SPSS Hands-on Training:Descriptive analysisby SPSS soft-wareDr Tin Myo HanM.B;B.S, M.Med.Sc(P.H), M.P.H ( Belgium), Diplom...
SPSS soft-ware Hands-on training 2:Step by Step for descriptive statistics• Hands-on demonstration- Frequency tables- Tran...
Data Management• Data coding & data cleaning by manualmethod• Data entering• Data cleaning by soft ware• Data Analysis - D...
Data coding & data cleaning bymanual method• Have You already collected Data!• Have you already coded your data &cleaned m...
Open SPSS soft- wareOpen existing fileData Editor Window
Open your FileOpen your fileData Editor Window
Open data file
Data editor window: Variable viewGo to variable view-displays a table consistingof variable names andtheir attributes.-You...
Analysis of Data
Descriptive Statistics• typical tools for exploring thedescriptive summaries• useful for data exploration• useful for data...
Descriptive Analysis:Frequency ( eg-age )Step-1Step-2Step-3 Step-4 SPSS –out put
Descriptive Analysis:Frequency ( eg-age group)Step-1Step-3 SPSS –out putAgegroupStep-2Step-4
Quantitative DataDataCategoricalOrdinale.g <good,poor, fair>Nominale.g. Color ofteethNumericalContinuous<Age of students >...
Data management by SPSS:Transform Menu:• Transform data from continuous numerical data categorical /ordinal data  nomina...
Data management by SPSS:Transform Menu:• Recode value of variable into same variable orinto different/new variableeg(1) va...
SPSS Pull down menu:Transform: ( recode into different variables)Step-1 Step-2 Step-3Step-4a new variable “age group” inva...
Descriptive analysis :Distribution of DPH marks of 10 Dental Studentsby SPSS-software5/24/2013 17DescriptiveStatistic Std....
Descriptive Analysis: DescriptiveStep-1SPSS –out putStep-2Step-3
Data management by SPSS:Data Menu:• Split the file• One master file into 2 or 3 sub-filesmaster file age of all participa...
SPSS Pull down menu:Data ( split file)Step-1 Step-2SPSS –out putSPSS –out put
SPSS Pull down menu: Analysis by Graph
Today ! Exercise with Your Data• a SPSS file for respective research data entry• Analysis:• (1)Frequency table to check da...
Overview of Data Analysis
Milestones ofDental students research project(KOD, IIUM)• 1st batch: 4th Malaysian Dental student conferences (2012)3rd pr...
Remarks!• They did not try to win a prize for theirresearch projects!• Their research projects deserved to beawarded becau...
Overview of Data Analysis
Validity of Questionnaires, SOP, Standardization,“Supervision” “27Validity ofData!
Valid Data Analysis1.A valid data analysis starts with a solid planningof the study2.A valid data analysis must have a val...
I. A valid data analysis starts with a solidplanning of the study.• For a survey study or an observational study --the ade...
I. A valid data analysis starts with a solidplanning of the study.• For a controlled experimental study,• by considering t...
II. A valid data analysis must have avalid set of data• to design a proper format for data entry• Many times data are ente...
II.A valid data analysis must have avalid set of data• After data entry, it is the data cleaning &manipulation stage.• It ...
III. Appropriate Statistical Proceduresare the key to a correct analysis.• Almost every statistical procedure hasassumptio...
III. Appropriate Statistical Proceduresare the key to a correct analysis.• It is often the case that appropriate statistic...
IV. Appropriate analysis needs correctinterpretation of the results.• How to interpret and summarize the resultsfrom a hug...
V. Data Types and Analysis• Generally speaking, statisticaltechniques are often determined basedon the type of data!!
General Considerations- There is no best way to conduct a quantitativestudy.- Different projects involve different conside...
Overview of Data Analysis• Hence, it is crucial to thoroughly investigate thecontext behind the project before a proper pl...
