This document provides an overview of the concept of "data cooking", which refers to falsifying or fabricating data in scientific research. It discusses how data cooking undermines the integrity of research. Examples are given, such as inflating sample sizes. Related terms like "data falsification" are also outlined. The document suggests ways to prevent data cooking, such as training researchers in integrity, supervising high-stake projects, and penalizing those found guilty of misconduct.

1. DATA COOKING: AN
OVERVIEW
Presented By:
SHIKHA AWASTHI
Junior Research Fellow
Department of Library & Information Science
Baba Saheb Bheem Rao Ambedkar University
Lucknow, 226025

2. RESEARCH IS WHAT I’M DOING WHEN I DON’T KNOW
WHAT I AM DOING.
WERNHER VON BRAUN
As researchers who often work with qualitative data, we frequently asked
to review qualitative papers and to speak about how to conduct
qualitative research. Through these experiences, we have come to believe
that there are prevalent misconceptions about the range of roles that
qualitative data can play in research on strategic organization.

3. CONTD…
This is an overview of data cooking
which means the centrality of the
relationship between analytic
perspectives and methodological
issues and the consequent
requirement to go beyond purely a
‘cookbook’ version of research
methods.

4. WHAT IS DATA COOKING
 Data Cooking is falsification and fabrication of data
which unfortunately is becoming the part of
scientific research throughout the world.
By : Haider A. Naqvi

5. EXAMPLE:
The most common practice seen at times is
to increase the number to meet the sample
size requirement; after having done 50 or
so questionnaires/interviews, the
researcher increase the sample size (by
just multiplying by 2, 4 even 10) because
either he/she thinks that he/she does not
need to or he/ she does not have time for it.

6. CONTD…
 New york Times senior reporter Jayson Blair forced to
resign after being accused of doing fraud with the data.
The newspaper said at least 36 of the 73 articles he had
written had problems with accuracy calling and deception a
“low point” in the news paper’s history.

7. RELATED TERMS
 Data falsification,
 Data fabrication,
 Cherry-picking,
 Data rigging
used for data manipulation which means the data itself might be
correct, but the picking of certain data points and twisting that data
to make it look like it means something different in order to support
your hypothesis is unethical.

8. BUT WHY IT HAPPENS
Lacking training research methods, additional burden of
meeting deadlines for promotions, people resort to
tinkering with numbers. Another compulsion behind this
falsification is that negative results are not interesting to
publishers.

9. PREVENTING DATA COOKING
 We are in need of developing a system which deters
such academic misconduct;
 Keep portfolios of student writing
 Vary assignments and topic suggestions each semester
 Describe the degree of collaboration is acceptable to
your students
 training people in research integrity through mentorship
from the grounding years,
 supervising and auditing the high-stake projects and
penalizing those who are found guilty.

10. CONTD…
 Require an annotated bibliography
 Shorter papers are okay
 Academic leadership has to work hand-in-hand with
researchers and journal editors to root out the evil
of this research misconduct.