220061 UE METH: UE STADA Statistical Data Analysis (2023S)
Continuous assessment of course work
Labels
Summary
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 20.02.2023 09:00 to We 22.02.2023 18:00
- Deregistration possible until Fr 31.03.2023 23:59
Registration information is available for each group.
Groups
Group 1
max. 30 participants
Language: German
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
Stand heute (11.01.2023) wird die Übung präsent abgehalten. Informationen zum Ablauf der Lehrveranstaltung im Sommersemester 2023 erhalten alle korrekt angemeldeten Studierenden rechtzeitig per E-Mail.
Im Falle von digitaler Lehre bzw. hybrider Lehre bleiben die Ziele und Inhalte der Lehrveranstaltung unverändert. Methodisch kommen verschiedene Übungsaktivitäten via Moodle zum Einsatz. Ergänzend können einzelne Gruppenarbeiten in einem Online-Live-Setting stattfinden.
- Thursday 30.03. 09:45 - 13:15 Seminarraum 6, Kolingasse 14-16, EG00
- Friday 31.03. 09:45 - 13:15 Seminarraum 6, Kolingasse 14-16, EG00
- Saturday 01.04. 09:45 - 13:15 Seminarraum 6, Kolingasse 14-16, EG00
Aims, contents and method of the course
Students get to know and practice descriptive and inductive methods of statistical data analysis. They learn to prepare, visualize, and analyze data with SPSS and interpret and report the results. Moreover, they learn how to read others’ results and critically examine them. After completion of the seminar, students have a basic knowledge about descriptive and inductive statistics and are able to independently carry out analyses with SPSS, as well as critically evaluate statistical representations and analyses in academic papers.It is strongly recommended to attend the corresponding lecture!
Assessment and permitted materials
Grading:
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.The course will be taught in English. Assignments will be accepted in English and German.
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.The course will be taught in English. Assignments will be accepted in English and German.
Minimum requirements and assessment criteria
75% Attendance is requiredGrading:00.0 – 49.9% Unsatisfactory
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
Examination topics
Data cleaning, visualization and analysis
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
Reading list
Literatur wird vor der Lehrveranstaltung auf Moodle bekannt gegeben.
Group 2
max. 30 participants
Language: English
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 16.03. 13:15 - 14:45 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Thursday 30.03. 13:15 - 14:45 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Thursday 27.04. 13:15 - 14:45 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Thursday 11.05. 13:15 - 14:45 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Thursday 01.06. 13:15 - 14:45 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Thursday 22.06. 13:15 - 14:45 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
Aims, contents and method of the course
Students get to know and practice descriptive and inductive methods of statistical data analysis. They learn to prepare, visualize, and analyze data with SPSS and interpret and report the results. Moreover, they learn how to read others’ results and critically examine them. After completion of the seminar, students have a basic knowledge about descriptive and inductive statistics and are able to independently carry out analyses with SPSS, as well as critically evaluate statistical representations and analyses in academic papers. It is strongly recommended to attend the corresponding lecture!After successful completion of this course, students will be able to:
• perform simple calculations and statistical analyses
• represent simple data in the appropriate graphical form
• interpret and critically evaluate statistical analyses and results
• communicate findings orally and in writing
• apply gained knowledge to conduct their own studies
• perform simple calculations and statistical analyses
• represent simple data in the appropriate graphical form
• interpret and critically evaluate statistical analyses and results
• communicate findings orally and in writing
• apply gained knowledge to conduct their own studies
Assessment and permitted materials
Please note that the course will be taught in English. Assignments will be only accepted in English.(1) Home Assignment 1 – 25%
(2) Home Assignment 2 – 35%
(3) Participation - In-class Exercises, Discussion – 40%
(2) Home Assignment 2 – 35%
(3) Participation - In-class Exercises, Discussion – 40%
Minimum requirements and assessment criteria
• Good or very good command of written and spoken English.
• Attendance is obligatory for 75% of the time. You may miss a maximum of one class (students are allowed to miss a maximum of one class).
• The seminar is planned as in-person class – this is a subject to change based on the development in the future and the university recommendations.
