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R/英文代写-GUIDE 2020-2021
时间：2021-05-14
1
Faculty of Humanities
School of Social Sciences
SOCIAL STATISTICS COURSE UNIT GUIDE 2020-2021
SOST70022 Longitudinal Data Analysis
Semester: 2
Credits: 15
Convenor: Alexandru Cernat
2
1. ESSENTIAL INFORMATION
Lecturer Dr Alexandru Cernat
Room G15 Humanities Bridgeford Street Building
Email Alexandru Cernat:
alexandru.cernat@manchester.ac.uk
Sijia Du:
sijia.du@postgrad.manchester.ac.uk
Ioana Macoveciuc:
ioana.macoveciuc@manchester.ac.uk;
Office Hours
with lecturer
by appointment via email;
Times and Dates
Lectures: Lectures will be pre-recorded and available a week in
advance
Live Q&A and discussion
of lab solutions
Wednesdays 14:00-16:00
(see dates bellow, links to meetings are on Blackboard)
Drop in sessions Fridays 13:00-14:00
Mondays 14:00-15:00
(see dates bellow, links to meetings are on Blackboard)
Formative coursework
Submission:
Assignment deadline for submission –
16th April (midnight)
Assessed coursework
Submission:
Assignment deadline for submission –
28th May (midnight)
Assignments and Assessments
One 3000 word essay worth 100% of the total mark
3
2. COURSE CONTENT
Aim
To provide students with an understanding of different longitudinal designs and
the skills needed to conduct appropriate analyses using longitudinal data. Methods
covered include the multilevel model for change and models for investigating event
occurrence over time.
Teaching Methods
The course will be divided in five topics. Each topic will be covered in a week and
will have:
1. Readings. Each week you will have some mandatory reading as well as
some optional material to read. You are expected to do the weekly reading
in order to fully understand the topics covered.
2. Videos. Each week will include pre-recorded videos that will help cement
what you learned in the reading and show applications of the methods
covered.
3. Lab practical. Each week you will have to go through a lab practical on
your own. This will have a series of tasks and questions that you need to
solve/answer. You will be provided with the data and the exercises a week
in advance. You will need to run the exercises using the latest version of R
and Rstudio on your own. As such, you will need to have access to a
computer that can run these free programs. Please use the drop in
sessions as well as the solution file when you need help with the
practical.
4. Live solution and Q&A. Each week we will have a live session in which
we will go through the lab solutions together. These will take place from
14:00 to 16:00 UK time on the Wednesday of that week (see dates below). We
will also use this as a live Q&A session. These sessions will be recorded
and posted on Blackboard.
5. Drop in sessions (optional). You can use the drop in sessions to ask
questions regarding the videos, the readings, the final assignment as well as
get help with the lab practicals.
6. Datacamp (optional). You will also get free access to datacamp.com which is
a free online platform for learning programming. You can use this to
4
enhance your programming skills. You can get access to all the courses for
free using this link (you need to use Manchester email address to register):
https://www.datacamp.com/groups/shared_links/3571c556d0caca6336b88
fcc0bd8f545a92aa279ffd09d0702a639a4562a5439
Objectives
• To gain competence in the concepts, designs and terms of longitudinal research;
• To be able to apply a range of different methods for longitudinal data analysis;
• To have a general understanding of how each method represents different kinds
of longitudinal processes;
• To be able to choose a design, a plausible model and an appropriate method of
analysis for a range of research questions.
Course
The UK is fortunate in having a rich and growing store of longitudinal studies for
researchers to analyse. The course will introduce students to the methodological
and statistical skills that will enable them to address questions about the
measurement and explanation of change.
General Course Readings
The following are the key texts for this course (see Blackboard weekly pages for
detailed reading for each week):
Singer, J., & Willett, J. (2003). Applied longitudinal data analysis: modeling
change and event occurrence. Oxford University Press. (available online)
Long, J. D. (2011). Longitudinal Data Analysis for the Behavioral Sciences
Using R. Thousand Oaks, Calif: SAGE Publications, Inc.
Newsom, J. T. (2015). Longitudinal Structural Equation Modeling: A
Comprehensive Introduction. Routledge.
