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Research Methodology and Descriptive Statistics full course summary (passed with an 8)

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A summary of the complete course including the book "The Practice of Social Research" by Earl Babbie, all micro lectures, slides and notes . After making and studying this summary I passed the course with a 8. Good luck!

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  • June 12, 2018
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  • 2017/2018
  • Summary

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By: easysummary • 4 year ago

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By: easysummary • 5 year ago

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By: easysummary • 5 year ago

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Research Methodology and Descriptive Statistics
By Easysummary for Stuvia

,Table of content


1. What is empirical research? 2

2. What are clear research questions? 2

3. What are data? 3

4. How to use SPSS or R? 4

5. Conceptualization of constructs 5

6. Collecting data: surveys 6

7. Collecting data: observation and content analysis 9

8. Two aspects of data quality 10

9. Displaying univariate data 12

10. Summarizing ratio variables 12

11. Variance and standard deviation 13

12. Distributions 13

13. Causality and bivariate causal hypotheses 14

14. Visualizing and analyzing bivariate relationships using SPSS or R 15

15. Causality and the effect of third variables 15

16. Research designs for testing causal hypotheses 16

17. Elaboration: analyzing multi-variate relationships using tables 20

18. Visualizing multivariate relationships 23

19. Sampling 23

20. First steps towards inference: certainty about means 24

21. First steps towards inference: effects and significance 24

22. Research ethics 25




Easysummary 1

,1. What is empirical research?


Empirical research is about: systematically answering empirical research questions; excluding the
possibility that other answers are better than the answers we give. Empirical: about things we can observe.
Research question: asked in the context of decision-making.


Design and (cycle of) decision-making




Need and problem analysis: how big is the problem?


Ex ante evaluation: (previous) evaluation of options  can we expect an option to work?
Program monitoring
Ex post evaluation (effect / impact research): did the selected option have the expected outcome?
(outcome evaluation)


Confirmation bias: search for, analyses and recall of information in a way that confirms pre-existing
beliefs, while giving disproportionately less consideration to alternative interpretations. It affects what we
think is true and because of that it affects what we do. Why confirmation bias is strong: limitations, wishful
thinking, and consistency (old evidence and we want the new evidence to confirm the old because we want
to world to be consistent). Consequences of confirmation bias: mistakes in knowledge (we think we know
things), we make bad decisions on the basis of this knowledge. How do we avoid confirmation bias:
systematic data gathering, research methodology.


Wheel of science: is an important logic, it is not logistical / empirical cycle 



2. What are clear research questions?


Research question
 Normative: what should be the case, what is correct, what
is justifiable (don’t confuse with legal facts). Is it justifiable
to have the death penalty in a country?
 Conceptual: about the meaning of concepts. What do we
mean with ‘the death penalty’?




Easysummary 2

,  Empirical: what is/will be/why, can only be answered by observations. Which countries still have
the death penalty? Does the death penalty reduce crime? Why did some countries abolish the death
penalty, while other countries still have it? How many people were legally executed between 2000
and 2012 in the US?
o Descriptive: description (not about causes and effects). Which countries still have the
death penalty in 2015? How many
people were legally executed?
o Explanatory: about causes and effects.
Does the death penalty reduce crime
(having it on one hand and level of crime
on the other so about cause and effect)?
Why did some countries abolish the
death penalty while others still have it?



Practical questions: political, social, personal problems. Problem & need analyses  find & design options
(which options have been used by others)  ex ante evaluation  choice  implementation (process
evaluation)  ex post choice evaluation  problem & need
Theoretical questions: puzzles, existing research (empirical cycle).


Unit (of analysis): what/who, objects like people and countries. If the variable is known, ask ‘of who’ and
you’ll find the answer.
Variable (attributes/values): characteristics expressed as attributes/values of those units or used to
describe those units. If the unit is known, ask what characteristic does the unit have?
Setting: time and place


Induction: starting with data and trying to arrive at conclusions based on this data
Deduction: starting with theory and then thinking how we can test the theory


Criteria that make up a good question: context (theoretical/practical, describe/explain), formulation of the
type of question (descriptive/causal), UTOS (units (variables), treatments, objects, setting time/place) and
is the question answerable (theory /methods like research design/data).



3. What are data?


Units of analysis are the units we are interested in (individuals, groups, social constructs). Ask questions
and formulate hypotheses about the units of analysis.
Units of observation: are the units we collect data from. Collect data about the units of observations and
make sure the two are linked.


Ecological fallacy: mixing up units of observation
and units of analyses. Drawing conclusions
(making generalizations) about lower level units
solely on the basis of aggregate data.




Easysummary 3

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