• Both home assignments must be submitted in order to complete the course. Home assignments must be done individually and not in a group.Grading:
• 0 - 49,9 % - Unsatisfactisfactory (5)
• 50 - 62.9 % - Sufficient (4)
• 63 - 74.9 % - Satisfactory (3)
• 75 - 86,9 % - Good (2)
• 87 - 100 % - Excellent (1)
• Attendance is obligatory for 75% of the time. You may miss a maximum of one class (students are allowed to miss a maximum of one class).
• The seminar is planned as in-person class – this is a subject to change based on the development in the future and the university recommendations.
• Both home assignments must be submitted in order to complete the course. Home assignments must be done individually and not in a group.Grading:
• 0 - 49,9 % - Unsatisfactisfactory (5)
• 50 - 62.9 % - Sufficient (4)
• 63 - 74.9 % - Satisfactory (3)
• 75 - 86,9 % - Good (2)
• 87 - 100 % - Excellent (1)
Examination topics
The SPSS interface, types of data
Descriptive statistics (central tendency, dispersion)
Data handling in SPSS
Hypothesis testing and T-tests
Chi-square test and correlation
Linear and multiple regression
Descriptive statistics (central tendency, dispersion)
Data handling in SPSS
Hypothesis testing and T-tests
Chi-square test and correlation
Linear and multiple regression
Reading list
Books:
• Field, A. (2014). Discovering statistics using IBM SPSS statistics. London: Sage.
• Field, A. (2014). Discovering statistics using IBM SPSS statistics. London: Sage.
Group 3
max. 30 participants
Language: German
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 15.03. 16:45 - 18:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Wednesday 29.03. 16:45 - 18:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Wednesday 26.04. 16:45 - 18:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Wednesday 10.05. 16:45 - 18:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Wednesday 24.05. 16:45 - 18:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Wednesday 07.06. 16:45 - 18:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Wednesday 21.06. 16:45 - 18:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
Aims, contents and method of the course
Students get to know and practice descriptive and inductive methods of statistical data analysis. They learn to prepare, visualize, and analyze data with SPSS and interpret and report the results. Moreover, they learn how to read others’ results and critically examine them. After completion of the seminar, students have a basic knowledge about descriptive and inductive statistics and are able to independently carry out analyses with SPSS, as well as critically evaluate statistical representations and analyses in academic papers.It is strongly recommended to attend the corresponding lecture!
Assessment and permitted materials
Grading:
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.The course will be taught in English. Assignments will be accepted in English and German.
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.The course will be taught in English. Assignments will be accepted in English and German.
Minimum requirements and assessment criteria
75% Attendance is requiredGrading:00.0 – 49.9% Unsatisfactory
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
Examination topics
Data cleaning, visualization and analysis
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
Reading list
Wird in der Lehrveranstaltung bekannt gegeben.
Group 4
max. 30 participants
Language: German
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 15.03. 15:00 - 16:30 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Wednesday 29.03. 15:00 - 16:30 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Wednesday 26.04. 15:00 - 16:30 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Wednesday 10.05. 15:00 - 16:30 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Wednesday 07.06. 15:00 - 16:30 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Wednesday 21.06. 15:00 - 16:30 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
Aims, contents and method of the course
Students get to know and practice descriptive and inductive methods of statistical data analysis. They learn to prepare, visualize, and analyze data with SPSS and interpret and report the results. Moreover, they learn how to read others’ results and critically examine them. After completion of the seminar, students have a basic knowledge about descriptive and inductive statistics and are able to independently carry out analyses with SPSS, as well as critically evaluate statistical representations and analyses in academic papers.It is strongly recommended to attend the corresponding lecture!
Assessment and permitted materials
Grading:
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.The course will be taught in English. Assignments will be accepted in English and German.
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.The course will be taught in English. Assignments will be accepted in English and German.
Minimum requirements and assessment criteria
75% Attendance is requiredGrading:00.0 – 49.9% Unsatisfactory
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
Examination topics
Data cleaning, visualization and analysis
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
Reading list
Wird in der Lehrveranstaltung bekannt gegeben.