5
The Course (week by week)
Date release
materials
Blackboard
Drop in 1
(13:00-14:00)
Drop in 2
(14:00-15:00)
Live session
(14:00 - 16:00)
Topic
3 March 5 March 8 March 10 March Introduction to longitudinal data
10 March 12 March 15 March 17 March Cross-lagged models
17 March 19 March 22 March 24 March Multilevel model of change
Easter break
24 March 16 April** 12 April 14 April Latent Growth model
14 April 23 April** 19 April 21 April Survival analysis
16 April*
Formative assessment
28 May*
Assignment deadline
* deadlines are for the midnight of that day
** session one week later due to Easter break
6
Lectures and Reading List
Lecture 1: Introduction to longitudinal data
Topics covered:
- Introduction to the concept of longitudinal data
- Data preparation and visualization
Learning outcomes:
- Being able to prepare lo
- Longitudinal data
- Being able to investigate transitions over time
- Being able to graph descriptive statistics
Mandatory reading:
Chapters 1, 2 in Singer, J., & Willett, J. (2003). Applied longitudinal data analysis:
modeling change and event occurrence. Oxford University Press. Link to book.
Chapters 2, 3, 4 in Long, J. D. (2011). Longitudinal Data Analysis for the Behavioral
Sciences Using R. Thousand Oaks, Calif: SAGE Publications, Inc.
>
Additional reading:
Great overview on how to think of data management (in R)
(EC) Wickham, H. (2014). Tidy Data. Journal of Statistical Software, 59(10).
https://doi.org/10.18637/jss.v059.i10
Additional data camp course: Cleaning data with R
Useful commands in R for data management: http://garrettgman.github.io/tidying/
7
Lecture: 2 Cross-lagged models
Topics covered:
- Introduction to lavaan
- Autoregressive models
- Cross-lagged models
- How to select between competing models
Learning outcomes:
- Being able to estimate and interpret autoregressive and cross-lagged models
- Being able to use lavaan
Mandatory reading
Chapters 1, 4, 5 in Newsom, J. T. (2015). Longitudinal Structural Equation Modeling:
A Comprehensive Introduction. Routledge. Link to book
Additional reading
Cole, D. A., & Maxwell, S. E. (2003). Testing Mediational Models With
Longitudinal Data: Questions and Tips in the Use of Structural Equation
Modeling. Journal of Abnormal Psychology, 112(4), 558–577.
Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the
cross-lagged panel model. Psychological Methods, 20(1), 102–116.
Brunton-Smith, I. (2011). Untangling the Relationship Between Fear of Crime
and Perceptions of Disorder: Evidence from a Longitudinal Study of Young
People in England and Wales. British Journal of Criminology, 51(6), 885–899.
Yu, G., Sessions, J. G., Fu, Y., & Wall, M. (2015). A multilevel cross-lagged
structural equation analysis for reciprocal relationship between social capital and
health. Social Science & Medicine, 142, 1–8.
8
Lecture 3: Multilevel model for change
Topics covered:
- Introduction to the MLM for change
- How to estimate lme4
- How to model and interpret the results of MLM using longitudinal data
Learning outcomes:
- Being able to estimate a MLM for change
- Being able to interpret coefficients and choose between competing models
Mandatory reading
Chapters 3, 4, 5, 6 in Singer, J., & Willett, J. (2003). Applied longitudinal data analysis:
modeling change and event occurrence. Oxford University Press. Link to book.
9
Lecture 4: Latent Growth Model
Topics covered:
- The Latent Growth Model
Learning outcomes:
- Being able to estimate a Latent Growth Model
- Interpret the results of LGM
Mandatory reading
Chapters 7, 8 in Newsom, J. T. (2015). Longitudinal Structural Equation Modeling: A
Comprehensive Introduction. Routledge. Link to book
Optional reading:
Chapter 8 in Singer, J., & Willett, J. (2003). Applied longitudinal data analysis:
modeling change and event occurrence. Oxford University Press. Link to book.
