Summary Social Research: Approaches and Fundamentals (pre-master UvT_Meth. Meas. And Stat. for PM students)

Social Research
Approaches and fundamentals

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




2

,Chapter 2. The nature of science
2.1. Knowledge as explanation and prediction

• Explanations → attempts to satisfy curiosity

• This can be done through several ways
• One possibility is explaining by citing a general empirical rule (as when we say that this
book falls when dropped because it is denser than air, and all objects denser than air
fall when dropped)
• 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:
1. Abstract statements/propositions, that relate changes in one general class of evens to
changes in another class of events in certain conditions.

• 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.

Example of a proposition:
when an individual manages a particular tasks well, then she will perform that task better in
the presence of others than when nobody else is present (this effect is called the social
facilitation effect; SFE).

• 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, and generally to explain empirical generalizations or
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

, 3. How can we research the proposition? → to apply it in a more concrete situation, so, we
will apply it into a hypothesis.

For example (building on our previous proposition):
• Athletes will perform better the more journalists there are that will comments on their
performances.

2.2. Science as a process
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.




Figure 1

Explanation of figure 1:
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 two main types of logical reasoning.

• Deductive reasoning → 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.
• Inductive reasoning → 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)

As shown in figure 1, inductive reasoning is a bottum-up process, moving from data to
empirical generalizations to theories. Deductive reasoning is a top-down process, proceeding
from general principles to specific observations or facts. Scientists reason deductively when
they show how hypothesis explains or predicts specific facts.




4