Group 5
max. 30 participants
Language: English
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 15.03. 16:45 - 18:15 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Wednesday 29.03. 16:45 - 18:15 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Wednesday 26.04. 16:45 - 18:15 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Wednesday 10.05. 16:45 - 18:15 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Wednesday 07.06. 16:45 - 18:15 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Wednesday 21.06. 16:45 - 18:15 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
Aims, contents and method of the course
The course aims to explain the basics of statistical analyses in communication science. Through practical exercises, students will learn descriptive statistics in SPSS, basic data analyses (e.g., correlation, t-test, linear regression) and how to interpret the results.After completing this seminar, students will be familiar with descriptive statistics and basic data analyses to independently carry out analyses in SPSS.Please note that this seminar is taught in English.
Assessment and permitted materials
Participation in classes and two homework assignments.The grade comprises of:
40% Participation in seminar
60% Two homework assignments (25% for the first home assignment, 35% for the second homework assignment)
To receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.
40% Participation in seminar
60% Two homework assignments (25% for the first home assignment, 35% for the second homework assignment)
To receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.
Minimum requirements and assessment criteria
75% Attendance is required to successfully pass the course.Grading
87.0 – 100% Excellent
75.0 – 86.9% Good
63.0 – 74.9% Satisfactory
50.0 – 62.9% Sufficient
00.0 – 49.9% Unsatisfactory
87.0 – 100% Excellent
75.0 – 86.9% Good
63.0 – 74.9% Satisfactory
50.0 – 62.9% Sufficient
00.0 – 49.9% Unsatisfactory
Reading list
Will be announced in the course.
Group 6
max. 30 participants
Language: German
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 15.03. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Wednesday 29.03. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Wednesday 26.04. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Wednesday 10.05. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Wednesday 07.06. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Wednesday 21.06. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
Aims, contents and method of the course
Die Lehrveranstaltung setzt sich zum Ziel, Grundlagen der statistischen Auswertungen in der Kommunikationswissenschaft zu erklären und mit praktischen Übungen näherzubringen.
Es wird dringend empfohlen, die dazugehörige Vorlesung zu besuchen.
Es wird dringend empfohlen, die dazugehörige Vorlesung zu besuchen.
Assessment and permitted materials
Mitarbeit (Einzel- oder Gruppenübungen zu jedem Thema in der jeweiligen Einheit, Vorbereitung der Pflichtlektüre) und fristgerechte Abgabe von zwei Hausübungen.Im Falle von digitaler bzw. hybrider Lehre wird die Mitarbeit ebenfalls durch die Abgabe von Mitarbeitsübungen bewertet. Die Arbeitsaufträge werden in diesem Fall jeweils in den entsprechenden Einheiten auf Moodle gestellt.
Minimum requirements and assessment criteria
75 % Anwesenheitspflicht, d.h. es ist nur eine Fehleinheit möglich. Im Falle von digitaler bzw. hybrider Lehre wird die Anwesenheitspflicht der Situation entsprechend angepasst und rechtzeitig an die Studierenden kommuniziert.Benotung: 60 % Hausübungen + 40 % Mitarbeit2 Hausübungen:
Hausübung 1: 25 %
Hausübung 2: 35 %Für eine positive Note müssen beide Hausübungen abgegeben werden und im Durchschnitt 50 % der Gesamtpunkte erreicht werden (d.h. über beide Hausübungen hinweg 30 Punkte). Zudem muss die Mitarbeit als Teilleistung ebenfalls positiv sein, d.h. es müssen mindestens 20 Mitarbeitspunkte über das Semester hinweg gesammelt werden.Im Falle von digitaler bzw. hybrider Lehre bleiben die Anforderungen und der Beurteilungsmaßstab zu den Hausübungen und Mitarbeitsübungen unverändert.Notenschlüssel:
100 - 87,0 % Sehr Gut
86,9 - 75,0 % Gut
74,9 - 63,0 % Befriedigend
62,9 - 50,0 % Genügend
49,9 - 00,0 % Nicht Genügend
Hausübung 1: 25 %
Hausübung 2: 35 %Für eine positive Note müssen beide Hausübungen abgegeben werden und im Durchschnitt 50 % der Gesamtpunkte erreicht werden (d.h. über beide Hausübungen hinweg 30 Punkte). Zudem muss die Mitarbeit als Teilleistung ebenfalls positiv sein, d.h. es müssen mindestens 20 Mitarbeitspunkte über das Semester hinweg gesammelt werden.Im Falle von digitaler bzw. hybrider Lehre bleiben die Anforderungen und der Beurteilungsmaßstab zu den Hausübungen und Mitarbeitsübungen unverändert.Notenschlüssel:
100 - 87,0 % Sehr Gut
86,9 - 75,0 % Gut
74,9 - 63,0 % Befriedigend
62,9 - 50,0 % Genügend
49,9 - 00,0 % Nicht Genügend
Reading list
Wird in der Lehrveranstaltung bekannt gegeben.