Sindall, K., Sturgis, P., Steele, F., Leckie, G., & French, R. (2019). A
reassessment of socio-economic gradients in child cognitive development using
growth mixture models. Longitudinal and Life Course Studies, 10(3), 283–305.
https://doi.org/10.1332/175795919X15628474680682
10
Lecture 5: Investigating event occurrence
Topics covered:
- Understand time to event data
- Discrete time event models
- Cox models
Learning outcomes:
- Being able to estimate hazard models
- Being able to model survival/hazard functions
- Being able to model continuous time to event data
Mandatory reading
Chapters 9, 10, 11, 12, 13, 14, 15 in Singer, J., & Willett, J. (2003). Applied
longitudinal data analysis: modeling change and event occurrence. Oxford University Press.
Link to book.
11
3. ASSIGNMENTS AND ASSESSMENTS
The assessment for this course will evaluate your ability to work independently and
apply what you have learned to real life situations. As such, the final mark will be
based on an essay where you answer a substantive research question using
longitudinal data. This is expected to be in the form of a mini research paper.
You are free to choose the research topic and dataset you are interested in. I
recommend discussing these with the TAs in the drop-in sessions before you start
working on it.
In the first assignment (formative, non-assessed) you will be expected to present an
introduction and the data and methods that you will use (basically the first part of
your research paper). I expect this will have a maximum 1500 words (including
references) and will include the following aspects:
- context and why the topic is important (with appropriate references)
- research question(s) /hypotheses
- explain how you will answer the question and why longitudinal analysis is important
- present the data and the variables that you are going to use
- present descriptive statistics (in tables/graphs) with a special emphasis on the
longitudinal aspects (eg. transition matrices, plots of trends, etc.)
- present the methods you aim to use (preferably also the specific models and
sequence)
Assessed Coursework Details
Based on this work and the feedback received you are expected to develop a 3000
(including references) word research paper that will be assessed. The new paper
will also have to include the statistical analysis and the conclusions (in addition to
the improved sections submitted before).
Presentation is important! The writing style, referencing, tables/figures (and their
titles) will also be taken into account. Within each section 10% of the grade will be
based on this. Use the readings as guides for writing and presentation.
12
Grading system for final essay:
Introduction (20% of final grade)
- Include the research question(s) with context why it is important
(with appropriate references)
- Explain how you will answer the question and why longitudinal analysis is
important
Data and methods (30% of final grade)
- Present the data and the variables that you are going to use
- Present descriptive statistics
- Explain what models (and their sequence) you are going to use
Analysis (40% of final grade)
- Present the different models and interpret them
- If choosing between multiple models motivate your decision
- Investigate/discuss the assumptions of the model chosen
Conclusions (10% of final grade)
- Restate the research question(s) and show how you answered it
- Explain how longitudinal data analysis helped answer the question
- Present limitations of the study
Coursework Submission
Coursework must be typed, double-spaced in a reasonable font (eg. 12 point in
Times New Roman or Arial).
Essays should be submitted online via Turnitin by midnight on the deadline day
given on p.2 above unless given course specific instructions by email. Ensure you
have familiarised yourself with the system and give yourself plenty of time for
submission as technology problems will not be an acceptable reason for late or
non-submission of work. If you have serious problems submitting on the day
please contact the SoSS Postgraduate Office. When you have successfully
submitted your essay you will be able to download and print a receipt. You must
keep a copy of your submission receipt until all work on this course is complete
and you have received your final grades.
13
Note that our online submission system includes Turnitin plagiarism detection
software. Be sure that you fully understand what plagiarism is; links for further
details are included in section 6 below. If, after reading the guidance, you are at all
unsure about what counts as plagiarism then you should contact your Academic
Advisor to discuss it.
If your essay is submitted late your grade will be reduced by 10 marks per day for 5
days, after which it will receive a mark of zero. For clarity a ‘day’ is 24 hours,
beginning immediately after the published deadline. *Deadlines will be strictly
enforced in all cases*. The mark published through Turnitin will show your mark
*before* the late penalty is applied. The final mark, with the late penalty applied,
will be recorded on the student system and used to calculate your overall course
unit mark.
