Net set research methodology

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Research Aptitude
Goalfinder Classes: CBSE NET 2016 - Paper 1
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Research Aptitude
Goalfinder Classes: CBSE NET 2016 - Paper 1
Table of Content
Aims and Objectives of Research
Hypotheses
Null hypothesis
Alternative Hypothesis
Significance, P-value, one tailed test etc.
Types of Research
Application
As per Objectives
Exploratory Research
Conclusive Research
Correlation Research
Explanatory (Causal / Experimental) Research
Comparison between exploratory, descriptive and causal
Experimentation and Market Testing
Type of Information Sought
Qualitative and Quantitative research
Other 10 types of Research Design
Data: Primary and Secondary Data
Data Collection and Method of study in research
Content analysis
Game or role-playing
Primary Market Research Method Quantitative
Experiments Quasi Experiment and Field Trials
Sociogram
Variable and their Types
Sampling Methods
a. Probability Sampling
1. Simple Random Sampling
2. Systematic Sampling:
3. Stratified random sampling
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4. Cluster Sampling
b. Non Probability Sampling
1. Convenience sampling
2. Purposive /Judgment Sampling
4. Snowball Sample
Types of Errors:
Measurement
Data Exploration
Univariate vs. Bivariate Data
Analysis of Variance (ANOVA)
Problem Solving
Central Tendency and Normal Distribution
Normal Distribution
Variance
Effect Size
Frequency distribution: Skewed, Mesokurtic, Leptokurtic, Platykurtic
Hypothesis Testing
"True" Mean and Confidence Interval
Margin of Error (Confidence Interval)
Type I errors and type II errors
One-Tailed and Two-Tailed Tests
Parametric and Non-parametric Tests
Bi- and Multivariate Inferential Statistical Tests (Parametric)
Chi Square
Degrees of freedom
T-test
Z-test and t-test
Analysis of Variance (ANOVA)
Correlation (measures relationships between two variables)
Factor analysis
Sign test
Run Test
Other data display methods
Experimental Design
Pre-Experimental Designs
Quasi experiment Design
True Experimental Design
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Research Aptitude

Reliability and Validity
Validity
Research Requirements
Steps of Research
The Preliminary Section
Research Ethics
Seminar, Workshop, Conference, Symposium
Paper, Article
Quality of a research journal
Style Rules
Appendix : Research Methodology Diagram
APA Format
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Sample: Research Aptitude
Null hypothesis
The null hypothesis (H0) is a hypothesis which the researcher tries to disprove, reject or nullify. The 'null' often
refers to the common view of something, while the alternative hypothesis is what the researcher really thinks
is the cause of a phenomenon.
The null hypothesis is essentially the "devil's advocate" position. That is, it assumes that whatever you are
trying to prove did not happen (hint: it usually states that something equals zero). For example, the two
different teaching methods did not result in different exam performances (i.e., zero difference). Another
example might be that there is no relationship between anxiety and athletic performance (i.e., the slope is
zero). The alternative hypothesis states the opposite and is usually the hypothesis you are trying to prove (e.g.,
the two different teaching methods did result in different exam performances). Initially, you can state these
hypotheses in more general terms (e.g., using terms like "effect", "relationship", etc.).
The simplistic definition of the null is as the opposite of the alternative hypothesis, H1, although the principle is
a little more complex than that.
A researcher may postulate a hypothesis:
H1: Tomato plants exhibit a higher rate of growth when planted in compost rather than in soil.
And a null hypothesis:
H0: Tomato plants do not exhibit a higher rate of growth when planted in compost rather than soil.
It is important to carefully select the wording of the null, and ensure that it is as specific as possible. For
example, the researcher might postulate a null hypothesis:
H0: Tomato plants show no difference in growth rates when planted in compost rather than soil.
There is a major flaw with this H0. If the plants actually grow more slowly in compost than in soil, an impasse is
reached. H1 is not supported, but neither is H0, because there is a difference in growth rates.
If the null is rejected, with no alternative, the experiment may be invalid. This is the reason why science uses a
battery of deductive and inductive processes to ensure that there are no flaws in the hypotheses.
Many scientists neglect the null, assuming that it is merely the opposite of the alternative, but it is good
practice to spend a little time creating a sound hypothesis. It is not possible to change any hypothesis
retrospectively, including H0.
