This summary is a combination of content both from the Social Research book and the lectures given by Luc van Baest.
However, this summary focuses on the Methodology part of the MMS course so I recommend you take a look at the bundle that includes all three summaries (Statistics, Methodology & S...
Chapter 2, 3, 4, 5, 8, 9 and pages 463 - 470 (chapter 15) + based on lecture slides
(Pre-Master Human Resource Studies, Social and Behavioral Science)
, Table of contents
CHAPTER 2. THE NATURE OF SCIENCE............................................................................................................ 3
2.1. KNOWLEDGE AS EXPLANATION AND PREDICTION.................................................................................................................................3
2.2. SCIENCE AS A PROCESS...........................................................................................................................................................................4
CHAPTER 3. ELEMENTS OF RESEARCH DESIGN................................................................................................5
3.1. TYPES OF VARIABLES.....................................................................................................................................................................5
3.2. TYPES OF HYPOTHESES..................................................................................................................................................................5
3.3. THE CONCEPTUAL MODEL.............................................................................................................................................................9
CHAPTER 15. MULTIVARIATE ANALYSIS (PAGE 463-470).................................................................................9
15.1. RESEARCHING THE 3TH VARIABLE EFFECT: ELABORATION................................................................................................................9
CHAPTER 3. ELEMENTS OF RESEARCH DESIGN..............................................................................................13
CHAPTER 5. SAMPLING................................................................................................................................ 15
CHAPTER 8. SURVEY RESEARCH.................................................................................................................... 19
8.1. SURVEY DESIGNS.................................................................................................................................................................................19
CHAPTER 9. SURVEY INSTRUMENTS............................................................................................................. 24
TYPES OF RESPONSE SCALES.........................................................................................................................................................................27
WORDING OF SURVEY QUESTIONS: RULES AND CAVEATS.........................................................................................................................29
CHAPTER 4. MEASUREMENT........................................................................................................................ 30
VALIDITY AND RELIABILITY...........................................................................................................................................................................32
RELIABILITY ASSESSMENT.............................................................................................................................................................................34
VALIDITY ASSESSMENT................................................................................................................................ 35
CONSTRUCT VALIDITY USING FACTOR ANALYSIS.......................................................................................... 38
Academics' approach is based on empirical sciences: we don’t just believe what we think is true
or what others claim to be true; no, we verify it with empirical data. This course deals with basic
methods and statistics needed to design and evaluate an empirical study.
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, Chapter 2. The nature of science
2.1.Knowledge as explanation and prediction
Explanations: attempts to satisfy curiosity
One possibility is explaining by citing a general empirical rule
This form of explanation meets the twin objectives of scientific knowledge.
To explain the past and present and to predict the future.
The ‘’empirical rules’’ with which scientific explanations are built consists of:
Theory(-ies) Propositions Hypotheses
1. Propositions, that are general statements regarding a regularity in the behavior or
opinion of subjects
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)
NB: The level of abstractness is important because the ideal in science is to develop the most
general understandings to establish propositions capable of enplaning and predicting the
widest possible of events.
Empirical generalization: when propositions are proposed from observations/ hypotheses
but not tested.
Scientific laws: propositions that have been repeatedly verified and widely accepted.
2. To answer ‘the why’ of propositions & generally to explain empirical generalizations/ laws,
science introduces theories:
A theory: provides an explanation for a proposition/set of propositions (no speculation).
Scientific theory: consists of a set of interconnected propositions that have the same form as
laws but are more general/abstract.
Example (building on the previous example):
Alternative theories for the SFE proposition:
‘biological’ the presence of others activates physiological triggers.
‘psychological’ people perform better when they believe they are begin
watched/evaluated.
3. How can we research the proposition? by making hypothesis = an expected
relationship between 2 or more variables that can be tested.
Remember: this implies that it is applied to a context!
Example: Athletes will perform better:
(a) the more spectators there are; (number of spectators & performance)
(b) the more journalists there are that will comment on their performances;
(c) when there is direct coverage of the event by the media (television,…).
3
, 2.2.Science as a process – empirical cycle
The product ‘knowledge is never finished but is constantly remodeled to fit the facts. The end
of an investigation often marks the beginning of another, new one.
Explanation:
At some point, theories generate predictions or hypotheses. Hypotheses are checked against data.
The data produce generalizations, and the generalizations support, contradict, or suggest
modifications of the theory. The horizontal dashed line bisecting empirical generalizations and
hypotheses, separates the world of theory from the world of research.
When people reason, they make inferences. That is, they draw conclusions based on information OR
evidence. There are 2 main types of logical reasoning:
DEDUCTION Based on theory you build a hypothesis & collect data to test them the
conclusion is absolutely certain if the evidence is true (e.g. all union members are democrats, Joan
belongs to the union, therefore Joan is a democrat)
Example
Theory: People perform better when they believe they are being watched/evaluated.
Hypothesis: Athletes perform better the more spectators there are.
Observation: Observe that athletes perform better when more spectators are watching.
INDUCTION from the data try to find a logical structure (generalization) and from that
try to find a theory (grounded theory)
the conclusion is uncertain even if the evidence is true because its content goes
beyond the evidence!! We can only judge the probability (e.g. Hubert, Walter and Joan, who
are union members are democrats, therefore are all union members democrats)
Example
Observation: Observe that athletes perform better when more spectators are watching.
Empirical generalization: Individuals perform better the more people are watching.
Theory: People perform better when they believe they are being watched/evaluated.
Conclusion:
Induction is a bottom-up process, moving from data to empirical generalizations to theories.
Deduction is a top-down process, proceeding from general principles to specific observations/
facts.
Scientists reason deductively when they show how hypothesis explain/ predicts specific facts.
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