Mitigating Circumstances and extension requests
If you think that your performance or academic progress is likely to be affected by
your circumstances or that you may not be able to hand in your
assignment/dissertation by the deadline, you may submit a Mitigating
Circumstances form/extension request form, with relevant supporting
documentation, for consideration by the Mitigating Circumstances Committee and
Board of Examiners.
The nature of the supporting documentation required will vary according to the
nature of the circumstances, but it must be sufficiently independent and robust to
confirm the veracity of the case you are making. Please note that it is your
responsibility as the student to submit a request for consideration of mitigating
circumstances by the published deadlines. You should not wait until your results
are issued or the deadline for the submission of your work to have passed to apply
for mitigating circumstances as cases will not be accepted retrospectively.
14
4. FEEDBACK
All Social Statistics courses include both formative feedback – which lets you know
how you’re getting on and what you could do to improve – and summative
feedback – which gives you a mark for your assessed work. This course uses the
following mechanisms for feedback:
Informal verbal feedback will be given during lectures and tutorials for
individual and group work. (You’ll need to contribute regularly to group
discussions to make the best use of this.)
Written formative feedback will be given on your non-assessed assignment
and made available via email.
Written summative feedback will be given on your assessed coursework,
available via the Turnitin/GradeMark on the Blackboard system.
Exam results are published only as a grade. If you wish to discuss your exam
performance with your lecturer please book an office hour slot by email and
let your lecturer know in advance that this is what you want to do.
Your Feedback to Us
We’re continually working to improve our teaching practices – for that we need
your feedback. Towards the end of the semester you’ll be asked to fill out a Unit
Survey for each of your modules – please do! The survey is designed to be very
short and easy to fill out but the results are really valuable for our monitoring of
teaching quality. We want to hear from you whether your opinion on the course
was good, bad or indifferent.
All of your Unit Surveys are available via Blackboard – simply go to ‘Unit
Evaluation’ on the left hand menu of the Blackboard website to begin.
Alternatively, you can download a smartphone app called EvaluationKit to fill out
Unit Surveys for all of your course units.
15
5. YOUR COMMITMENT
Study Schedule
You are expected to:
Watch the pre-recorded videos;
Attend the live sessions;
To read all mandatory readings before each class;
You are strongly encouraged to read the additional reading;
Submit the formative assignment;
Submit the final assignment.
Attendance
You are expected to attend all live sessions that are part of your programme. It is
also expected that you arrive on time.
Email and Blackboard
Your commitment is also to check your University email and Blackboard at
least every other day in order to make sure that you are informed of any
communications from tutors or administrative staff. These might, for example,
concern important meetings with staff, changes of room; notification of course
options registration, or course-relevant information from your lecturer. Being
unaware of arrangements because you have not checked your email or Blackboard
is not an acceptable excuse.
6. REFERENCING & PLAGIARISM
The lack of a proper bibliography and appropriate reference in assessed essay will
potentially greatly affect the mark for the work and may be considered plagiarism,
which is a serious offence.
All essays must employ the scholarly apparatus of references and a bibliography.
There are different acceptable referencing styles. In Social Statistics we
recommend use of the Harvard system of referencing, which is described in detail
here: http://subjects.library.manchester.ac.uk/referencing-harvard
In short, Harvard referencing means that you refer to the author and date of
publication in brackets within the text, wherever you are referring to the ideas of
16
another writer. Where you quote an author you must always include quotation
marks and a page number in the reference.
All essays must include a References List which lists your sources in alphabetical
order by author's surname. This should include all (and only) the sources you have
directly referenced in the text. Whatever your source is, you need to provide a full
set of publication details as described in the guide linked above. All academic texts
you read will include bibliographies and these should give you plenty of examples
of what information to include.
