Course Outline and Assessment 2013-2014
SEMAL Dr Andrew Clegg Dr Dawn Robins
Data Analysis for Research
BML224: Data Analysis for Research
Data Analysis for Research
Data Analysis for Research Introduction
The acquisition, manipulation, interpretation and presentation of data are important skills for graduates. The aim of this module is to introduce students to the use of computer-based statistical techniques for the analysis and presentation of quantitative data sources. The module provides an appropriate link to Business Research, where more qualitative research methodologies will be discussed. The module is designed to reflect the lack of confidence and anxiety felt by students when dealing with statistical techniques, often for the first time. The module will take the students on a structured and applied journey, starting at an introductory level looking at the rationale and contextualisation for the use of quantitative research methodologies. From here consideration will be given to the generation and use of descriptive statistics, through to the application of more advanced statistical techniques.
Knowledge and Understanding: On successful completion of this module students will be able to: Relate and critically apply the use of quantitative methodologies to their own research Distinguish between the characteristics of different data types and apply to quantitative methodologies and data collection strategies Acquire, analyse, interpret and present quantitative data appropriately using SPSS and Excel Accurately select and apply appropriate advanced statistical techniques in SPSS and analyse the output accordingly Relate underlying statistical theory, such as the normal distribution, to statistical analysis
Data Analysis for Research
Research Design and Data Collection 1
No Sessions - Self-Directed Tasks
Research Design and Data Collection 2
Research Design and Data Collection 3
Exploratory Data Analysis 1 - Basic Descriptive Statistics
Week 6: 14th/15th/16th Oct
Exploratory Data Analysis 2 - Presenting Data
Week 7: 21st/22nd/23rd Oct
Understanding Your Data: Normal Distribution and Patterns of Dispersion
Understanding Your Data - Looking for Difference: Student T-Test and Paired Samples T-Test
Understanding Your Data - Looking for Difference: Mann Whitney and Wilcoxon
Understanding Your Data - Looking for Difference: Chi-Squared
Understanding Your Data - Looking for Association: Correlation
Dates: Week 1: 9th/10th/11th Sept Week 2: 16th/17th/18th Sept Week 3: 23th/24th/25th Sept Week 4: 30th Sept/1st/2nd Oct Week 5: 7th/8th/9th Oct
Week 8: 28th/29st Oct/30th Oct Week 9: 4th/5th/6th Nov Week 10: 11th/12th/13th Nov Week 11: 18th/19th/20th Nov Week 12: 25th/26th/27th Nov Week 13: 2nd/3rd/4th Dec
Sessions for BML224 will be on a Monday, Tuesday and Wednesday. Specific learning outcomes for each session are provided in your statistics manual and are also detailed on the BML224 homepage on Moodle. Sessions will involve the use of a series of data files that can be downloaded from the BML224 homepage on Moodle. Please download these files into your own file space ready for use in the session.
Additional resources are also available and are detailed
throughout this manual. Activities and resources are signposted by a number of icons including: This icon refers to class-based and self-directed activities. Details relating to activities are provided in your manual.
Data Analysis for Research This icon refers to your Word log book. The activities in this manual are mirrored in the log book and you will be expected to keep your log book up-to-date and record the activities that you have completed. You will also be expected to submit sections of the log book as you progress through the module, so that the module tutor can monitor your completion and understanding of different activities. Failure to complete the log book and meet specific submission dates will result in you having to attend additional workshop sessions. This icon relates to online simulations that have been developed to provide additional guidance on the use of SPSS. Each of the statistical tests covered in the manual has an accompanying simulation that you can access online. The weblink to these online simulations is available on the BML224 homepage on Moodle.
As part of the course, you will be asked to complete short tasks as part of the lecture session.
Specific tasks will be allocated on a
weekly basis. It is essential that these tasks are completed, in order to demonstrate your competency in the statistical methods that are being employed during the module. Please ensure that you read through the handouts provided thoroughly.
The module will be using SPSS. You can get a copy of SPSS to install on your own PC from the library (free of charge!). This software will be licensed to you as long as you are at student at the University of Chichester.
You are free to install SPSS on your own laptop and
bring that to the weekly sessions.
Data Analysis for Research
The assessment for this module will consist of (i) a short quantitative research project and research briefing (60%, 2,100 word eqivalent) and (ii) a practical assessment (40%, 1,400 word equivalent). Research Project and Research Briefing Working in small groups, students will be asked to execute a short questionnaire survey and present a research briefing to their peers, as part of a quantitative methods research conference. This will consist of a short press briefing (approx. 6 minutes per group) and a supporting poster presentation/press pack that outlines the key results from the survey. Students will be expected to develop a suitable research topic, and design an on-line questionnaire. The research should be designed to demonstrate progression from basic to advanced statistical techniques, covering the key techniques covered in the module. As part of the research conference, students will also have the opportunity to assess each respective presentation/poster, as part of a wider conference exhibit. The assessment criteria for research briefing: Evidence of clear research aims and objectives linking to applied quantitative methodologies Clear and logical structure of analysis demonstrating progression from basic to advanced statistical techniques Clear extrapolation of answers and analysis based on the use of the appropriate statistical techniques and the interpretation/ application of SPSS output Ability to convey relevant results accurately and succinctly using appropriate formats and conventions Standard of presentation of all included elements (e.g. PowerPoint/ Prezi/poster) Practical Assessment Students will complete an individual 90 minute in-class practical relating to areas covered during the course of the module. The assessment criteria for practical assessment are: Demonstration of underlying statistical theory in relation to the use of descriptive and advanced statistical techniques Clear and logical provision of analysis demonstrating progression from basic to advanced statistical techniques Extrapolation of answers and analysis based on the use of the appropriate statistical techniques and the interpretation of SPSS output
Data Analysis for Research The resit for this module will consist of a 3 hour practical examination. A pass on the module is based on your overall grade profile therefore if you were to unexpectedably fail a specific element of the assessment if your overall grade profile was above 40% (including the fail) you would pass the module. Remember a non-submission in any part of the assessment would also result in the failure of the whole module. Additional details relating to the assessment for this module can be found in the accompanying documentation.
