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Spss basic Dr Marwa Zalat


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Statistical pacakage for social science Basic

Published in: Data & Analytics
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Spss basic Dr Marwa Zalat

  1. 1. Expectations
  2. 2. ILOs To describe how to use SPSS for conducting basicstatistical analysis and interpret the output of the analysis.  Preparing SPSS file for data entry  Displaying data  Methods of presenting and Summarizing data  Graphical presentation of data By the end of this workshop you will be able to: ‫االهداف‬
  3. 3. SPSS made easy
  4. 4. SPSS •STANDS FOR STATISTICAL PACKAGE FOR SOCIAL SCIENCE ‫اإلجتماعية‬ ‫العلوم‬ ‫في‬ ‫اإلحصائية‬ ‫الحزم‬
  5. 5. Variable , Data • Variables ‫:متغيرات‬ These are observations, which vary from one person to another or from one group of members to others as: age, weight, blood pressure, sex. • Data ‫البيانات‬: Value of the variable. • Body weight= 70 KG
  6. 6. Example: You are conducting a research to see if proper infection control training for nurses decreases prevalence of needle stick injury. Dependent variable (DV): Decreases prevalence of needle stick injury. Independent variable (ID): proper infection control training
  7. 7. Data 1.Quantitative (numerical):  Continuous quantitative: e.g. age, weight, height  Discrete quantitative: e.g. no of patients, number of children per family. 2.Qualitative (non numerical)/categorigcal: Nominal qualitative: e.g. (blood grouping A, B, AB, O),(sex: male and female) Ordinal qualitative: e.g. (mild, moderate, severe)
  8. 8. An icon next to each variable provides information about data type Scale (Continuous) Ordinal Nominal
  9. 9. Session 1 cont.: How to design questionnaire in SPSS How to enter data in SPSS •SPSS Windows Variable view Data view • Coding and Design questionnaire in SPSS • Data entry Copy, paste from excel into SPSS From SPSS, open existing excel file Create new file directly from SPSS
  10. 10. SPSS opening window
  11. 11. 15 SPSS Variable View
  12. 12. 16 SPSS Data View
  13. 13. 17
  14. 14. Practical training 1: (10 min.) •Please each one design the questionnaire with you on SPSS and enter data
  15. 15. N.B How to Code questions with more than one answer What are risks, adverse effects do you know of oral Isotretinoin therapy? (You can choose more than one answer): 1. Teratogenicity 2. Dryness 3. Constipation 4. Lipid profile disturbance 5. Hepatic side effects 6. Depression 7. Anemia 8. Others…………………. 9. I don’t know Each item in answer enter as a separate variable with yes, no answer
  16. 16. How to Code open ended questions • Read through responses • Create a preliminary code based on responses • Put responses into category and code it • Try not to have more than 10 categories, with no individual category receiving less than 5% of responses. • Also, there is software that can be used to help you code open-ended responses.
  17. 17. Session 2: How to perform data cleaning/manipulation? Use data set 1 • Data files are not always organized to meet specific user needs, user may need to select specific group, split data file into separate group for analysis • Copy, paste: age, duration, HbA1C From Data menu 1. Select case: first 20 cases, male only, Saudi, age <45ys 2. Split file: by sex, nationality 3. Sort cases: duration, HbA1C (ascending, descending) From SPSS dialog box, go to: Data Select cases, Sort cases, Split file
  18. 18. Practical training 2 Data transformation Use data set 1 From transform menu 1. Recode into different variable: Age: from number to groups (1- <25y, 2- ≥25y) Duration 1- <5, 2- ≥5 HBA1C 1- <6.5, 2- ≥6.5 1. Recode into same variable 2. Compute variable: Mean, %, BMI ‫ضرورى‬ ‫االقواس‬ From SPSS dialog box, go to: Transform Recode Into Same variables Into different variables
  19. 19. Methods of presentation Numerical Graphical Qualitative Bar Pie Quantitative Histogram Frequency polygon Box plot Tabular • Frequency tables • Cross tabulations Mathematical • Mean • Median • Mode • Rang, IQR • SD
  20. 20. Session 3: Descriptive statistics, Data presentation • After data entry, it can be analyzed using descriptive statistics Purpose: • To find wrong entries, have basic knowledge on the sample, summarize data •Tabular presentation  Frequency analysis (simple frequency table)  Crosstabs (2 X 2 table and C x R)
  21. 21. 1- Simple frequency table From the menu choose: Analyze > Descriptive Statistics > Frequencies... e.g. (sex, nationality, compliance and comments)
  22. 22. Output
  23. 23. So you can do frequency to filter data, detect missed one Missed data Missing Values for a Numeric Variable • Type 999 in the Value field. Missing Values for a String Variable • Type NR (no response) in the Value field. A value is missing may be important to your analysis. For example, you may find it useful to distinguish between those respondents, and non respondents
  24. 24. Characteristics of good table: 1. Simple 2. Self explanatory 3- Explaining abbreviation 4- Columns and rows labeled clearly 5- Unites of measures should be written 6- Title: Every table should have a title, above the table, which is clear and answers, as possible as you can, four questions (what, who, where and when).
  25. 25. 2- Crosstabs • Crosstabs are used to examine the relationship between two variables Analyze > Descriptive Statistics >crosstabs • e.g. 2x2 (sex & nationality)
  26. 26. Statistics iconCells icon
  27. 27. Output