Plagiarism
The University defines plagiarism as ‘presenting the ideas, work or words of other
people without proper, clear and unambiguous acknowledgement.’ It is an example
of academic malpractice and can lead to very serious penalties up to exclusion
from the University. You should read the University’s guidelines here:
http://documents.manchester.ac.uk/display.aspx?DocID=2870
There is additional useful guidance on plagiarism and referencing in the Crucial
Guide:
http://www.studentnet.manchester.ac.uk/crucial-guide/academic-
life/support/referencing-and-plagiarism/
学霸联盟
Faculty of Humanities
School of Social Sciences
SOCIAL STATISTICS COURSE UNIT GUIDE 2020-2021
SOST70022 Longitudinal Data Analysis
Semester: 2
Credits: 15
Convenor: Alexandru Cernat
2
1. ESSENTIAL INFORMATION
Lecturer Dr Alexandru Cernat
Room G15 Humanities Bridgeford Street Building
Email Alexandru Cernat:
alexandru.cernat@manchester.ac.uk
Sijia Du:
sijia.du@postgrad.manchester.ac.uk
Ioana Macoveciuc:
ioana.macoveciuc@manchester.ac.uk;
Office Hours
with lecturer
by appointment via email;
Times and Dates
Lectures: Lectures will be pre-recorded and available a week in
advance
Live Q&A and discussion
of lab solutions
Wednesdays 14:00-16:00
(see dates bellow, links to meetings are on Blackboard)
Drop in sessions Fridays 13:00-14:00
Mondays 14:00-15:00
(see dates bellow, links to meetings are on Blackboard)
Formative coursework
Submission:
Assignment deadline for submission –
16th April (midnight)
Assessed coursework
Submission:
Assignment deadline for submission –
28th May (midnight)
Assignments and Assessments
One 3000 word essay worth 100% of the total mark
3
2. COURSE CONTENT
Aim
To provide students with an understanding of different longitudinal designs and
the skills needed to conduct appropriate analyses using longitudinal data. Methods
covered include the multilevel model for change and models for investigating event
occurrence over time.
Teaching Methods
The course will be divided in five topics. Each topic will be covered in a week and
will have:
1. Readings. Each week you will have some mandatory reading as well as
some optional material to read. You are expected to do the weekly reading
in order to fully understand the topics covered.
2. Videos. Each week will include pre-recorded videos that will help cement
what you learned in the reading and show applications of the methods
covered.
3. Lab practical. Each week you will have to go through a lab practical on
your own. This will have a series of tasks and questions that you need to
solve/answer. You will be provided with the data and the exercises a week
in advance. You will need to run the exercises using the latest version of R
and Rstudio on your own. As such, you will need to have access to a
computer that can run these free programs. Please use the drop in
sessions as well as the solution file when you need help with the
practical.
4. Live solution and Q&A. Each week we will have a live session in which
we will go through the lab solutions together. These will take place from
14:00 to 16:00 UK time on the Wednesday of that week (see dates below). We
will also use this as a live Q&A session. These sessions will be recorded
and posted on Blackboard.
5. Drop in sessions (optional). You can use the drop in sessions to ask
questions regarding the videos, the readings, the final assignment as well as
get help with the lab practicals.
6. Datacamp (optional). You will also get free access to datacamp.com which is
a free online platform for learning programming. You can use this to
4
enhance your programming skills. You can get access to all the courses for
free using this link (you need to use Manchester email address to register):
https://www.datacamp.com/groups/shared_links/3571c556d0caca6336b88
fcc0bd8f545a92aa279ffd09d0702a639a4562a5439
Objectives
• To gain competence in the concepts, designs and terms of longitudinal research;
• To be able to apply a range of different methods for longitudinal data analysis;
• To have a general understanding of how each method represents different kinds
of longitudinal processes;
• To be able to choose a design, a plausible model and an appropriate method of
analysis for a range of research questions.
Course
The UK is fortunate in having a rich and growing store of longitudinal studies for
researchers to analyse. The course will introduce students to the methodological
and statistical skills that will enable them to address questions about the
measurement and explanation of change.
General Course Readings
The following are the key texts for this course (see Blackboard weekly pages for
detailed reading for each week):
Singer, J., & Willett, J. (2003). Applied longitudinal data analysis: modeling
change and event occurrence. Oxford University Press. (available online)
Long, J. D. (2011). Longitudinal Data Analysis for the Behavioral Sciences
Using R. Thousand Oaks, Calif: SAGE Publications, Inc.
Newsom, J. T. (2015). Longitudinal Structural Equation Modeling: A
Comprehensive Introduction. Routledge.
