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Mini summary for the methods, measurement, statistics course
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Mini summary for the methods, measurement, statistics course for pre-master students at Tilburg University
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Methods, Measurement & StatisticsLecturer: Guy Moors
Methods: teach you how to design a study to answer your research question
Measurement: teach you how to measure social and psychological constructs
Statistics: teach you how to describe and analyze your data and test hypotheses
Structure of the course:
• Study the literature before following a lecture
• Each week you have 2 lectures: 1 on methods and 1 on statistics
• You have tutorials on the statistical topics (except from week 1)
• In week 2, 4 and 6 you will have an SPSS computer lab sessions
Tests:
1. Multiple choice exam: test knowledge and comprehensions of course material
50 multiple choice questions: 25 questions on methods/measurement and 25 questions on
statistics/measurement
2. SPSS practical test: tests whether you master basic SPSS skills. Test is similar to assignments
in SPSS lab sessions
Graded Pass or Fail
Lecture 1: introduction to Methods
Literature: B.C. Straits & R.A. Singleton, Social Research, Approaches and Fundamentals, New York: Oxford
University Press, 2018 (International 6th Edition)
Selected chapters: 2, 3, 5, 8, 9, 4 + selected pages chapter 15 on “Elaboration: Tables and Beyond”
Tip: lecture sheets cover the main topics; read the chapters with focus on sections that can be linked to topics
introduced in the lectures. The reading is complementary to the sheets (sheets are not a summary).
Methods lectures:
• Voice over recordings in lecture sheets
Your job: view, take notes, email questions
• Weekly class meetings: complementary explanation, answering your questions, in-class
assignments
Lecture overview:
- Cornerstones of social research
- Science as a process
- Concepts, variables and hypotheses
- Types of hypotheses, conceptual models and elaboration
- Causality
- Unit of analysis and nested data
- Logical fallacies
- Principles of sampling
, - Survey research
- Survey instruments
- Measurement: validity and reliability
- Measurement: factor analysis
Prelude: On ‘naive’ statistics
A journal reported that the economy of country X was under severe stress in 2021, because of the fact
that approximately 16% of all employees suffered from burnout.
Seems very impressive, isn’t it? So let’s have a closer look:
The journal reported a figure from country X’s national bureau of statistics (NBS). Detailed
information from NBS also indicated that:
- 80% suffered from mild burn-out and were on sick leave for 2 months;
- 15% had regular amount of burnout and recovered within 6 months;
- And the remaining 5% did not work for the full year.
Assume:
- 10.000.000 employees of which 1.600.000 (16%) burnouts
- 264 working days per year (= 22 on average per month)
- Total number of working days in one year = 10.000.000 * 264 = 2.640.000.000 (a)
- Total number of working day lost by sick leave due to burnout = 109.120.000 => 4,13% of (a)
or +- 11 working days on average
Lesson learned:
• Be prudent with just looking at one figure, you need to put in in perspective.
• Don’t let emotions run ahead of you logical thinking, be critical.
• Be cleverer than the average journalist ;)
Part 1: Cornerstones of social research
Proposition
Example: “When an individual manages a particular task well, then (s)he will perform that task better
in the presence of others than when nobody else is present.”
(= social facilitation effect SFE)
= general statement regarding a regularity in the behavior or opinion of subjects
The question is: why is this the case?
Theory = provides an explanation for a proposition or set of propositions ≠ speculation!
Example: Alternative theories for the SFE proposition: biological versus psychological ‘explanation’
- ‘Biological’: the presence of others activates physiological triggers
- ‘Psychological’: people perform better when they believe they are being watched/evaluated
= nature versus nurture debate in many social and behavioral sciences
,Next question: how can we research that?
= applying it in a concrete situation
Example: athletics
Hypotheses = athletes will perform better:
A) The more spectators there are;
B) The more journalist there are that will comment on their performances;
C) When there is direct coverage of the event by the media (television, …)
Distinguishing proposition from hypothesis: A class example
A well-known ‘theorem’ in the social sciences states that (Thomas theorem)
“If men define situations as real, they are real in their consequences.”
Assignment: define a proposition and a hypothesis that can be matches with this theorem in the
context of ‘superstition’.
Proposition: When someone strongly beliefs in the existence of invisible creatures having certain
powers, the alleged powers assigned to such creatures become real in their consequences.
Hypothesis: The more convinced children are that invisible monsters live under their bed waiting to
haunt them in the dark, the more likely these children will experience nightmares that prevent them
from sleeping in dark bedrooms.
An example on how this would translate into a multiple choice question:
Evaluate the following statement:
“the more convinced children are that monsters live under their bed waiting to haunt them when it is
dark, the more likely these children will experience nightmares that prevent them from sleeping in
dark bedrooms.” is an example of a hypothesis rather than a proposition
• True or false: True
, Part 2: Science as a process: induction – deduction
Example: Inductively induced research project
Johan Denollet’s “D-personality”:
- He was working as an psychologist at the cardiac rehabilitation unit of the Antwerp
University Hospital when he observed that different patients had different chances of
rehabilitation that seems to be linked to personality characteristics.
- He then developed a method that allowed measuring two aspects of personality (‘negative
affectivity’ (NA( and ‘social inhibition’ (SI)) and found that when patients combined both of
them these patients had less chance of rehabilitation.
- He then developed his theory on the D-type personality (D= distress = combining NA + SI)
arguing that his broad and stable personality trait is a clear risk factor of cardiac events in the
future.
Peter M. Blau and Otis D. Duncan:
- Developed the status attainment model in the 1960’s to explain social mobility patterns in
societies
- The overarching research question was: what attributes facilitate the movement of
individuals into occupations with particular (desirable) social status?
- Thet argued that occupational outcomes are shaped by family of orgigin in a direct and
indirect way. A typical model is as follows:
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