distance, height, age Discrete Finite values No valid values in-between E.g., male/female full/part-time/co-op

35. 35 Discrete or Continuous Variable Hypothesis: City areas have different water temperatures. Discrete Variable: e.g., West, Central, East area Yes or No Value, no values in-between, no equi-distance Continuous: e.g., water temperature Infinite number or range of possible values in-between

36. Dependent vs Independent Variable Dependent Assumed to change Independent Assumed to influence or does not change Hypothesis: City areas have different water temperatures Dependent Water temperature Independent City area

37. Measurement Scales How do we measure what we are working with? Types: 1. Nominal 2. Ordinal 3. Interval 4. Ratio

38. Nominal Scale Finite values No values in-between No logical order Yes/No answer E.g., East, West, Central E.g., colour, gender

39. Ordinal Scale Finite values No values in-between Yes logical order Yes/No answer E.g., letter grades Grade B Grade C Grade A

40. Interval Scale Infinite values Logical order Values in-between Equal distance between data points How much? Numerical value No natural zero, keeps going No meaningful ratio between numbers E.g., temperature, 20 degrees NOT twice as hot as 10

41. Ratio Scale Infinite values Logical order Values in-between Equal distance between data points Comparing how much? Numerical values 0 value means something Meaningful ratio between numbers E.g., AGE Adult earns $50K; Teenager earns $25K Adult earns twice as much as teenager :

42. Variable Type Review Variable TypeValues Infinite Range Values in- between Value in order Values Equidist ant Scale * Discrete (Yes/no) XXXX Nominal Discrete Grade (Yes/no) A B C XXX Ordinal Continuous (How much?) Temperature or $$, Age Interval or Ratio DependentAssumed changes due to an independent variable IndependentAssumed does not change * Certain variables lend themselves to using certain types of measurement scales Areas

43. Measurement Scale Review ScaleValues finite in a range Values have order Values in- between Values are equidistant Test * Nominal (yes/no), AREAS XXXFrequency Ordinal A,B,C, (yes/no) XXFrequency Interval/ Ratio (how much for 1 sample?) Age XDescriptive Statistics Interval/ Ratio (how much for comparing 2 or more samples?) G1 age G2 age XInferential Statistics e.g. t-test Note: * Certain scales lend themselves to using certain statistical tests.

44. 44 Statistical Tests Frequency count for discrete data (nominal, ordinal scale) * - quantity Descriptive Statistics for continuous data (interval, ratio scale) –mean, median, mode –Characteristics of single sample Inferential Statistics for continuous data (ratio scale) –t-test –Comparison of two or more samples –Making inference from samples to populations Note: Can do frequency count for all data types

45. 45 Methodology By using a discrete/continuous independent variable called area (for city areas West, East, and Central), this study examined the discrete/continuous, dependent variable (with its possible multiple range of values) namely, water temperature. To measure the independent variable, (to indicate if someone lived in the West, East, or Central area), a nominal/ordinal/interval/ratio scale was used. To measure the dependent variable, to indicate how much the water temperature is), a(n) nominal/ordinal/interval/ratio scale was used.

46. 46 Methodology By using a discrete independent variable called area, this study examined the continuous, dependent variable namely, water temperature. To measure the independent variable, a nominal scale was used. To measure the continuous dependent variable, an interval scale was used.

47. 47 Calculations The statistical calculations we would conduct with Excel would be: Frequency counts for each of the 3 city areas Descriptive statistics of mean, median, mode for water temperatures

48. 48 Review of Descriptive Statistical Terms Mean – average Median – where 50% of scores lie above a certain score and where 50% lie below a certain score Mode – score that results most often

49. Format Cells Open Booklet Open File 1c)