The first step in evaluating sample results is to set up a null hypothesis (Ho). The null hypothesis is a
hypothesis of no differences. We formulate it for the express purpose of rejecting it. It is formulated before we
collect the data (a priori). For example, we may wish to know whether a particular promotional campaign has
succeeded in increasing awareness amongst housewives of a certain brand of biscuit. Before the campaign we
have a certain measure of awareness, say x%. After the campaign we obtain another measure of the
awareness, say y%. The null hypothesis in this case would be that "there is no difference between the
proportions aware of the brand, before and after the campaign",
Since we are dealing with sample results, we would expect some differences; and we must try and establish
whether these differences are real (i.e. statistically significant) or whether they are due to random error or
chance.
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Exploratory Research
Exploratory research for discovering new ideas, understanding a problem, screening alternatives
• To develop initial hunches or insights
• To provide direction for any further
research needed.
• To shed light on the nature of a situation
• To identify any specific objectives or data
needs for additional research.
• Most useful when a decision maker needs
 to better understand a situation
 to identify decision alternatives.
Exploratory research is often conducted because a problem has not been clearly defined as yet, or its real
scope is as yet unclear. It allows the researcher to familiarize him/herself with the problem or concept to be
studied, and perhaps generate hypotheses to be tested. It is the initial research, before more conclusive
research is undertaken.
As the name implies, exploratory research is the initial exploration done to get an idea and insights
into the problem. Research is a relatively expensive process; exploratory research ensures that this
process is not initiated without a thorough understanding of the problem. This study is qualitative
(understanding the concept) rather than quantitative (providing precise measurement). Also, this
type of research does not give conclusive evidence and subsequent research needs to be done.
Further, the following purposes justify the use of exploratory research:
Exploratory Research - Research conducted to clarify and define nature of the problem
Experience
Surveys
individuals with
knowledge
about a
particular
research
problem are
surveyed
Secondary
Data Analysis
data collected
for some other
purpose is
reviewed to
solve early
stage
problems of
research
Case Studies
thoroughly
investigating
few
situations
similar to the
research
problem at
hand
Pilot Studies
small scale
research
project using
sampling but
no rigorous
standards are
applied
Focus Group
A group
discussion
conducted
with the
participation
of and 7 - 12
people to
capture their
experiences
and views
Literature
Survey
search of all
types of
published
literature in
order to
identify a
breadth of
good quality
references
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Phenomenological research
Phenomenological research an inductive, descriptive research approach developed from phenomenological
philosophy; its aim is to describe an experience as it is actually lived by the person(also refer to page 22)
Phenomenology is the study of structures of consciousness as experienced from the first-person point of view.
Literally, phenomenology is the study of “phenomena”: appearances of things, or things as they appear in our
experience, or the ways we experience things, thus the meanings things have in our experience.
Phenomenology studies conscious experience as experienced from the subjective or first person point of view.
This field of philosophy is then to be distinguished from, and related to, the other main fields of philosophy:
ontology (the study of being or what is), epistemology (the study of knowledge), logic (the study of valid
reasoning), ethics (the study of right and wrong action), etc. is often restricted to the characterization of
sensory qualities of seeing, hearing, etc.: what it is like to have sensations of various kinds. However, our
experience is normally much richer in content than mere sensation.
Basically, phenomenology studies the structure of various types of experience ranging from perception,
thought, memory, imagination, emotion, desire, and volition to bodily awareness, embodied action, and social
activity, including linguistic activity.
• Phenomenology is concerned with the study of experience from the perspective of the individual,
‘bracketing’ taken-for-granted assumptions and usual ways of perceiving.
• They are based in a paradigm of personal knowledge and subjectivity, and emphasise the importance
of personal perspective and interpretation.
• As such they are powerful for understanding subjective experience, gaining insights into people’s
motivations and actions, and cutting through the clutter of taken-for-granted assumptions and
conventional wisdom.
Ex-Post Facto Research
The term ex post facto in Latin means 'after the fact.' Example an ex post facto law is a law that is passed after
the fact that criminalizes an action that was legal when it was committed like changing the age of juvenile
from 18 to 16 after they are found to commit heinous crimes.
An ex post facto research design is a method in which groups with qualities that already exist are compared on
some dependent variable. Also known as "after the fact" research, an ex post facto design is considered quasi-
experimental because the subjects are not randomly assigned - they are grouped based on a particular
characteristic or trait.
Although differing groups are analyzed and compared in regards to independent and dependent variables it is
not a true experiment because it lacks random assignment. The assignment of subjects to different groups is
based on whichever variable is of interest to the researchers.