Group 7
max. 30 participants
Language: English
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
We are planning to hold courses on-site to enable personal exchange between students and the teacher. However, due to COVID-19, we might have to switch to a digital format at short notice. Please regularly obtain information on u:find and check your e-mails.
- Tuesday 14.03. 15:00 - 16:30 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Tuesday 28.03. 15:00 - 16:30 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Tuesday 25.04. 15:00 - 16:30 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Tuesday 09.05. 15:00 - 16:30 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Tuesday 23.05. 15:00 - 16:30 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Tuesday 13.06. 15:00 - 16:30 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Tuesday 27.06. 15:00 - 16:30 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
Aims, contents and method of the course
After successful completion of this course, students will be able to:
• perform simple calculations and statistical analyses
• represent simple data in the appropriate graphical form
• interpret and critically evaluate statistical analyses and results
• communicate findings orally and in writing
• apply gained knowledge to conduct their own studies
• perform simple calculations and statistical analyses
• represent simple data in the appropriate graphical form
• interpret and critically evaluate statistical analyses and results
• communicate findings orally and in writing
• apply gained knowledge to conduct their own studies
Assessment and permitted materials
(1) Home Assignment 1 – 25%
(2) Home Assignment 2 – 35%
(3) In-class Exercises – 20%
(4) In-class Discussion – 20%
(2) Home Assignment 2 – 35%
(3) In-class Exercises – 20%
(4) In-class Discussion – 20%
Minimum requirements and assessment criteria
• good or very good command of written and spoken English
• basic math skills and not being afraid of maths and statistics
• basic computer skills and (preferably) the ability to install software
• ability to meet a set-in-stone deadline
• obligatory attendance (students are allowed to miss a maximum of one class)
• both home assignments must be submitted in order to complete the courseA = 1 (Very Good): 87 - 100%
B = 2 (Good): 75 - 86,99%
C = 3 (Satisfactory): 63 - 74,99%
D = 4 (Enough): 50 - 62,99%
F = 5 (Not Enough): 00 - 49,99%
• basic math skills and not being afraid of maths and statistics
• basic computer skills and (preferably) the ability to install software
• ability to meet a set-in-stone deadline
• obligatory attendance (students are allowed to miss a maximum of one class)
• both home assignments must be submitted in order to complete the courseA = 1 (Very Good): 87 - 100%
B = 2 (Good): 75 - 86,99%
C = 3 (Satisfactory): 63 - 74,99%
D = 4 (Enough): 50 - 62,99%
F = 5 (Not Enough): 00 - 49,99%
Examination topics
Data cleaning, visualization, and analysis
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Squared Test
- Correlation
- Simple and multiple linear regression
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Squared Test
- Correlation
- Simple and multiple linear regression
Reading list
Field, A. (2017). Discovering statistics using IBM SPSS statistics (5th edition). Thousand Oaks, CA: SAGE Publications.
Software
• IBM SPSS Statistics (26 or 27)
Software
• IBM SPSS Statistics (26 or 27)
Group 8
max. 30 participants
Language: English
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
ATTENTION: In this course, we will work with the statistical program SPSS for which a license will be provided to you. Since we are not in a computer room, students need to bring their own laptops to class.