Final spss  hands on training (descriptive analysis) may 24th 2013
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Final spss hands on training (descriptive analysis) may 24th 2013

  1. 1. SPSS Hands-on Training:Descriptive analysisby SPSS soft-wareDr Tin Myo HanM.B;B.S, M.Med.Sc(P.H), M.P.H ( Belgium), Diploma in Family Medicine(Malaysia)24th May2013 KOD, IIUM, Kuantan
  2. 2. SPSS soft-ware Hands-on training 2:Step by Step for descriptive statistics• Hands-on demonstration- Frequency tables- Transformation of Data- Descriptive analysis- splitting data file• Hands-on exercise• Overview of data Analysis- Valid data analysis
  3. 3. Data Management• Data coding & data cleaning by manualmethod• Data entering• Data cleaning by soft ware• Data Analysis - Descriptive analysis&- Inferential analysis
  4. 4. Data coding & data cleaning bymanual method• Have You already collected Data!• Have you already coded your data &cleaned manually?• Have You already set a SPSS data sheet toenter your data?• Have You had your own SPSS data set?( 10- 20% of your sample size “n” is OK !)
  5. 5. Open SPSS soft- wareOpen existing fileData Editor Window
  6. 6. Open your FileOpen your fileData Editor Window
  7. 7. Open data file
  8. 8. Data editor window: Variable viewGo to variable view-displays a table consistingof variable names andtheir attributes.-You can1. modify the propertiesof each variable or2. add new variables or3. delete existing variablesin the variable viewWindow.Variable view Window
  9. 9. Analysis of Data
  10. 10. Descriptive Statistics• typical tools for exploring thedescriptive summaries• useful for data exploration• useful for data cleaning.before any further analysis.• P-P plots and Q-Q plots areuseful for checking thedistribution assumptionrequired by statisticaltechniques.
  11. 11. Descriptive Analysis:Frequency ( eg-age )Step-1Step-2Step-3 Step-4 SPSS –out put
  12. 12. Descriptive Analysis:Frequency ( eg-age group)Step-1Step-3 SPSS –out putAgegroupStep-2Step-4
  13. 13. Quantitative DataDataCategoricalOrdinale.g <good,poor, fair>Nominale.g. Color ofteethNumericalContinuous<Age of students >Discrete<Number of teeth at80 yr old>
  14. 14. Data management by SPSS:Transform Menu:• Transform data from continuous numerical data categorical /ordinal data  nominal dataeg(1) Age  age groups ( < 5 yrs, 6-10 year, > 11 years ]younger age group, elder age group(2) DMFT value  high caries /low caries group(3) CFU/bacterial count in contaminated water acceptable level / unacceptable level! Cut-off level (drinking water/ domestic use/ industrial use)
  15. 15. Data management by SPSS:Transform Menu:• Recode value of variable into same variable orinto different/new variableeg(1) value of gender ( male = 1 , female =2 ) (male= M, female= F)(2) response of smoking cessation3 categories  2 categories( no smoke= 1, still smoke = 2, trying to quit =3){( no smoke = 1], [ still smoke+ trying to quit = smoke= 2 ])
  16. 16. SPSS Pull down menu:Transform: ( recode into different variables)Step-1 Step-2 Step-3Step-4a new variable “age group” invariable view
  17. 17. Descriptive analysis :Distribution of DPH marks of 10 Dental Studentsby SPSS-software5/24/2013 17DescriptiveStatistic Std. ErrorDPH mark Mean 56.30 5.23995% Confidence Interval for Mean Lower Bound 44.45Upper Bound 68.155% Trimmed Mean 55.28Median 50.00Variance 274.456Std. Deviation 16.567Minimum 42Maximum 89Range 47Interquartile Range 27Skewness 1.096 .687Kurtosis .051 1.334CentralTendencyDispersionShape
  18. 18. Descriptive Analysis: DescriptiveStep-1SPSS –out putStep-2Step-3
  19. 19. Data management by SPSS:Data Menu:• Split the file• One master file into 2 or 3 sub-filesmaster file age of all participants( smokers + non smokers)• Age distribution of all participants(min, max and mean ± SD )-age distribution of smoker group?-age distribution of non-smoker group?
  20. 20. SPSS Pull down menu:Data ( split file)Step-1 Step-2SPSS –out putSPSS –out put
  21. 21. SPSS Pull down menu: Analysis by Graph
  22. 22. Today ! Exercise with Your Data• a SPSS file for respective research data entry• Analysis:• (1)Frequency table to check data &(2) descriptive statistics of continuous variables• Enter you data
  23. 23. Overview of Data Analysis
  24. 24. Milestones ofDental students research project(KOD, IIUM)• 1st batch: 4th Malaysian Dental student conferences (2012)3rd prize in oral presentation (Individual group)• 2nd batch: 5th Malaysian Dental student conferences (2013)1st prize in oral presentation(Individual group)“Champion” in overall results( University)( Out of 14 Universities, ties with IMU, USM)
  25. 25. Remarks!• They did not try to win a prize for theirresearch projects!• Their research projects deserved to beawarded because of …applicability of results of theirresearch projects!effort to get the valid results !