5
The Course (week by week)
Date release
materials
Blackboard
Drop in 1
(13:00-14:00)
Drop in 2
(14:00-15:00)
Live session
(14:00 - 16:00)
Topic
3 March 5 March 8 March 10 March Introduction to longitudinal data
10 March 12 March 15 March 17 March Cross-lagged models
17 March 19 March 22 March 24 March Multilevel model of change
Easter break
24 March 16 April** 12 April 14 April Latent Growth model
14 April 23 April** 19 April 21 April Survival analysis
16 April*
Formative assessment
28 May*
Assignment deadline
* deadlines are for the midnight of that day
** session one week later due to Easter break
6
Lectures and Reading List
Lecture 1: Introduction to longitudinal data
Topics covered:
- Introduction to the concept of longitudinal data
- Data preparation and visualization
Learning outcomes:
- Being able to prepare lo
- Longitudinal data
- Being able to investigate transitions over time
- Being able to graph descriptive statistics
Mandatory reading:
Chapters 1, 2 in Singer, J., & Willett, J. (2003). Applied longitudinal data analysis:
modeling change and event occurrence. Oxford University Press. Link to book.
Chapters 2, 3, 4 in Long, J. D. (2011). Longitudinal Data Analysis for the Behavioral
Sciences Using R. Thousand Oaks, Calif: SAGE Publications, Inc.
>
Additional reading:
Great overview on how to think of data management (in R)
(EC) Wickham, H. (2014). Tidy Data. Journal of Statistical Software, 59(10).
https://doi.org/10.18637/jss.v059.i10
Additional data camp course: Cleaning data with R
Useful commands in R for data management: http://garrettgman.github.io/tidying/
7
Lecture: 2 Cross-lagged models
Topics covered:
- Introduction to lavaan
- Autoregressive models
- Cross-lagged models
- How to select between competing models
Learning outcomes:
- Being able to estimate and interpret autoregressive and cross-lagged models
- Being able to use lavaan
Mandatory reading
Chapters 1, 4, 5 in Newsom, J. T. (2015). Longitudinal Structural Equation Modeling:
A Comprehensive Introduction. Routledge. Link to book
Additional reading
Cole, D. A., & Maxwell, S. E. (2003). Testing Mediational Models With
Longitudinal Data: Questions and Tips in the Use of Structural Equation
Modeling. Journal of Abnormal Psychology, 112(4), 558–577.
Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the
cross-lagged panel model. Psychological Methods, 20(1), 102–116.
Brunton-Smith, I. (2011). Untangling the Relationship Between Fear of Crime
and Perceptions of Disorder: Evidence from a Longitudinal Study of Young
People in England and Wales. British Journal of Criminology, 51(6), 885–899.
Yu, G., Sessions, J. G., Fu, Y., & Wall, M. (2015). A multilevel cross-lagged
structural equation analysis for reciprocal relationship between social capital and
health. Social Science & Medicine, 142, 1–8.
8
Lecture 3: Multilevel model for change
Topics covered:
- Introduction to the MLM for change
- How to estimate lme4
- How to model and interpret the results of MLM using longitudinal data
Learning outcomes:
- Being able to estimate a MLM for change
- Being able to interpret coefficients and choose between competing models
Mandatory reading
Chapters 3, 4, 5, 6 in Singer, J., & Willett, J. (2003). Applied longitudinal data analysis:
modeling change and event occurrence. Oxford University Press. Link to book.
9
Lecture 4: Latent Growth Model
Topics covered:
- The Latent Growth Model
Learning outcomes:
- Being able to estimate a Latent Growth Model
- Interpret the results of LGM
Mandatory reading
Chapters 7, 8 in Newsom, J. T. (2015). Longitudinal Structural Equation Modeling: A
Comprehensive Introduction. Routledge. Link to book
Optional reading:
Chapter 8 in Singer, J., & Willett, J. (2003). Applied longitudinal data analysis:
modeling change and event occurrence. Oxford University Press. Link to book.