Participation in the course is recommended for those who have already attended the corresponding lecture. While this is not mandatory, familiarity with the basic concepts is required. For those who still need to attend the lecture, study material for independent studies before class will be provided in time.- Thursday 30.03. 13:30 - 17:00 Seminarraum 6, Kolingasse 14-16, EG00
- Friday 31.03. 13:30 - 17:00 Seminarraum 6, Kolingasse 14-16, EG00
- Saturday 01.04. 13:30 - 17:00 Seminarraum 6, Kolingasse 14-16, EG00
Aims, contents and method of the course
Students get to know and practice descriptive and inductive methods of statistical data analysis. They learn to prepare, visualize, and analyze data with SPSS and interpret and report the results. Moreover, they learn how to read others’ results and critically examine them. After completion of the seminar, students have a basic knowledge about descriptive and inductive statistics and are able to independently carry out analyses with SPSS, as well as critically evaluate statistical representations and analyses in academic papers.It is strongly recommended to attend the corresponding lecture!
Assessment and permitted materials
Grading:
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.The course will be taught in English. Assignments will be accepted in English and German.
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.The course will be taught in English. Assignments will be accepted in English and German.
Examination topics
Data cleaning, visualization and analysis
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
Reading list
b. a.
Group 9
max. 30 participants
Language: German
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
- Monday 20.03. 09:45 - 11:15 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Monday 17.04. 09:45 - 11:15 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Monday 08.05. 09:45 - 11:15 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Monday 22.05. 09:45 - 11:15 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Monday 12.06. 09:45 - 11:15 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Monday 26.06. 09:45 - 11:15 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
Aims, contents and method of the course
Students get to know and practice descriptive and inductive methods of statistical data analysis. They learn to prepare, visualize, and analyze data with SPSS and interpret and report the results. Moreover, they learn how to read others’ results and critically examine them. After completion of the seminar, students have a basic knowledge about descriptive and inductive statistics and are able to independently carry out analyses with SPSS, as well as critically evaluate statistical representations and analyses in academic papers.It is strongly recommended to attend the corresponding lecture!
Assessment and permitted materials
Grading:
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved. In addition, the cooperation as a partial performance must also be positive, i.e. at least 20 cooperation points must be accumulated over the semester. In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.
The course will be taught in German. Assignments will be accepted in English and German.
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved. In addition, the cooperation as a partial performance must also be positive, i.e. at least 20 cooperation points must be accumulated over the semester. In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.
The course will be taught in German. Assignments will be accepted in English and German.
Minimum requirements and assessment criteria
75% Attendance is required (that means a maximum of one lesson can be missed). The attendance requirement applies to on-site teaching as well as to digital or hybrid teaching. In the case of digital or hybrid teaching, compulsory attendance is checked either via attendance exercises or via participation in synchronous online units.Grading:00.0 – 49.9% Unsatisfactory
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
Examination topics
Data cleaning, visualization and analysis
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
Reading list
- Field, A. (2014). Discovering statistics using IBM SPSS statistics. London: Sage.
Group 10
max. 30 participants
Language: German
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 29.03. 13:00 - 16:00 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Wednesday 17.05. 13:00 - 16:00 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Wednesday 28.06. 13:00 - 16:00 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
Aims, contents and method of the course
Students get to know and practice descriptive and inductive methods of statistical data analysis. They learn to prepare, visualize, and analyze data with SPSS and interpret and report the results. Moreover, they learn how to read others’ results and critically examine them. After completion of the seminar, students have a basic knowledge about descriptive and inductive statistics and are able to independently carry out analyses with SPSS, as well as critically evaluate statistical representations and analyses in academic papers.It is strongly recommended to attend the corresponding lecture!
Assessment and permitted materials
Grading:
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.The course will be taught in English. Assignments will be accepted in English and German.
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.The course will be taught in English. Assignments will be accepted in English and German.
Minimum requirements and assessment criteria
75% Attendance is requiredGrading:00.0 – 49.9% Unsatisfactory
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
Examination topics
Data cleaning, visualization and analysis
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
Reading list
Wird in der Lehrveranstaltung bekannt gegeben.