  26. 26. Overview of Data Analysis
  27. 27. Validity of Questionnaires, SOP, Standardization,“Supervision” “27Validity ofData!
  28. 28. Valid Data Analysis1.A valid data analysis starts with a solid planningof the study2.A valid data analysis must have a valid set ofdata ( supervision of your project supervisor)3.Appropriate Statistical Procedures are the keyto a correct analysis.4. Appropriate analysis needs correctinterpretation of the results.
  29. 29. I. A valid data analysis starts with a solidplanning of the study.• For a survey study or an observational study --the adequate measurement,-the target population,-sampling techniques, sample size,-factors associated with the intendedcharacteristics,- designing questionnaires, &ways of distributing and collecting the survey.
  30. 30. I. A valid data analysis starts with a solidplanning of the study.• For a controlled experimental study,• by considering the measurement,• the potential confounding factors associatedwith the measurement,• the intended factors for the experiment,• the design of the experiment,• experimental units, sample size, and possiblestatistical techniques for analysis based on theexperimental design.
  31. 31. II. A valid data analysis must have avalid set of data• to design a proper format for data entry• Many times data are entered in such a way that it isnot readable by statistical software.• data values can only be either numeric or non-numeric( String)• Numeric values can be quantified; while non-numeric values can only be summarized in mostcases.• It is important that proper data values be created sothat statistical software ( SPSS) can perform theanalysis.
  32. 32. II.A valid data analysis must have avalid set of data• After data entry, it is the data cleaning &manipulation stage.• It can happen that some data points areentered completely out of range.• A quick way of locating these out-of-rangedata values is by performing frequencyprocedures or descriptive procedures, andcheck the output results to see if any variablehas such a problem.• Data transformation is often used before avalid analysis can be performed.
  33. 33. III. Appropriate Statistical Proceduresare the key to a correct analysis.• Almost every statistical procedure hasassumptions behind it.• It is necessary to carefully consider the violationof the assumptions for a statistical procedure.• A minor violation usually does not createserious problems.• However, if there is a serious violation,appropriate data transformation or selectingdifferent statistical procedures may benecessary.
  34. 34. III. Appropriate Statistical Proceduresare the key to a correct analysis.• It is often the case that appropriate statistical proceduresare associated with the types of data.• Categorical data needs to be analyzed using proceduresthat are developed for analyzing categorical data.• We do not perform frequency analysis or cross-tabulationprocedures to analyze continuous data.• It happens often in data analysis that one needs toconduct several analyses before an appropriate one isselected.• One should expect that the analysis is never only a onestep process. It involves many back and forth analysesand decisions for a proper analysis.
  35. 35. IV. Appropriate analysis needs correctinterpretation of the results.• How to interpret and summarize the resultsfrom a huge pile of output is certainly acrucial step for a valid data analysis.• It involves the understanding of the project,the statistical techniques and how to bringthe numbers into the context of the project.• One must make sure that the output isproperly interpreted and summarized to adegree that non-statisticians can understandthem.
  36. 36. V. Data Types and Analysis• Generally speaking, statisticaltechniques are often determined basedon the type of data!!
  37. 37. General Considerations- There is no best way to conduct a quantitativestudy.- Different projects involve different considerationsof the contexts behind the study.- Without proper understanding of the contexts ofthe study that are associated with the project, thequantitative study will be purely empirical.- The empirical results may not be able to answerthe root causes of the problem. ( Your researchquestion!)
  38. 38. Overview of Data Analysis• Hence, it is crucial to thoroughly investigate thecontext behind the project before a proper plan anddesign of a quantitative study is conducted.• The common aspects related to the contexts behindthe study other than the intended quantitativemeasurements that need to be addressed mayinclude, but not limited to:- External environmental conditions- Background of the subjects- Possible factors associated with theintended measurement.- Common sense and logic

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