For example, a researcher is interested in how weight influences self-esteem levels in adults. So the
participants would be separated into differing groups (underweight, normal weight, overweight) and their self-
esteem levels measured. This is an ex post facto design because a pre-existing characteristic (weight) was used
to form the groups.
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Variable
Scientists use an experiment to search for cause and effect relationships in nature. In other words, they design
an experiment so that changes to one item cause something else to vary in a predictable way.
These changing quantities are called variables. A variable is any factor, trait, or condition that can exist in
differing amounts or types. An experiment usually has three kinds of variables:
①independent, ②dependent, and ③controlled.
Independent variable:
The independent variable is the one that is changed by the scientist. To ensure a fair test, a good experiment
has only one independent variable. As the scientist changes the independent variable, he or she observes what
happens.
Dependent variable:
The scientist focuses his or her observations on the dependent variable to see how it responds to the change
made to the independent variable. The new value of the dependent variable is caused by and depends on the
value of the independent variable.
For example, if you open a faucet (the independent variable), the quantity of water flowing (dependent
variable) changes in response--you observe that the water flow increases. The number of dependent variables
in an experiment varies, but there is often more than one.
Controlled variables:
Experiments also have controlled variables. Controlled variables are quantities that a scientist wants to
remain constant, and he must observe them as carefully as the dependent variables. For example, if we want
to measure how much water flow increases when we open a faucet, it is important to make sure that the
water pressure (the controlled variable) is held constant. That's because both the water pressure and the
opening of a faucet have an impact on how much water flows. If we change both of them at the same time, we
can't be sure how much of the change in water flow is because of the faucet opening and how much because
of the water pressure. In other words, it would not be a fair test. Most experiments have more than one
controlled variable. Some people refer to controlled variables as "constant variables."
In an experiment, if you have multiple trials, you want to reduce the number of changes between each trial. If
you tell the ball throwers on the first day to toss a ping-pong ball into a little red cup, and on the second day
you tell ball throwers to hurl a bowling ball into a barrel, your results are going to be different.
Each experiment has control variables, which are variables that are kept the same in each trial. These would
be things like:
• Ball size and weight
• Number of people in each trial
• Where participants are throwing the ball and
• Instructions of what to do
In a good experiment, the scientist must be able to measure the values for each variable. Weight or mass is an
example of a variable that is very easy to measure. However, imagine trying to do an experiment where one of
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the variables is love. There is no such thing as a "love-meter." You might have a belief that someone is in love,
but you cannot really be sure, and you would probably have friends that don't agree with you. So, love is not
measurable in a scientific sense; therefore, it would be a poor variable to use in an experiment.
As a researcher, you're going to perform an experiment
Hunger and Performance: so let's say your experiment will examine four people's ability to throw a ball when
they haven't eaten for a specific period of time - 6, 12, 18 and 24 hours.
We can say that in your experiment, you are going to do something and then see what happens to other things.
But, that sentence isn't very scientific. So, we're going to learn some new words to replace the unscientific
ones, so we can provide a scientific explanation for what you're going to do in your experiment.
The starting point here is to identify what a variable is. A variable is defined as anything that has a quantity or
quality that varies. Your experiment's variables are not eating and throwing a ball.
Now, let's science up that earlier statement, ‘You are going to manipulate a variable to see what happens to
another variable.' It still isn't quite right because we're using the blandest term for variable and we didn't
differentiate between the variables. Let's take a look at some other terms that will help us make this
statement more scientific and specific.
Independent: Hrs. (not eaten)
Dependent : Ability to throw a ball
A moment ago, we discussed the two variables in our experiment - hunger and throwing a ball. But, they are
both better defined by the terms 'dependent' or 'independent' variable.
The dependent variable is the variable a researcher is interested in. The changes to the dependent variable
are what the researcher is trying to measure with all their fancy techniques. In our example, your dependent
variable is the person's ability to throw a ball. We're trying to measure the change in ball throwing as
influenced by hunger.
An independent variable is a variable believed to affect the dependent variable. This is the variable that you,
the researcher, will manipulate to see if it makes the dependent variable change. In our example of hungry
people throwing a ball, our independent variable is how long it's been since they've eaten.
To reiterate, the independent variable is the thing over which the researcher has control and is manipulating.
In this experiment, the researcher is controlling the food intake of the participant. The dependent variable is
believed to be dependent on the independent variable.
Your experiment's dependent variable is the ball throwing, which will hopefully change due to the
independent variable. So now, our scientific sentence is, 'You are going to manipulate an independent variable
to see what happens to the dependent variable.'
The simplest experimental design includes two variables and two groups of participants.
