1. 1 Tutorial 1 GE 5 Conducting research khan.j@nhtv.nl vjkhan.com @v_j_khan

2. 2 Rules  No use of mobile devices unless told  Computer use strictly for tutorial purposes  Failure to comply means immediate expelling from class which might also have other consequences

3. 3 Attitude  STUDENT TYPE I  Excuses  Complaints  9 to 5  Procrastinate  Bored  STUDENT TYPE II  Overcomes obstacles  Suggestions  Beginning to end  Hard work  Will find interest in it

4. 4 Round of Introductions  Name  Experience with statistics  Why is statistics useful for a media manager?

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8. 8 Participant wearing biometric sensors

9. 9 Plan for today Introduction to statistics Variables and frequencies Purpose of statistics Tables and diagrams Terminology The BIG Smartie survey SPSS workshop

10. 10 Building blocks statistical theory data collection data collection data analysis data analysis reporting presenting reporting presenting Howitt & Cramer Limesurvey SPSS Matthews

11. 11 Introduction statistics (chapter 1 and 2)

12. 12 Variables Nominal / category Examples: cigarette brand (Camel, Marlboro, other), religious affiliation (Roman Catholic, Dutch Reformed, Calvinist, other Christian, Muslim, other religion, none). Score / numerical Examples: temperature of the smoke in °C, golf scores (holes above or below par), year, time to complete a task, age, height.

13. 13 Variables Dichotomous. Examples: being a smoker (yes/no), gender (male/female). Other nominal. Examples: cigarette brand (Camel, Marlboro, other), religious affiliation (Roman Catholic, Dutch Reformed, Calvinist, other Christian, Muslim, other religion, none). Ordinal. Examples: frequency of smoking (never, incidentally, daily), rank in competion, clothing sizes (S,M,L,XL), attitude toward organic vegetables (positive, neutral, negative). Interval. Examples: temperature of the smoke in °C, golf scores (holes above or below par), year. Ratio. Examples: time to complete a task, age, height.

14. 14 Discrete versus continuous variables.. Discrete: The variable has a certain fixed values. i.e. ranking 1 st, 2 nd, etc. Continuous: a continuous variable is a variable that can have any value you can imagine. Example: weight. A weight is 5 kilograms… But I can measure it also in grams… then it might be 5025 grams…

15. 15 Variables and data variable: characteristic or condition that changes or has different values for different individuals value: possible outcome code: a number for a score data: measurements or observations of a variable data set: a collection of measurements or observations. score: a single measurement or observation

16. 16 Populations and samples population: the set of all the individuals or objects of interest in a particular study sample: a set of individuals or objects selected from the population, usually intended to represent the population in a research study

17. 17 Populations and samples

18. 18 Population and samples parameter: a value that describes a population statistic: a value that describes a sample sampling error: the discrepancy, or amount of error, that exists between a sample statistic and the corresponding population parameter

19. 19 watch soaps do not watch soaps Sample with a lot of sampling error

20. 20 Descriptive versus inferential statistics Statistics serve two general purposes: descriptive statistics: statistics is used to present data in a convenient way: tables, graphs and figures (summarize, organize and simplify the data). inferential statistics: statistics is used to generalize from the sample to the population (it uses information from a sample to draw conclusions – inferences – about the population from which the sample was taken).

21. 21 Functions of statistics  descriptive statistics inferential statistics individual variables frequency distribution, means, etc. for example election research to predict the percentage of votes that a party will receive relationshi ps between variables cross tabulation, compare means, scatter plot, correlation coefficient, etc. hypotheses, significance testing

22. 22 Statistical notation ’ ’ statistics uses basic mathematical operations and notation, but also some specific notation: X: scores are referred to as X (and Y etc.) N: is the number of scores in a population n: the number of scores in a sample Σ: the frequently used symbol (Greek capital S) stands for ‘summation’