Sindall, K., Sturgis, P., Steele, F., Leckie, G., & French, R. (2019). A
reassessment of socio-economic gradients in child cognitive development using
growth mixture models. Longitudinal and Life Course Studies, 10(3), 283–305.
https://doi.org/10.1332/175795919X15628474680682
10
Lecture 5: Investigating event occurrence
Topics covered:
- Understand time to event data
- Discrete time event models
- Cox models
Learning outcomes:
- Being able to estimate hazard models
- Being able to model survival/hazard functions
- Being able to model continuous time to event data
Mandatory reading
Chapters 9, 10, 11, 12, 13, 14, 15 in Singer, J., & Willett, J. (2003). Applied
longitudinal data analysis: modeling change and event occurrence. Oxford University Press.
Link to book.
11
3. ASSIGNMENTS AND ASSESSMENTS
The assessment for this course will evaluate your ability to work independently and
apply what you have learned to real life situations. As such, the final mark will be
based on an essay where you answer a substantive research question using
longitudinal data. This is expected to be in the form of a mini research paper.
You are free to choose the research topic and dataset you are interested in. I
recommend discussing these with the TAs in the drop-in sessions before you start
working on it.
In the first assignment (formative, non-assessed) you will be expected to present an
introduction and the data and methods that you will use (basically the first part of
your research paper). I expect this will have a maximum 1500 words (including
references) and will include the following aspects:
- context and why the topic is important (with appropriate references)
- research question(s) /hypotheses
- explain how you will answer the question and why longitudinal analysis is important
- present the data and the variables that you are going to use
- present descriptive statistics (in tables/graphs) with a special emphasis on the
longitudinal aspects (eg. transition matrices, plots of trends, etc.)
- present the methods you aim to use (preferably also the specific models and
sequence)
Assessed Coursework Details
Based on this work and the feedback received you are expected to develop a 3000
(including references) word research paper that will be assessed. The new paper
will also have to include the statistical analysis and the conclusions (in addition to
the improved sections submitted before).
Presentation is important! The writing style, referencing, tables/figures (and their
titles) will also be taken into account. Within each section 10% of the grade will be
based on this. Use the readings as guides for writing and presentation.
12
Grading system for final essay:
Introduction (20% of final grade)
- Include the research question(s) with context why it is important
(with appropriate references)
- Explain how you will answer the question and why longitudinal analysis is
important
Data and methods (30% of final grade)
- Present the data and the variables that you are going to use
- Present descriptive statistics
- Explain what models (and their sequence) you are going to use
Analysis (40% of final grade)
- Present the different models and interpret them
- If choosing between multiple models motivate your decision
- Investigate/discuss the assumptions of the model chosen
Conclusions (10% of final grade)
- Restate the research question(s) and show how you answered it
- Explain how longitudinal data analysis helped answer the question
- Present limitations of the study
Coursework Submission
Coursework must be typed, double-spaced in a reasonable font (eg. 12 point in
Times New Roman or Arial).
Essays should be submitted online via Turnitin by midnight on the deadline day
given on p.2 above unless given course specific instructions by email. Ensure you
have familiarised yourself with the system and give yourself plenty of time for
submission as technology problems will not be an acceptable reason for late or
non-submission of work. If you have serious problems submitting on the day
please contact the SoSS Postgraduate Office. When you have successfully
submitted your essay you will be able to download and print a receipt. You must
keep a copy of your submission receipt until all work on this course is complete
and you have received your final grades.
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Note that our online submission system includes Turnitin plagiarism detection
software. Be sure that you fully understand what plagiarism is; links for further
details are included in section 6 below. If, after reading the guidance, you are at all
unsure about what counts as plagiarism then you should contact your Academic
Advisor to discuss it.
If your essay is submitted late your grade will be reduced by 10 marks per day for 5
days, after which it will receive a mark of zero. For clarity a ‘day’ is 24 hours,
beginning immediately after the published deadline. *Deadlines will be strictly
enforced in all cases*. The mark published through Turnitin will show your mark
*before* the late penalty is applied. The final mark, with the late penalty applied,
will be recorded on the student system and used to calculate your overall course
unit mark.