The two variables (Independent versus Dependent variables)
• The IV is the predictor variable whereas the DV is the outcome variable.
• Researchers manipulate and control the IV to study its effect on the DV.
The two groups of participants (Control versus Experimental group).
• Before beginning the experiment, the researcher (randomly) assigns his/her sample to two different
groups: the control group and the experimental (treatment group or clinical group).
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2. Purposive /Judgment Sampling
• Also called as the Purposive sampling.
• Firstly, a sample is drawn from the population, which a researcher thinks to be a representative of the
population.
• All the members do not get the chance to get selected in the sample.
In judgement sampling researcher relies on his or her own judgement when choosing members of population
to participate in the study.
Judgement sampling is a non-probability sampling method and it occurs when “elements selected for the
sample are chosen by the judgement of the researcher. Researchers often believe that they can obtain a
representative sample by using a sound judgement, which will result in saving time and money”.
Judgment sampling is used quite frequently in qualitative research where the desire is to develop hypothesis
rather than to generalize to larger populations.
Example: In previous example of interviewing 10,000 university students, if we were only interested in
achieving a sample size of say 100 students, we may simply stand at the entrance to a particular campus most
convenient to us, where it would be easy to invite the many students that pass by to take part in the research.
Alternatively, judgement sampling method may prove to be effective when only limited numbers of people
can serve as primary data sources due to the nature of research design and aims and objectives.
For example, for a research analyzing effects of personal tragedy such as family bereavement on performance
of senior level managers the researcher may use his/her own judgement in order to choose senior level
managers who could particulate in in-depth interviews.
This sampling method offers the following advantages:
• Less time and low cost consuming compared to many other sampling methods because only suitable
candidates are targeted
• Results of purposive sampling are usually more representative of target population compared to other
sampling methods
• Purposive sampling can be the only way to recruit the members of rare or much sought after groups
Purposive sampling may be associated with the following disadvantages:
• Limited representation of wider population
• low level of reliability and high levels of personal bias
Types of Judgmental sampling:
a) Typical case: Explains cases that are average and normal.
b) Extreme or deviant case: Deriving samples from cases that are perceived as unusual or rare such as
exploring the reasons for corporate failure by interviewing executives that have been fired by shareholders.
c) Convenience: The choice of purposive sampling only due to its convenience in terms of saving time,
money and efforts. This specific case is naturally associated with lower research validity and credibility.
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Frequency Distribution:
In statistics, a frequency distribution is a table that displays the frequency of various outcomes in a sample.
Each entry in the table contains the frequency or count of the occurrences of values within a particular group
or interval, and in this way, the table summarizes the distribution of values in the sample.
An example of a univariate (i.e. single variable) frequency table, the frequency of each response to a survey
question is depicted.
Rank Degree of agreement Number
1 Strongly agree 20
2 Agree somewhat 30
3 Not sure 20
4 Disagree somewhat 15
5 Strongly disagree 15
A different tabulation scheme aggregates values into bins such that each bin encompasses a range of values.
For example, the heights of the students in a class could be organized into the following frequency table.
Height range Number of students Cumulative number
less than 5.0 feet 25 25
5.0–5.5 feet 35 60
5.5–6.0 feet 20 80
6.0–6.5 feet 20 100
A frequency distribution shows us a summarized grouping of data divided into mutually exclusive classes and
the number of occurrences in a class. It is a way of showing unorganized data e.g. to show results of an
election, income of people for a certain region, sales of a product within a certain period, student loan
amounts of graduates, etc. Some of the graphs that can be used with frequency distributions are histograms,
line charts, bar charts and pie charts. Frequency distributions are used for both qualitative and quantitative
data.
One-Way Tables in Statistics
A one-way table is the tabular equivalent of a bar chart. Like a bar chart, a one-way table displays categorical
data in the form of frequency counts and/or relative frequencies.
A one-way table is distinguished from a two-way table (described in the next lesson); because the data entries
in a one-way table refer to one variable, whereas the data entries in a two-way table refer to two variables.
When a one-way table shows frequency counts for a particular category of a categorical variable, it is called a
frequency table.
Below, the bar chart and the frequency table display the same data. Both show frequency counts, representing
travel choices of 10 travel agency clients.
Choice USA Europe Asia
Frequency 5 3 2
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Margin of Error (Confidence Interval)
No sample will be perfect, so you need to decide how much error to allow. The confidence interval determines
how much higher or lower than the population mean you are willing to let your sample mean fall. The margin
of error expresses the maximum expected difference between the true population mean and a sample
estimate of that mean.
