Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
708002122016APPLIED STATISTICSCompulsory243
Level of Course Unit
First Cycle
Objectives of the Course
The aim of this course is to help sociology students to be able to do statistical analyses on a field research data and to be able to read, understand and properly interpret the statistical findings
Name of Lecturer(s)
Göknur (Bostancı) Ege
Learning Outcomes
1identifying the basis of statistical analyses applied in the social sciences
2having knowledge on basic classifications like that of variable, research universe, and sampling types.
3making right decisions on which statistical analyses appropriate for a research data
4entering and analyzing data by using an statistical applications software
5be able to read and interpret accurately the results of statistical analyses, tables and graphs
6understanding the related terminology in English
Mode of Delivery
Face to Face
Prerequisites and co-requisities
Competence in English: to be able to read, write, understand and speak in English
Recommended Optional Programme Components
The students are recommended to take previous course “Statistics” in first semester before attending the course. The students are recommended to do further practices after the class and do further reading to get better understanding for developing their own thinking and being aware of different approaches on the course subjects
Course Contents
The course content covers following subjects: introduction to applied statistics, basic terminology, theories and statisticians influential in the history of applied statistics. theory of probability, research universe and sample types; finite and infinite universes, randomness in sampling, random and non-random samples, central limit theorem, representative and unbiased sampling, calculating sample size, variable types (discrete/continuous, qualitative, quantitative, dependent/independent) data and data and scale types (nominal, ordinal, interval, ratio), significance, reliability and tolerance, parameters of a research universe, describing normal distribution, standard deviance, mean, median variance through illustrations, correlation, regression and multiple regression by examples, one way annova, two way annova with illustrations, cross tables and Chi square test with illustrations, SPSS data entry, defining values and variables, illustrative applications of specific statistical analyses, How to read, understand and evaluate results of statistical analyses ( number, percent, valid percent, cumulative percent in a frequency table, column percent, row percent, total percent in a cross table etc.)
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
1Introduction to applied statistics, definitions of basic terminology
2Introducing theories and statisticians influential in the history of applied statistics. theory of probability
3Research universe and sample types; finite and infinite universes, randomness in sampling, random and non-random samples, central limit theorem
4Representative and unbiased sampling, calculating sample size
5Variable types (discrete/continuous, qualitative, quantitative, dependent/independent) data and data and scale types (nominal, ordinal, interval, ratio)
6Significance, reliability and tolerance
7Parameters of a research universe, describing normal distribution, standard deviance, mean, median variance through illustrations
8Mid-Term Exam
9Correlation, regression and multiple regression by examples
10One way annova, two way Annova with illustrations
11Cross Tables and Chi Square test with illustrations
12SPSS Data entry, defining values and variables
13Missing Values, recoding, weighting
14Illustrative applications of specific statistical analyses
15How to read, understand and evaluate results of statistical analyses ( number, percent, valid percent, cumulative percent in a frequency table, column percent, row percent, total percent in a cross table etc
16Final Exam
Recommended or Required Reading
Ozkan Unver , Hamza Gamgam, Uygulamali Istatistiksel yontemler, Siyasal Kitabevi 1996 Darrel Huff, Istatistik ve Istatistikle Nasil Yalan Soylenir, Cen :Engin Koparan, Sarmal Yayinevi , 2002 Seref Kalayci (Ed) SPSS Uygulamali Cok Degiskenli Istatistik Teknikleri, Asil Yayin 2005. Yahsi Yazicioglu, Samiye Erdogan, SPSS Uygulamali bilimsel Arastirma yontemleri, Detay Yayincilik, 2004 Burhan Cil, Istatistik, Detay Yayincilik, 2005. Fazil Guler, Temel Istatistik, Beta yayim, 2007
Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
SUM0
End Of Term (or Year) Learning ActivitiesQuantityWeight
SUM0
SUM0
Language of Instruction
English
Work Placement(s)
None
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination111
Final Examination111
Attending Lectures14228
Practice14114
Question-Answer15115
Self Study10110
Individual Study for Mid term Examination11010
Individual Study for Final Examination21020
TOTAL WORKLOAD (hours)99
Contribution of Learning Outcomes to Programme Outcomes
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10
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13
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* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
Ege University, Bornova - İzmir / TURKEY • Phone: +90 232 311 10 10 • e-mail: intrec@mail.ege.edu.tr