Group 11
max. 30 participants
Language: German
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
- Tuesday 14.03. 09:45 - 11:15 PC-Raum 1 Schenkenstraße 8-10, 1.UG
- Tuesday 28.03. 09:45 - 11:15 PC-Raum 1 Schenkenstraße 8-10, 1.UG
- Tuesday 25.04. 09:45 - 11:15 PC-Raum 1 Schenkenstraße 8-10, 1.UG
- Tuesday 09.05. 09:45 - 11:15 PC-Raum 1 Schenkenstraße 8-10, 1.UG
- Tuesday 23.05. 09:45 - 11:15 PC-Raum 1 Schenkenstraße 8-10, 1.UG
- Tuesday 13.06. 09:45 - 11:15 PC-Raum 1 Schenkenstraße 8-10, 1.UG
- Tuesday 27.06. 09:45 - 11:15 PC-Raum 1 Schenkenstraße 8-10, 1.UG
Aims, contents and method of the course
Die Studierenden lernen deskriptive und induktive Methoden der statistischen Datenanalyse kennen und anzuwenden. Sie lernen, Daten mit SPSS aufzubereiten, zu visualisieren und zu analysieren sowie die Ergebnisse zu interpretieren und zu berichten. Außerdem lernen sie, die Ergebnisse anderer zu lesen und kritisch zu hinterfragen. Nach Abschluss des Seminars verfügen die Studierenden über Grundkenntnisse in der deskriptiven und induktiven Statistik und sind in der Lage, selbstständig Analysen mit SPSS durchzuführen sowie statistische Darstellungen und Analysen in wissenschaftlichen Arbeiten kritisch zu bewerten. Der Besuch der dazugehörigen Vorlesung wird dringend empfohlen!Informationen zum konkreten Ablauf der Lehrveranstaltung im 2023S erhalten alle korrekt angemeldeten Studierenden rechtzeitig per E-Mail. Derzeit ist die Lehrveranstaltung als Vor-Ort-Seminar geplant. Im Falle von digitaler bzw. hybrider Lehre bleiben die Ziele und Inhalte der Lehrveranstaltung unverändert. Methodisch kommen verschiedene Übungsaktivitäten via Moodle zum Einsatz.Die Unterrichtssprache ist Deutsch.
Assessment and permitted materials
Mitarbeit (Einzel- und Gruppenaufgaben in der Übungseinheit), fristgerechte Abgabe von 2 Hausübungen, Erreichen der Mindestpunkte in den eigenständig verfassten Hausübungen.Im Falle von digitaler bzw. hybrider Lehre wird die Mitarbeit ebenfalls durch die Abgabe von Mitarbeitsübungen bewertet. Die Arbeitsaufträge werden in diesem Fall jeweils in den entsprechenden Einheiten auf Moodle gestellt.Zur Sicherung der guten wissenschaftlichen Praxis kann die Lehrveranstaltungsleitung Studierende zu einem notenrelevanten Gespräch nach Abgabe der Hausübungen einladen, welches positiv zu absolvieren ist.
Minimum requirements and assessment criteria
75 % Anwesenheitspflicht bei Vor-Ort-Lehre (nur 1 Fehleinheit möglich). Im Falle von digitaler bzw. hybrider Lehre wird die Anwesenheitspflicht der Situation entsprechend angepasst und rechtzeitig an die Studierenden kommuniziert.Benotung: 60% Hausübungen, 40% Mitarbeit in UE2 Hausübungen:
HÜ1: 25 %
HÜ2: 35%
Für eine positive Note müssen beide Hausübungen abgegeben werden und im Durchschnitt 50% der Gesamtpunkte erreicht werden (d.h. über beide Hausübungen hinweg 30 Punkte). Zudem muss die Mitarbeit als Teilleistung ebenfalls positiv sein, d.h. es müssen mindestens 20 Mitarbeitspunkte über das Semester hinweg gesammelt werden. Die Hausübungen sind auf Deutsch zu verfassen.Im Falle von digitaler bzw. hybrider Lehre bleiben die Anforderungen und der Beurteilungsmaßstab zu den Hausübungen und Mitarbeitsübungen unverändert.Notenschlüssel:
00,0 - 49,9% Nicht Genügend
50,0 - 62,9% Genügend
63,0 - 74,9% Befriedigend
75,0 - 86,9% Gut
87,0 - 100% Sehr Gut
HÜ1: 25 %
HÜ2: 35%
Für eine positive Note müssen beide Hausübungen abgegeben werden und im Durchschnitt 50% der Gesamtpunkte erreicht werden (d.h. über beide Hausübungen hinweg 30 Punkte). Zudem muss die Mitarbeit als Teilleistung ebenfalls positiv sein, d.h. es müssen mindestens 20 Mitarbeitspunkte über das Semester hinweg gesammelt werden. Die Hausübungen sind auf Deutsch zu verfassen.Im Falle von digitaler bzw. hybrider Lehre bleiben die Anforderungen und der Beurteilungsmaßstab zu den Hausübungen und Mitarbeitsübungen unverändert.Notenschlüssel:
00,0 - 49,9% Nicht Genügend
50,0 - 62,9% Genügend
63,0 - 74,9% Befriedigend
75,0 - 86,9% Gut
87,0 - 100% Sehr Gut
Examination topics
- Datenbereinigung, -visualisierung und -analyse
- Interpretation von eigenen und fremden Ergebnissen
- Deskriptive Statistik
- Umgang mit Daten
- Mittelwertvergleich (t-Test)
- Chi-Quadrat-Test
- Korrelation
- Einfache und mehrfache lineare Regression
- Interpretation von eigenen und fremden Ergebnissen
- Deskriptive Statistik
- Umgang mit Daten
- Mittelwertvergleich (t-Test)
- Chi-Quadrat-Test
- Korrelation
- Einfache und mehrfache lineare Regression
Reading list
Wird in der Lehrveranstaltung bekannt gegeben.
Group 12
max. 30 participants
Language: German
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
Die LV ist aktuell (Stand 19.01.2023) als Präsenz-LV geplant. Sollte eine Implementierung von hybrider/digitaler Lehre aufgrund triftiger Gründe nötig sein, werden sie rechtzeitig darüber informiert.
- Thursday 16.03. 13:15 - 14:45 PC-Raum 1 Schenkenstraße 8-10, 1.UG
- Thursday 30.03. 13:15 - 14:45 PC-Raum 1 Schenkenstraße 8-10, 1.UG
- Thursday 27.04. 13:15 - 14:45 PC-Raum 1 Schenkenstraße 8-10, 1.UG
- Thursday 11.05. 13:15 - 14:45 PC-Raum 1 Schenkenstraße 8-10, 1.UG
- Thursday 01.06. 13:15 - 14:45 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
- Thursday 22.06. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
Aims, contents and method of the course
Die Lehrveranstaltung setzt sich zum Ziel, Grundlagen der statistischen Auswertungen in der Kommunikationswissenschaft zu erklären und mit praktischen Übungen näherzubringen.Es wird dringend empfohlen, die dazugehörige Vorlesung zu besuchen.Informationen zum konkreten Ablauf der Lehrveranstaltung im Sommersemester 2023 erhalten alle korrekt angemeldeten Studierenden rechtzeitig per E-Mail. Im Falle von digitaler Lehre bzw. hybrider Lehre bleiben die Ziele und Inhalte der Lehrveranstaltung unverändert.
Minimum requirements and assessment criteria
75% Anwesenheitspflicht, d.h. es ist eine Fehleinheit möglich. Die Anwesenheitspflicht gilt bei Vor-Ort-Lehre wie auch bei digitaler bzw. hybrider Lehre.Benotung: 60 % Hausübungen + 40 % Mitarbeit
2 Hausübungen:
Hausübung 1: 25 %
Hausübung 2: 35 %Für eine positive Note müssen beide Hausübungen abgegeben werden und im Durchschnitt 50% der Gesamtpunkte erreicht werden (d.h. über beide Hausübungen hinweg 30 Punkte). Zudem muss die Mitarbeit als Teilleistung ebenfalls positiv sein, d.h. es müssen mindestens 20 Mitarbeitspunkte über das Semester hinweg gesammelt werden.Im Falle von digitaler Lehre bzw. hybrider Lehre bleiben die Anforderungen und der Beurteilungsmaßstab zu den Hausübungen und zur Mitarbeit unverändert.Notenschlüssel:
100 - 87,0 % Sehr Gut
86,9 - 75,0 % Gut
74,9 - 63,0 % Befriedigend
62,9 - 50,0 % Genügend
49,9 - 00,0 % Nicht Genügend
2 Hausübungen:
Hausübung 1: 25 %
Hausübung 2: 35 %Für eine positive Note müssen beide Hausübungen abgegeben werden und im Durchschnitt 50% der Gesamtpunkte erreicht werden (d.h. über beide Hausübungen hinweg 30 Punkte). Zudem muss die Mitarbeit als Teilleistung ebenfalls positiv sein, d.h. es müssen mindestens 20 Mitarbeitspunkte über das Semester hinweg gesammelt werden.Im Falle von digitaler Lehre bzw. hybrider Lehre bleiben die Anforderungen und der Beurteilungsmaßstab zu den Hausübungen und zur Mitarbeit unverändert.Notenschlüssel:
100 - 87,0 % Sehr Gut
86,9 - 75,0 % Gut
74,9 - 63,0 % Befriedigend
62,9 - 50,0 % Genügend
49,9 - 00,0 % Nicht Genügend
Reading list
Literatur wird in der LV bekannt gegeben.
