final spss hands on training (descriptive analysis) may 24th 2013
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TRANSCRIPT
SPSS Hands-on Training:
Descriptive analysis
by SPSS soft-ware
Dr 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
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
Data Management
• Data coding & data cleaning by manual method
• Data entering• Data cleaning by soft ware• Data Analysis - Descriptive analysis
& - Inferential analysis
Data coding & data cleaning by manual method
• Have You already collected Data!
• Have you already coded your data & cleaned manually?
• Have You already set a SPSS data sheet to enter your data?
• Have You had your own SPSS data set?
( 10- 20% of your sample size “n” is OK !)
Open SPSS soft- ware
Open existing file
Data Editor Window
Data editor window: Variable view
Go to variable view
-displays a table consistingof variable names and their attributes. -You can1. modify the properties
of each variable or 2. add new variables or 3. delete existing variables
in the variable view Window.
Variable view Window
Descriptive Statistics
• typical tools for exploring the descriptive summaries
• useful for data exploration
• useful for data cleaning. before any further analysis.
• P-P plots and Q-Q plots are useful for checking the distribution assumption required by statistical techniques.
Descriptive Analysis:Frequency ( eg-age )
Step-1
Step-2
Step-3 Step-4 SPSS –out put
Descriptive Analysis:Frequency ( eg-age group)
Step-1
Step-3 SPSS –out put
Age group
Step-2
Step-4
Quantitative Data
Data
Categorical
Ordinal
e.g <good, poor, fair>
Nominal
e.g. Color of teeth
Numerical
Continuous
<Age of students >
Discrete
<Number of teeth at 80 yr old>
Data management by SPSS:
Transform Menu:
• Transform data from continuous numerical data categorical /ordinal data nominal data
eg(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)
Data management by SPSS:
Transform Menu:
• Recode value of variable into same variable orinto different/new variable
eg(1) value of gender
( male = 1 , female =2 ) (male= M, female= F)
(2) response of smoking cessation
3 categories 2 categories
( no smoke= 1, still smoke = 2, trying to quit =3)
{( no smoke = 1], [ still smoke+ trying to quit = smoke= 2 ])
SPSS Pull down menu:Transform: ( recode into different variables)
Step-1 Step-2 Step-3
Step-4a new variable “age group” in
variable view
Descriptive analysis :
Distribution of DPH marks of 10 Dental Students
by SPSS-software
5/24/2013 17
Descriptive
Statistic Std. Error
DPH mark Mean 56.30 5.239
95% Confidence Interval for Mean Lower Bound 44.45
Upper Bound 68.15
5% Trimmed Mean 55.28
Median 50.00
Variance 274.456
Std. Deviation 16.567
Minimum 42
Maximum 89
Range 47
Interquartile Range 27
Skewness 1.096 .687
Kurtosis .051 1.334
Central Tendency
Dispersion
Shape
Descriptive Analysis: Descriptive
Step-1
SPSS –out put
Step-2
Step-3
Data management by SPSS:
Data Menu:
• Split the file
• One master file into 2 or 3 sub-files
master 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?
SPSS Pull down menu:Data ( split file)
Step-1 Step-2
SPSS –out put
SPSS –out put
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
Milestones of Dental 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)
Remarks!
• They did not try to win a prize for their research projects!
• Their research projects deserved to be awarded because of …
applicability of results of their research projects!
effort to get the valid results !
Validity of Questionnaires, SOP, Standardization,“Supervision” “27
Validity of Data!
Valid Data Analysis
1.A valid data analysis starts with a solid planning of the study
2.A valid data analysis must have a valid set of data ( supervision of your project supervisor)
3.Appropriate Statistical Procedures are the key to a correct analysis.
4. Appropriate analysis needs correct interpretation of the results.
I. A valid data analysis starts with a solid planning 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 intended characteristics,
- designing questionnaires, &
ways of distributing and collecting the survey.
I. A valid data analysis starts with a solid planning of the study.
• For a controlled experimental study,
• by considering the measurement,
• the potential confounding factors associated with the measurement,
• the intended factors for the experiment,
• the design of the experiment,
• experimental units, sample size, and possible statistical techniques for analysis based on the experimental design.
II. A valid data analysis must have a valid set of data
• to design a proper format for data entry
• Many times data are entered in such a way that it is not 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 most cases.
• It is important that proper data values be created so that statistical software ( SPSS) can perform the analysis.
II.A valid data analysis must have a valid set of data
• After data entry, it is the data cleaning & manipulation stage.
• It can happen that some data points are entered completely out of range.
• A quick way of locating these out-of-range data values is by performing frequency procedures or descriptive procedures, and check the output results to see if any variable has such a problem.
• Data transformation is often used before a valid analysis can be performed.
III. Appropriate Statistical Procedures are the key to a correct analysis.
• Almost every statistical procedure has assumptions behind it.
• It is necessary to carefully consider the violation of the assumptions for a statistical procedure.
• A minor violation usually does not create serious problems.
• However, if there is a serious violation, appropriate data transformation or selecting different statistical procedures may be necessary.
III. Appropriate Statistical Procedures are the key to a correct analysis.
• It is often the case that appropriate statistical procedures are associated with the types of data.
• Categorical data needs to be analyzed using procedures that are developed for analyzing categorical data.
• We do not perform frequency analysis or cross-tabulation procedures to analyze continuous data.
• It happens often in data analysis that one needs to conduct several analyses before an appropriate one is selected.
• One should expect that the analysis is never only a one step process. It involves many back and forth analyses and decisions for a proper analysis.
IV. Appropriate analysis needs correct interpretation of the results.
• How to interpret and summarize the results from a huge pile of output is certainly a crucial step for a valid data analysis.
• It involves the understanding of the project, the statistical techniques and how to bring the numbers into the context of the project.
• One must make sure that the output is properly interpreted and summarized to a degree that non-statisticians can understand them.
V. Data Types and Analysis
• Generally speaking, statistical techniques are often determined based on the type of data!!
General Considerations
- There is no best way to conduct a quantitative study.
- Different projects involve different considerations of the contexts behind the study.
- Without proper understanding of the contexts of
the study that are associated with the project, the
quantitative study will be purely empirical.
- The empirical results may not be able to answer the root causes of the problem. ( Your research question!)
Overview of Data Analysis
• Hence, it is crucial to thoroughly investigate the context behind the project before a proper plan and design of a quantitative study is conducted.
• The common aspects related to the contexts behind the study other than the intended quantitative measurements that need to be addressed may include, but not limited to:
- External environmental conditions
- Background of the subjects
- Possible factors associated with theintended measurement.
- Common sense and logic