For example, a pollster might report that 50% of voters will choose a BJP candidate. To indicate the quality of
the survey result, the pollster might add that the margin of error is +5%, with a confidence level of 90%. This
means that if the survey were repeated many times with different samples, the true percentage of BJP voters
would fall within the margin of error 90% of the time.
The Critical Value
There should then be limits set on the critical value, beyond which you can assume that the experiment proves
that the null hypothesis is false and therefore using statistical hypothesis testing, the experiment shows there
is enough evidence to reject the null hypothesis. This is generally set at 5% or 1% chance probability.
Significance level
The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is
true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when
there is no actual difference.
We'll use these tools to test the following hypotheses(as in the below graph)
• Null hypothesis: The population mean equals the hypothesized mean (260).
• Alternative hypothesis: The population mean differs from the hypothesized mean (260).
These types of definitions can be hard to understand because of their technical nature. A picture makes the
concepts much easier to comprehend!
The significance level determines how far out from the null hypothesis value we'll draw that line on the graph.
To graph a significance level of 0.05, we need to shade the 5% of the distribution that is furthest away from
the null hypothesis.
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Factor analysis
The main applications of factor analytic techniques are: (1) to reduce the number of variables and (2) to
detect structure in the relationships between variables, that is to classify variables. Therefore, factor analysis is
applied as a data reduction or structure detection method (the term factor analysis was first introduced by
Thurstone, 1931).
Suppose we want to measure people's satisfaction with their lives. We design a satisfaction questionnaire with
various items; among other things we ask our subjects how satisfied they are with their hobbies (item 1) and
how intensely they are pursuing a hobby (item 2). Most likely, the responses to the two items are highly
correlated with each other.
Combining Two Variables into a Single Factor
You can summarize the correlation between two variables in a scatterplot. A regression line can then be fitted
that represents the "best" summary of the linear relationship between the variables. If we could define a
variable that would approximate the regression line in such a plot, then that variable would capture most of
the "essence" of the two items. Subjects' single scores on that new factor, represented by the regression line,
could then be used in future data analyses to represent that essence of the two items. In a sense we have
reduced the two variables to one factor. Note that the new factor is actually a linear combination of the two
variables.
If we extend the two-variable example to multiple variables, then the computations become more involved,
but the basic principle of expressing two or more variables by a single factor remains the same.
Sign test
The sign test is a statistical method to test for consistent differences between pairs of observations, such as
the weight of subjects before and after treatment. Given pairs of observations (such as weight pre- and post-
treatment) for each subject, the sign test determines if one member of the pair (such as pre-treatment) tends
to be greater than (or less than) the other member of the pair (post-treatment).
The paired observations may be designated x and y.
For comparisons of paired observations (x,y), the sign test is most useful if comparisons can only be expressed
as x > y, x = y, or x < y. If, instead, the observations can be expressed as numeric quantities (x = 7, y = 18), or as
ranks (rank of x = 1st, rank of y = 8th), then the paired t-test or the Wilcoxon signed-rank test. will usually have
greater power than the sign test to detect consistent differences.
If X and Y are quantitative variables, the sign test can be used to test the hypothesis that the difference
between the median of X and the median of Y is zero, assuming continuous distributions of the two random
variables X and Y, in the situation when we can draw paired samples from X and Y.
The sign test can also test if the median of a collection of numbers is significantly greater than or less than a
specified value. For example, given a list of student grades in a class, the sign test can determine if the median
grade is significantly different from, say, 75 out of 100.
The sign test is a non-parametric test which makes very few assumptions about the nature of the distributions
under test - this means that it has very general applicability but may lack the statistical power of the
alternative tests.
Run Test
Run test (Wald Wolfowitz test) is a non-parametric test, that examines a sequence of numbers or characters in
a string (one after another) and determines their randomness, thus it can prove or disprove a null
hypothesis. These are observations from a random variable say X or can even combine results from two
variables X and Y
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Paper, Article
Paper
Research paper may refer to:
• Academic paper (also called scholarly paper), which is published in academic journals and contains
original research results or reviews existing results
• Term paper, written by college students
• Thesis or dissertation, a document submitted in support of a candidature for a degree or professional
qualification, presenting the author's research and findings
Academic Paper / Scholarly paper: In academic publishing, a paper is an academic work that is usually
published in an academic journal. It contains original research results or reviews existing results. Such a paper,
also called an article, will only be considered valid if it undergoes a process of peer review by one or more
referees (who are academics in the same field) who check that the content of the paper is suitable for
publication in the journal. A paper may undergo a series of reviews, editions and resubmissions before finally
being accepted or rejected for publication. This process typically takes several months. Next there is often a
delay of many months (or in some subjects, over a year) before an accepted manuscript appears.
