skip to navigation skip to content
Mon 19 Jan, Mon 26 Jan, ... Mon 9 Feb 2015
14:00 - 16:00

Venue: University Information Services, Titan Teaching Room 1, New Museums Site

Provided by: Social Sciences Research Methods Centre


Booking

Bookings cannot be made on this event (Programme is completed).


Other dates:

No more events



Register interest
Register your interest - if you would be interested in additional dates being scheduled.


Booking / availability

Logistic Regression
Prerequisites

Mon 19 Jan, Mon 26 Jan, ... Mon 9 Feb 2015

Description


Bookings for this module open on THURSDAY, 11 DECEMBER at 10:00 am
For more information see: http://www.ssrmc.group.cam.ac.uk/ssrmc-modules/core/making/windows

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

Often researchers deal with outcomes that come in the form of ‘yes’ or ‘no’ responses to questions, or where respondents only have two options to choose from. Similarly, there are occasions where researchers are using unordered (e.g. red, yellow, green) or ordered categories (low, medium, high) as response variables. This module will teach you about how to analyse these different types of data. This will include (a) interpreting outputs, (b) conducting diagnostic tests, (c) calculating effect sizes and (d) making predictions.

Target audience
Prerequisites
  • a working knowledge of statistical methods up to the level of multiple linear regression
  • familiarity with interpreting regression output from statistical programmes
  • a University Information Services (Computing) Desktop Services password (http://www.ucs.cam.ac.uk/linkpages/newcomers)
  • access to CamTools
Sessions

Number of sessions: 4

# Date Time Venue Trainer
1 Mon 19 Jan 2015   14:00 - 16:00 14:00 - 16:00 University Information Services, Titan Teaching Room 1, New Museums Site map N. Peri-Rotem
2 Mon 26 Jan 2015   14:00 - 16:00 14:00 - 16:00 University Information Services, Titan Teaching Room 1, New Museums Site map N. Peri-Rotem
3 Mon 2 Feb 2015   14:00 - 16:00 14:00 - 16:00 University Information Services, Titan Teaching Room 1, New Museums Site map N. Peri-Rotem
4 Mon 9 Feb 2015   14:00 - 16:00 14:00 - 16:00 University Information Services, Titan Teaching Room 1, New Museums Site map N. Peri-Rotem
Topics covered
  • Session 1: Binary I - Probability, odds & logit
  • Session 2: Binary II - Logistic regression in SPSS
  • Session 3: Binary III - Diagnostics
  • Session 4: Binary IV - Categorical and ordinal
Objectives

To teach students how to analyse different types of data using SPSS

Aims
  • to deal with outcomes that form "yes" or "no" responses to questions and unordered or ordered response variable
Format

Presentations, demonstrations and practicals

Taught using

STATA on MCS

Assessement

One written exercise [optional, dependent upon department]

Textbook(s)
  • Field, A. Discovering Statistics Using SPSS (3rd ed.) London:Sage.
  • Tarling, R (2009) Statistical Modelling for Social Researchers: Principles and Practice. London: Routledge.
  • Long, J.S. (1997) Regression models for categorical and limited dependent variables London: Sage. (Advanced)

In addition, there is a chapter on ‘Nonlinear estimation’ in the Electronic Statistics Textbook (EST): http://www.statsoft.com/textbook/stathome.html

Notes
  • To gain maximum benefits from the course it is important that students do not see this course in isolation from the other MPhil courses or research training they are taking.
  • Responsibility lies with each student to consider the potential for their own research using methods common in fields of the social sciences that may seem remote. Ideally this task will be facilitated by integration of the SSRMC with discipline-specific courses in their departments and through reading and discussion.
Duration

Four sessions of two hours each.

Frequency

Once a week for four weeks.


Booking / availability