23. 23 Presentation modes raw data (data matrix) tables diagrams measures words

24. 24 Raw data

25. 25 Table

26. 26 (Bar) graphs

27. 27 (Histogram) Graphs

28. 28 47 (Line) graph

29. 29 Frequency curve

30. 30 Choosing the right graphs For nominal variables and for discrete numerical variables a bar graph is used instead of a histogram Only difference: spaces between bars The spaces between adjacent bars indicate discrete categories without order (nominal), categories of unmeasurable width (ordinal) or the non- existence of values between the categories (discrete variables)

31. 31 Frequency distribution a frequency distribution shows the number of individuals located in each category can be either a table or a graph the table or the graph shows: (1) the categories that make up the scale, (2) the frequency, or number of individuals

32. 32 Frequency distribution (example)

33. 33 Frequency distribution

34. 34 do the The BIG smartie survey

35. 35 CREATE TEAMS  DO NOT VISIT MOODLE WEBSITE UNLESS INSTRUCTED!  Groups of 2  Decide who are you partnering with  In case of an odd number of class members: 1 group of 3  WAIT until you are pointed at. Groups will register one by one Do not make a mess!

36. 36 steps 1 st team member choses team 2 nd team member joins team 1 person uploads file from assignment ( according guidelines on Moodle) !!!! if you are not part of a team you will not receive a mark!!!!!

37. 37 SPSS

38. 38 Functions of SPSS data input data processing statistical analysis presentation results

39. 39 SPSS WORKSHOP

40. 40 SPSS OVERVIEW Data view Variable view Output (on Viewer) Frequencies Select cases Transform -> Recode into different vars

41. 41 Exercise  For 10 students:  Gender  Height

42. 42 Menu READ AND WRITE FILES (Menu: File) Open (Data files, Output file, Syntax files) Save / Save as … DATA DEFINITIONS (Menu: Data or Data View) Insert variables Define variable properties Select cases DATA TRANSFORMATIONS (Menu: Transform) Recode Count Compute STATISTICAL PROCEDURES (Menu: Analyze) Descriptive Statistics > Frequencies etc.

43. 43 Variable view switch to SPSS name type (numerical, …, string) width decimals label values missing measure (scale, ordinal, nominal)

44. 44 Variable types numeric: figures (real figures and codes) string: names etc. (no mean calculation possible etc.) other: less important (dates etc.)

45. 45 Download the big smartie survey dataset from moodle Open the file in SPSS We will give A Guided tour Variable view Date View Menu options ( basic) Cleaning and making the Dataset ready for analysis Example

46. 46 Workshop Variable View Elements name type (width) decimals label values missing (columns) (align) measure (role)

47. 47 Workshop Variable View case Variable score DATASET

48. 48 Workshop Cleaning Dataset Take a look at the dataview. -What do you notice in the dataset -Clean your dataset -Select the FIRST variable and SORT ( right mouse click) -Select the cases that are empty -Delete the cases

49. 49 Workshop Cleaning Dataset Take a look at the dataview. -What do you notice in the dataset -Clean your dataset -Select the FIRST variable and SORT ( right mouse click) -Select the cases that are empty -Delete the cases -Check the other cases if there are cases that do not contain any relevant information ( delete those cases too)

50. 50 Workshop Setting the correct variable information Take a look at the variable view. -What do you notice in the variable view? -Correct the incorrect variables -Type -Measure -Check the labels

51. 51 Workshop SPSS Frequency tables  Create a Frequency table for one of the variables  Analyse  Descriptives  Frequencies choose variable create table  Make table APA (academic) style  Make clear what the table is about ( title)

52. 52 Missing values  specify missing values to remove missing from the dataset  answers in your analysis: Go to the screen DATA VIEW Find missing values Give them a impossible value ( i.e. 5 point scale value 99) Go to screen ‘Variable View’ … click on the grey square in [MISSING] column provide (one or more) missing values (99) close with [OK]

53. 53 Tuning SPSS Edit > Options > General: ◙ display names ◙ no scientific notation for small numbers Edit > Options > Viewer: ◙ display commands in the log Edit > Options > Output labels: names and labels and values and labels

54. 54 HOMEWORK