Key dates for your diary:
- The practical assessment will be scheduled on Wednesday 18th December.
- The research conference will take place on Tuesday 10th,
Wednesday 11th and Thursday 12th December.
Regardless of the date and time of your presentation, all posters and completed presentations should be submitted by 1pm on Tuesday 10th December.
Dawn and I can be found on the top of floor of the Dome on the Bognor Regis campus. If you have any problems please do not hesitate to come see us. While I am usually around, consultancy work does take me off campus from time to time. Therefore while you are welcome to pop in informally, please email Dawn or myself to make an appointment (email@example.comfirstname.lastname@example.org) to guarantee that we are in to see you. You can also contact me via Skype or Twitter - details are available on the Moodle homepage. You will be introduced to a number of new concepts and techniques in this module. Statistics is not ‘everybody’s cup of tea’ and I am very conscious about what is called ‘statistics anxiety’. If at any time you are unclear about what we are doing, please do not hesistate to come and see us. The support materials for this module have been designed to make the everything as self-explanatory as possible. Please make time to read through the materials provided, and use the online simulations to enhance your familiarity with the different statistical techniques we will be using. The materials have been expanded and developed as a result of feedback from student evaluations. It is imperative that you read through all the materials provided and take responsibility for your own learning. Fail to do so could result in you failing this module.
Data Analysis for Research
At the end of the module, you will have the opportunity to complete a module evaluation form to comment on the overall structure, content and quality of the programme. The module evaluation for 2012 can be found on the BML224 homepage. If you have any immediate concerns about the quality of the module then please do not hesitate to come and talk to me directly. You can also make comments throughout the course of the module by using the comment and suggestion wall that has been embedded into the BML224 homepage.
The University’s Commitment Charter (Section C) sets out the codes of behaviour that staff and students can expect from one another. Every member of the University community is expected to uphold the Charter commitments and to help to maintain a respectful and constructive learning environment for themselves and for others. In contact (class) time, and outside of it, the University expects you to show consideration towards other students and the staff of the University. In lectures, seminars and workshops it is your responsibility to avoid behaviour which distracts the learning process for yourself and others. Behaviours which may seem insignificant to you, such as whispering to friends, or texting during a seminar, are almost always noticed! They can have an accumulative, negative impact on the group and the tutor. Such behaviours signal lack of respect for others - even if this was not your intention. To help illustrate these points, here are some behaviours that students and tutors have found distracting: •
Talking or whispering in lectures, outside times set aside for group discussion
Talking or whispering while other students are making points
Interrupting other students or the tutor while they are talking
Habitually arriving late or leaving early (without forewarning the tutor)
Sending and receiving texts
Mobile phones ringing
Using MP3 players
Playing electronic games
Surfing the net in class
Data Analysis for Research Students whose behaviour disrupts a class persistently may be asked to leave the session. However we are sure that as adult learners youâ€™ll use common sense and be willing to help create the best possible learning environment for everyone. Students often find statistical analysis rather difficult. Therefore considerable time and effort has gone into the design of learning and teaching materials. The sessions will be tutor-led to start, therefore students are asked to pay close attention to the instructions that are given. Please note:  I will not expect to see students using software other than that
being used in the session - no emailing, checking Facebook
or equivalent. Students infringing this request will be asked to leave the session.
 Please be punctual as there is quite alot of ground to cover in each of the weekly sessions. Evidence indicates that students who have a poor attendance record fail this module.
 Mobile phones should be switched off before the session.  This is a difficult module and you will need to concentrate. I will need to try and help everybody through the session. This is not helped by constant chatting, as such any students
persistently talking or causing a distraction will be asked to leave the session.
 It is critical that any self-directed activities or quizzes are completed satisafactorily. Failure to complete weekly tasks
will result in you being excluded from sessions until these activities are completed. Please note that I can monitor
completion via Moodle, and I will contact students that are not
engaging with resources. Failure to engage with the module resources will ultimately result in you failing the module.
Students are reminded that attendance at all modules is compulsory. If you miss a session, for whatever reason, you should complete and submit a student absence form to the SEMAL admin office. It is also courteous to let the module tutor know of any absence in advance or immediately after the session that was missed. This should be completed as soon as possible from the date of absence. You are reminded that persistent absence can potentially result in your deregistration from the module. The full University regulations p. viii
Data Analysis for Research regarding attendance can be found in your student handbook. You are also asked to arrive punctually for your lectures. Students that are persistently late will be marked as absent. If you do miss a session for BML224 is it imperative that you read through the lecture notes and complete all set tasks. If you fail to do this you will find yourself falling behind, and unable to follow activities undertaken during the sessions. Dawn and I will not be repeating material in the session for those that have lost their way through non-attendance. For reference, students that tend to fail this module do so because of a lack of attendance and engagement. You are
also advised that student engagement with tasks is monitored closely via Moodle, and any student persisently shown to be not engaging will be asked to attend a meeting with the module tutor to explain their lack of academic endeavour.