Mitigating Circumstances and extension requests
If you think that your performance or academic progress is likely to be affected by
your circumstances or that you may not be able to hand in your
assignment/dissertation by the deadline, you may submit a Mitigating
Circumstances form/extension request form, with relevant supporting
documentation, for consideration by the Mitigating Circumstances Committee and
Board of Examiners.
The nature of the supporting documentation required will vary according to the
nature of the circumstances, but it must be sufficiently independent and robust to
confirm the veracity of the case you are making. Please note that it is your
responsibility as the student to submit a request for consideration of mitigating
circumstances by the published deadlines. You should not wait until your results
are issued or the deadline for the submission of your work to have passed to apply
for mitigating circumstances as cases will not be accepted retrospectively.
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4. FEEDBACK
All Social Statistics courses include both formative feedback – which lets you know
how you’re getting on and what you could do to improve – and summative
feedback – which gives you a mark for your assessed work. This course uses the
following mechanisms for feedback:
Informal verbal feedback will be given during lectures and tutorials for
individual and group work. (You’ll need to contribute regularly to group
discussions to make the best use of this.)
Written formative feedback will be given on your non-assessed assignment
and made available via email.
Written summative feedback will be given on your assessed coursework,
available via the Turnitin/GradeMark on the Blackboard system.
Exam results are published only as a grade. If you wish to discuss your exam
performance with your lecturer please book an office hour slot by email and
let your lecturer know in advance that this is what you want to do.
Your Feedback to Us
We’re continually working to improve our teaching practices – for that we need
your feedback. Towards the end of the semester you’ll be asked to fill out a Unit
Survey for each of your modules – please do! The survey is designed to be very
short and easy to fill out but the results are really valuable for our monitoring of
teaching quality. We want to hear from you whether your opinion on the course
was good, bad or indifferent.
All of your Unit Surveys are available via Blackboard – simply go to ‘Unit
Evaluation’ on the left hand menu of the Blackboard website to begin.
Alternatively, you can download a smartphone app called EvaluationKit to fill out
Unit Surveys for all of your course units.
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5. YOUR COMMITMENT
Study Schedule
You are expected to:
Watch the pre-recorded videos;
Attend the live sessions;
To read all mandatory readings before each class;
You are strongly encouraged to read the additional reading;
Submit the formative assignment;
Submit the final assignment.
Attendance
You are expected to attend all live sessions that are part of your programme. It is
also expected that you arrive on time.
Email and Blackboard
Your commitment is also to check your University email and Blackboard at
least every other day in order to make sure that you are informed of any
communications from tutors or administrative staff. These might, for example,
concern important meetings with staff, changes of room; notification of course
options registration, or course-relevant information from your lecturer. Being
unaware of arrangements because you have not checked your email or Blackboard
is not an acceptable excuse.
6. REFERENCING & PLAGIARISM
The lack of a proper bibliography and appropriate reference in assessed essay will
potentially greatly affect the mark for the work and may be considered plagiarism,
which is a serious offence.
All essays must employ the scholarly apparatus of references and a bibliography.
There are different acceptable referencing styles. In Social Statistics we
recommend use of the Harvard system of referencing, which is described in detail
here: http://subjects.library.manchester.ac.uk/referencing-harvard
In short, Harvard referencing means that you refer to the author and date of
publication in brackets within the text, wherever you are referring to the ideas of
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another writer. Where you quote an author you must always include quotation
marks and a page number in the reference.
All essays must include a References List which lists your sources in alphabetical
order by author's surname. This should include all (and only) the sources you have
directly referenced in the text. Whatever your source is, you need to provide a full
set of publication details as described in the guide linked above. All academic texts
you read will include bibliographies and these should give you plenty of examples
of what information to include.
Plagiarism
The University defines plagiarism as ‘presenting the ideas, work or words of other
people without proper, clear and unambiguous acknowledgement.’ It is an example
of academic malpractice and can lead to very serious penalties up to exclusion
from the University. You should read the University’s guidelines here:
http://documents.manchester.ac.uk/display.aspx?DocID=2870
There is additional useful guidance on plagiarism and referencing in the Crucial
Guide:
http://www.studentnet.manchester.ac.uk/crucial-guide/academic-
life/support/referencing-and-plagiarism/
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