Some journals, particularly newer ones, are now published in electronic form only. Paper journals are now
generally made available in electronic form as well, both to individual subscribers, and to libraries.
Term paper: A term paper' is a research paper written by students over an academic term. Term papers are
generally intended to describe an event, a concept, or argue a point. A term paper is a written original work
discussing a topic in detail, usually several typed pages in length and is often due at the end of a semester ∕
session. There is much overlap between the terms "research paper" and "term paper". The phrase "term
paper" was originally used to describe a paper (usually a research based paper) that was due at the end of the
"term" - either a semester or session, depending on which unit of measure a school used. Common usage has
"term paper" and "research paper" as interchangeable, but this is not completely accurate. Not all term papers
involve academic research, and not all research papers are term papers.
In the present day an entire industry has sprung up to provide plagiarized, pre-written, or custom written term
papers to students. Use of such papers is frowned upon by educators and administrators, and submission of
these works is considered plagiarism, and grounds for disciplinary action on the basis of academic dishonesty.
Thesis: A dissertation or thesis is a document submitted in support of candidature for an academic degree or
professional qualification presenting the author's research and findings. In some countries/ universities, the
word "thesis" or a cognate is used as part of a bachelor's or master's course, while "dissertation" is normally
applied to a doctorate, while in others, the reverse is true.
The word dissertation can at times be used to describe a treatise without relation to obtaining an academic
degree. The term thesis is also used to refer to the general claim of an essay or similar work.
Article
A research article reports the results of original research, assesses its contribution to the body of knowledge in
a given area, and is published in a peer-reviewed scholarly journal. A given academic field will likely have
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dozens of peer-reviewed journals. For university professors, publishing their research plays a key role in
determining whether they are granted tenure. Once, research articles had only a limited audience consisting
Quality of a research journal
Journals that are included on the ISI (Institute of Scientific Information) databases have been through a
rigorous selection process including peer review. ISI produces statistical analysis of journals, most widely used
is the journal impact factor although the immediacy index and citing half-life are also useful indicators. The
literature suggests the impact factor is just one of a suite of measures that should be used in conjunction with
"measures of esteem, performance, visibility and testimony of peers expert in relation to the activity that is
being analysed".
Impact factor
Impact factor, an index based on the frequency with which a journal's articles are cited in scientific
publications, is a putative marker of journal quality.
The impact factor of a journal reflects the frequency with which the journal's articles are cited in the scientific
literature. It is derived by dividing the number of citations in year 3 to any items published in the journal in
years 1 and 2 by the number of substantive articles published in that journal in years 1 and 2 . For instance, the
year 2002 impact factor for Journal X is calculated by dividing the total number of citations during the year
2002 to items appearing in Journal X during 2000 and 2001 by the number of articles published in Journal X in
2000 and 2001.
-----------------------------------------------------------------------------------------------------------------------------
A = Total citations in 2002 to items published in Journal X B = 2002 citations to items published in Journal X in
2000-01 (subset of A) C = Number of substantive articles published in Journal X in 2000-01
Impact factor = B/C
Example:
Assume that in 2002. There were 3,200 citations to items published in Journal X. Of these, 550 were citations to
items published in Journal X in 2000 and 2001.
During those two years. Journal X published 72 articles.
2002 Journal X impact factor = 550/72 = 7.64
-------------------------------------------------------------------------------------------------------------------------------
Impact factor is used by librarians in selecting journals for library collections, and, in some countries, it is used
to evaluate individual scientists and institutions for the purposes of academic promotion and funding
allocation.
The use of impact factor as an index of journal quality relies on the theory that citation frequency accurately
measures a journal's importance to its end users. This theory is plausible for journals whose audiences are
primarily researchers, most of whom write manuscripts for publication. By citing articles from a given journal
in their own manuscripts, researchers are in essence casting votes for that journal. Impact factor serves as a
tally of those votes.
The immediacy index
is calculated by dividing the number of citations to articles published in a given year by the number of articles
published in that year. It is useful in comparing how quickly journals are cited.
The citing half-life
is the number of publication years from the current year that account for 50% of
the current citations published by a journal in its article references. This helps evaluate the age of
the majority of articles referenced by a journal, while dramatic changes in the citing half-lifes over
time may indicate a change in a journal's format.
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