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| Course Description |
|
| Course Name |
: |
Data Processing |
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| Course Code |
: |
BİS542 |
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| Course Type |
: |
Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
|
| Course Semester |
: |
Spring (16 Weeks) |
|
| ECTS |
: |
4 |
|
| Name of Lecturer(s) |
: |
InstructorDr. YAŞAR SERTDEMİR
Prof.Dr. HÜSEYİN REFİK BURGUT
|
|
| Learning Outcomes of the Course |
: |
The student knows how to use the different data bases
Prepares the data entry form via Epi info
Enters data using Excel, Access and SPSS
Knows to apply transformations to non normal data
Knows the definition of missing data and which method to apply
Knows how to transform data to be analyzed or how to transform repeated data sets proper to analysis method .(repeated or GEE analysis)
Knows the basic features of SPSS and R and how to simulate data using SPSS or R
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|
| Mode of Delivery |
: |
Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
To introduce the programs which are commonly used in data processing. To teach how to prepare data entry forms.
To teach the transformations which can be applied to normalize non normal distributed data.
to teach how to handle missing data problems using SPSS and R.
To prepare data using SPSS, R and Excel/Access for different analysis methods
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|
| Course Contents |
: |
What is epi info?, data entry in EPİ, editing of data in EPİ, data entry in Excell, Transformation for nonnormal data, procedures for the data with missing cases, introduction to R and basic commands, Simulation using SPSS and use of syntax command and simulating data using R |
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| Language of Instruction |
: |
Turkish+English |
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| Work Place |
: |
Informatics lab in Department of Biostatistics |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Course Introduction and Introduction to databases |
none |
|
|
2 |
Examination of the program Epi info |
epiinfo indirilmesi-www.cdc.gov/software |
Homework and reading |
|
3 |
The creation of data entry forms using Epi info. |
Reads the Epiinfo manual |
Homework and reading |
|
4 |
The creation of data entry forms using Epi info and validation of the data. |
Reads the Epiinfo manual |
Homework and reading |
|
5 |
Data entry using Excel and Access |
Reads the Excel-Access manual |
Homework and reading |
|
6 |
Data entry using SPSS and the use of syntax commands in SPSS |
Reads the references or help manual (SPSS) |
Homework and reading |
|
7 |
Detection of outliers |
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|
|
8 |
Transformation methods for non normal data |
Revises the distributions |
Homework and reading |
|
9 |
Methods used to deal with missing data problems |
reads about MCAR, MAR and MNR |
Homework and reading |
|
10 |
MID-TERM |
|
|
|
11 |
Introduction to R programming |
Reads the R manual |
Homework and reading |
|
12 |
Basic commands in R |
|
Homework and reading |
|
13 |
Introduction to simulation |
|
Homework and reading |
|
14 |
Data simulation using SPSS |
|
Homework and reading |
|
15 |
Data simulation using R |
|
Homework and reading |
|
16/17 |
Final |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Hamza Erol “SPSS paket programı ile istatistiksel veri analizi” Adana Nobel Kitabevi, 2010.
Alan Bryman and Duncan Cramer “Quantitative data analysis with SPSS 12 and 13 : a guide for social scientists London ; New York : Routledge, 2005.
Andy Field “Discovering statistics using SPSS for Windows: advanced techniques for the beginner” London : Sage Publications, 2003.
Geert Molenberghs, Michael G Kenward “ Missing Data in Clinical Studies” Wiley 2007
Brian S. Everitt and Torsten Hothorn ”A Handbook of Statistical Analyses Using R” London and Erlangen 2005.
Verzani J .Using R for Introductory Statistcs.
Crawley MJ. The R book.Wiley 2007.
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| |
| Required Course Material(s) |
http://wwwn.cdc.gov/epiinfo/index.htm
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|
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Assessment Methods and Assessment Criteria |
|
Semester/Year Assessments |
Number |
Contribution Percentage |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
50 |
|
Homeworks/Projects/Others |
7 |
50 |
|
Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
|
Final Assessments
|
100 |
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Rate of Final Assessments to Success
|
60 |
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Total |
100 |
|
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Students design scientific research studies in order to give response to the problem arising from health and clinical sciences |
0 |
|
2 |
Students provide consulting services by using effective communication skills; take part in research teamworks; defend the ethical rules. |
0 |
|
3 |
Students collect data from research studies, analyze, and make inferences |
0 |
|
4 |
Students design health survey, determine the sampling method and conduct the survey |
0 |
|
5 |
Students knows the system of international classification of diseases, obtain and analyze hospital statistics. |
0 |
|
6 |
Students select the appropriate statistical procedure for analysis , apply and make inferences. |
0 |
|
7 |
Students use the necessary statistical packages for analysis, if necessary write and develop software. |
5 |
|
8 |
Students select and use proper statistical procedure for diagnosis and in making inferences for the data in health and clinical medicine and provide consultance to clinicians in the field. |
0 |
|
9 |
Students comprehends the fundamentals of statistical theory related to the field of health ( probability and bayesian biostatistics). |
0 |
|
10 |
Students explain demographic terminologies and statistical methods in the field of health sciences. |
0 |
|
11 |
Students understand and use medical terminology. |
0 |
|
12 |
Students develop the ability of critical thinking, make a conclusion with a critical approach to the evidence |
0 |
|
13 |
Students apply analytical procedure to frequently used survival data, multivariate procedure and regression techniques. |
0 |
|
14 |
Students follow the latest development in medical informatics and employ frequently used tools and methods. |
4 |
|
15 |
Students explain the fundamental terminologies in epidemiology, guide researchers conducting field survey and clinical studies, develop methodologies in determining disease risk factor and disease burden and advise for choosing proper diagnostic test. |
0 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
|
Out of Class Study (Preliminary Work, Practice) |
14 |
2 |
28 |
| Assesment Related Works |
|
Homeworks, Projects, Others |
7 |
3 |
21 |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
3 |
3 |
|
Final Exam |
1 |
3 |
3 |
|
Total Workload: | 97 |
| Total Workload / 25 (h): | 3.88 |
| ECTS Credit: | 4 |
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