Introduction to quantitative research

Title:Introduction to quantitative research

Duration:2 days
Training Units4.0
The Graduate School

This course is delivered by the Graduate School 

The Graduate School courses are very popular and the majority are run in both semesters to give you the opportunity to attend at a time of the year that suits you. Semester 1 courses will be available for booking from the second week of October and Semester 2 courses from the second week of February.



Target Audience

Early stage postgraduate research students

Early career researchers


This course is for you if you have little or no previous experience of quantitative research methods (i.e. methods involving numbers and basic statistics) but feel that there is potential to apply such methods to your research. The course covers different types of quantitative methods including calculation of summary statistics, an introduction to correlation and regression analysis, hypothesis testing and calculating p-values.

This course will be useful if:

 

  • You need to apply these methods in your own discipline

  • You want to assess whether there is potential to use quantitative methods

  • You need to interpret the output from other quantitative research

     

    Pre-requisites

    There are no pre-requisities but participants are strongly encouraged to look at 'Statistics - an intuitive introduction' before attending to familiarise themselves with common statistical techniques, terms and calculations.

     

    Please ensure that you bring a calculator with you on the day.

     

    Researchers from the Faculty of Engineering should book onto Quantitative Methods for Engineers

     

    Process

    A two-day course involving discussion and practical exercises.

     

    Description

    This is a two-day introductory course, and does not assume any previous experience of quantitative research methods.

    The aim of the course is an introduction to statistics as a whole, but will focus on several common topics of interest to many disciplines. The course does not aim to teach students how to use particular statistical packages, though there will be some examples of implementing statistical methods in R.

     

    The focus is on understanding the topics; applications are shown by simple examples calculated by hand. Although 'by hand' calculations are not common practice in research, their purpose here is for students to gain an insight into how to obtain statistical results and to appreciate the meaning and implications of their output from statistics software.

     

    The course includes the following topics:

     

  • A description of different types of quantitative data

  • Strength sand weaknesses of quantitative data

  • Discussions on data integrity

  • Describing data graphically

  • Calculating summary statistics

  • Measures of correlation

  • Conducting hypothesis testing

  • Calculating and interpreting p-values

  • Introduction to regression analysis

  • Disseminating quantitative research

     

    Course Materials

    You can self-enrol to access the course resources in Moodle by following this link: http://moodle.nottingham.ac.uk/course/view.php?id=20980

     

    Here you will find all the course information for GSTRM4/GSTQME along with the course workbook and formulae sheet.

     

    You do not need to bring a copy of the workbook, as this is intended to be used as a reference having completed the course. You need to bring a printed copy or have access to the electronic version of the formulae sheet, as this will often be used for exercises during the course.

     

    The R codes used in the class are also available on the course page. The lecture slides and solutions to exercises will be made available on the course page after completion of the course.

     

    If you cannot access the course page, please email pg-training@nottingham.ac.uk.

     

    Related courses

    'Introduction to SPSS' is an online course designed to teach you the basis of IBM® SPSS® Statistics.

     

    'Statistical test advisor' is an online learning tool for those who are familiar with the basics of data collection and analysis and require more information about statistical methods.

     

    Additionally,help is available from the Methods and Data Institute (MDI): PGRs engaged in empirical research for their PhD thesis can call on the MDI for advice on their choice and use of methods and data. A drop-in session especially for PGRs is available every Tuesday from 2-5pm - appointments must be made for this by emailing methodsanddata@nottingham.ac.uk

     

    Research Integrity - stand-alone online course designed to strengthen your awareness of your own responsibilities and accountability when planning and conducting research and provides guidance on what to do should things go wrong. 

     

    Aims

    The aim of the course is to provide a brief introduction to the principles of quantitative research methods.

     

    Objectives

    By the end of the course, you will:

     

  • Be able to distinguish between different types of quantitative data

  • Be able to provide basic numerical summaries of data

  • Be able to perform hypothesis tests and report p-values

  • Be able to interpret simple regressions

     

    Booking Conditions

     

    Latecomer policy

    Researchers should plan to arrive prior to the advertised course start time. Except for exceptional reasons, there will be no admittance to a Graduate School or Faculty Training Programme (FTP) course 15 minutes after the advertised course start time.

     

    Importance of booking commitment

    When booking onto a Graduate School short course you are entering into a commitment to attend.If you find that you are no longer available to attend you MUST cancel your place (on the system if more than three days before the course or if at short notice by emailing pg-training@nottingham.ac.uk). This will ensure that your place can be offered to another researcher on the waiting list. Failure to cancel a place results in other researchers missing out on places through the waiting list process.

    It is unacceptable for researchers to just not attend when booked onto a course. The Graduate School maintains records of those who repeatedly do not attend courses they have booked. This may affect future eligibility to book onto further Graduate School courses and will affect considerations for Graduate School funded opportunities.




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