  28. 28. Odds Ratio (OR) • The odds ratio is the odds of outcome occurrence in one group divided by the odds of outcome occurrence in the comparison group • Analysis of case-control studies • If the OR = 1 there is no difference between the two groups • If the OR >1 this exposure is risk factor for occurrence of disease • If the OR <1 this exposure is protective factor for occurrence of disease Relative risk (RR)  RR indicates how many times those exposed are likely to develop the disease relative to non-exposed. • Analysis of cohort studies RR= 1: the exposure is not associated with the disease. RR > 1: the exposure is a risk RR < 1: the exposure is a protective
  29. 29. Practical training 3: •Using data set 1 exercise •Descriptive statistics: Simple frequency table for sex, compliance •Crosstabs: 2 X 2 table: relation between gender and nationality. •C x R: Association of compliance to treatment and gender
  30. 30. Statistical tests A-Parametric tests 1-Normal distribution data B-Non parametric tests Not normally distributed data
  31. 31. Methods of data presentation Central Tendency Mathematical Dispersion Mean Median Mode Range Interquartile range Standard Deviation
  32. 32. Central Tendency
  33. 33. Mean, Median, Mode Analyze > Descriptive Statistics >Descriptives
  34. 34. Output
  35. 35. Mean, Median, Mode Analyze > Descriptive Statistics >Frequencies
  36. 36.  1- The Range  2- Interquartile range: Max – Min = Range  3- Standard deviation (SD): Measures of Dispersion/scatter/spread
  37. 37. Quartiles 2nd quartile median 3rd quartile 75th percentile 75% 50% 1st quartile 25th percentile 25%
  38. 38. For calculating dispersion measures: Analyze > Descriptive Statistics >Frequencies Analyze > Descriptive Statistics >explore Exercise Calculate Mean, SD of: Age
  39. 39. Practical Training 3 Perform descriptive analysis for the variables: • Age and HbA1C (mean, median, SD, range, min, max ) and write the comment on table
  40. 40. Percentiles  A percentile or (centile) is the value below which a certain percentage of observations fall.  For example, the 10th percentile is the value below which 10 percent of the observations may be found.  Often used to compare an individual value with a norm. e.g. physical growth charts for children e.g. weight for age chart
  41. 41. SD, SEM • SEM measures the variability of the mean of the sample as an estimate of the true value of the mean of the population from which the sample was drawn ‫يستخدم‬‫البعض‬‫الخطأ‬‫المعيارى‬‫للمتوسط‬‫كأحد‬ ‫مقاييس‬‫التشتت‬‫وهو‬‫ما‬‫يعتبر‬‫من‬‫األخطاء‬ ‫الشائعة‬‫حيث‬‫ال‬‫يعبر‬‫الخطأ‬‫المعيارى‬‫عن‬‫التباين‬ ‫وال‬‫عن‬‫مدى‬‫االختالف‬‫الموجود‬‫داخل‬‫البيانات‬
  42. 42. Practice: Try calculate SD,SEM for age
  43. 43. Graphical presentation of data
  44. 44. How to present data by graph ? Graphical Qualitative Bar Pie Quantitative Histogram Frequency polygon Box plot
  45. 45. Bar chart • This type of graph is suitable to represent data of the two subtypes of qualitative and quantitative discrete type • Analyze> descriptive statistics> frequency> chart> bar chart • Graphs > legacy dialogs>bar
  46. 46. Types of bar charts: Simple bar chart Multiple/grouped bar chart Segmented/stacked bar chart
  47. 47. Histogram Continuous quantitative data Pie chart For all the four types of variables  Angle = 𝐟𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲 𝐨𝐟 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐲 𝐨𝐫 𝐢𝐧𝐭𝐞𝐫𝐯𝐚𝐥 𝐭𝐨𝐭𝐚𝐥 𝐟𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲 × 360
  48. 48. Box plot ( often called box and whisker plot) • This is a vertical or horizontal rectangle, with the ends of the rectangle corresponding to the upper and lower quartiles of the data values. • A line drawn through the rectangle corresponds to the median value. • Whiskers, starting at the ends of the rectangle, usually indicate minimum and maximum values. 54
  49. 49. Box plot Graphs > legacy dialogs> box plot
  50. 50. Scatter plot  The dependent variable on the vertical axis (the y-axis)  The independent variable on the horizontal axis (the x-axis).
  51. 51. The value of (r) ranges between ( -1) and ( +1)
  52. 52. Pareto chart (Analyze> quality control> pareto) • Is a vertical bar graph in which values are plotted in decreasing order of relative frequency from left to right. • Is one of the seven basic tools of quality control. Useful for analyzing what problems need attention first. • Pareto principle (80/20 rule), is a theory maintaining that 80 percent of the output from a given situation or system is determined by 20 percent of the input. • Pareto chart guiding how to solve 80% of the problem.
  53. 53. Quiz: which cause could solve 80% of problem Pareto chart ranking perceived problems of food service providers at dietary department
  54. 54. Area chart • A way to quickly and easily visualize how well the students in your class were doing over the course of the year. • A way to show the average exam scores throughout the course on an area chart. • Show a trend over time
  55. 55. Practical 3: • Using data set 2 exercise to make: • Pie graph for sex , Bar chart for compliance • Bar graph for compliance with sex • Bar chart for compliance of females only • Histogram of age, duration • Histogram of female height/ male height • Box plot for age
  56. 56. References • SPSS for the Classroom: the Basics ents1.htm • California state university. IBM SPSS statistics 20. part 1 descriptive statistics. • IBM SPSS Statistics 20 Brief Guide. • www. ‫صالحا‬ ‫كله‬ ‫عملنا‬ ‫اجعل‬ ‫اللهم‬
  57. 57. ‫اللهم‬‫به‬ ‫ينتفع‬ ‫علما‬ ‫اجعله‬