Group 13
max. 30 participants
Language: English
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
- Tuesday 14.03. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Tuesday 28.03. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Tuesday 25.04. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Tuesday 09.05. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Tuesday 23.05. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Tuesday 13.06. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Tuesday 27.06. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
Aims, contents and method of the course
Students get to know and practice descriptive and inductive methods of statistical data analysis. They learn to prepare, visualize, and analyze data with SPSS, Interpretation and reporting of the results. Moreover, they learn how to read and interpret results and critically examine them. After completion of the seminar, students will have a basic knowledge about descriptive and inductive statistics and will be able to independently carry out analyses with SPSS, as well as critically evaluate statistical representations and analyses in academic papers. It is strongly recommended to attend the corresponding lecture.
Assessment and permitted materials
Grading:
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.The course will be taught in English. Assignments will be accepted in English and German.
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.The course will be taught in English. Assignments will be accepted in English and German.
Minimum requirements and assessment criteria
75% Attendance is required (one class can be missed)Grading:00.0 – 49.9% Unsatisfactory
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
Examination topics
- Data cleaning, visualization and analysis
- Interpretation of results
- Descriptive statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square test
- Correlations
- Linear regression
- Interpretation of results
- Descriptive statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square test
- Correlations
- Linear regression
Reading list
b. a.
Group 14
max. 30 participants
Language: German
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
Ablauf
Tag 1: Einführung, Datenmanagement und Visualisierungen
Tag 2: Deskriptive Statistik und Theorie der Hypothesentestung. Statstische Tests: t-test und chi-Quadrat
Tag 3: Statstische Tests: Korrelation und Regressionsanalyse
Hausübung 1: 12. Juli 2023
Hausübung 2: 19. Juli 2023
- Friday 30.06. 11:30 - 14:30 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Saturday 01.07. 11:30 - 14:30 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
- Sunday 02.07. 11:30 - 14:30 Lehrredaktion Publizistik, Währinger Straße 29 2.OG
Aims, contents and method of the course
Students get to know and practice descriptive and inductive methods of statistical data analysis. They learn to prepare, visualize, and analyze data with SPSS and interpret and report the results. Moreover, they learn how to read others’ results and critically examine them. After completion of the seminar, students have a basic knowledge about descriptive and inductive statistics and are able to independently carry out analyses with SPSS, as well as critically evaluate statistical representations and analyses in academic papers. It is strongly recommended to attend the corresponding lecture!
Assessment and permitted materials
Grading:
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.
60% homework (25% for the first homework, 35% for the second homework)
40% participation in classesTo receive a positive grade, both homework assignments must be submitted and an average of 50% of the total points must be achieved.In case the semester will take place in the form of remote learning, content and aims of the course remain unchanged.
Minimum requirements and assessment criteria
75% Attendance is requiredGrading:00.0 – 49.9% Unsatisfactory
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
50.0 – 62.9% Sufficient
63.0 – 74.9% Satisfactory
75.0 – 86.9% Good
87.0 - 100% Excellent
Examination topics
Data cleaning, visualization and analysis
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
- Interpretation of own and others’ results
- Descriptive Statistics
- Data handling
- Mean comparison (t-test)
- Chi-Square Test
- Correlation
- Simple and multiple linear regression
Reading list
nur die Slides der Einheiten
Association in the course directory
Last modified: Th 14.11.2024 00:15