Author:
Drew Roan

Previous
piece in this series:

A basic guide on what to look for in research papers - http://egafeminist.blogspot.co.uk/2016/04/egafem-analysis-part-1-basic-guide-on.html

A basic guide on what to look for in research papers - http://egafeminist.blogspot.co.uk/2016/04/egafem-analysis-part-1-basic-guide-on.html

As stated
in my previous piece, Egalitarian Feminism want to help people learn how to
read research papers, what to look for and from there how to interpret the
data.

Continuing
on with that theme, I have compiled a basic glossary of some frequently used terms
and their definitions to help people understand some of the language used. This
list is not exhaustive by any means, but hopefully people may find it useful.

**Aggregate:**

Definition:
A total created from multiple smaller units. To take an example, the population
of a country is the aggregate of all the cities, towns, villages, rural areas
and so forth.

**Attrition:**

Definition:
The rates at which participants drop out of a study over an extended period of
time. A study with a high attrition rate risks creating significant bias in the
results and potentially threatening the overall quality of the research.

**Bias:**

Definition:
Bias is a form of influence in a set of data which ends up producing lopsided
or misleading results. As a result of different biases, a set of data might be
over-representative or under-representative of the larger population. Bias can
come in many forms, such as positive & negative response rate bias in
samples, common method bias (see below), instruction bias (where instructions
as to what is wanted are unclear, researchers use their judgement to dictate
what they want. However people can respond to this differently depending on
their perception of the instructions) and more besides.

**Chi square:**

Definition:
A statistical test used to compare expected data with data that has been
collected, usually represented with an x

^{2}. A large difference between expected and collected results indicates that something may have caused the discrepancy. A suitably large difference allows researchers to reject the**(see further down).***null hypothesis***Coefficient:**

Definition:
The number or known factor (usually a constant) by which another value (usually
a variable) is multiplied. As an example, imagine you have a sample of workers
that is 10% of the total population of workers in an area. Having collected
your results from the sample (let’s say how many employees work in sales), you
wish to estimate how the larger population is likely to look if your data is
accurate. You would therefore multiply your variable (sales employees) by your
coefficient (10).

**Common Method Variance/Bias:**

Definition:
A term given to concerns raised by how the data is interpreted and supplied from
surveys. For example, a survey in which respondents use their own
interpretation of terms might receive very different response rates depending
on who is responding and how they interpret the questions being asked.

**Confidence Interval:**

Definition:
A term used to express the researchers’ level of uncertainty in their
estimates. A researcher who claims that their confidence level stands at 60% is
telling you that if you were to take the same sampling method but choose
different samples, you would expect the true population parameters to fall
within that estimate 60% of the time. The smaller the confidence interval, the
greater the uncertainty in the accuracy of the results.

**Control group:**

Definition:
In an experiment, the control group has data collected, but the findings from
their data is not included in the results. Its’ purpose is to show what would
normally happen in a given situation and to compare that data with what happens
when you alter an independent variable. This allows the researchers to
determine if altering a variable is having an effect on a test group, as well
as demonstrating what effect it has.

**Dependent variable:**

Definition:
A variable that can be influenced by another variable which researchers can
change. For example, consider two variables “employment” and “age”. Here,
“employment” is the dependent variable. It can be affected by the variable
“age”.

**Double blind experiment:**

Definition:
An experiment where both the researcher(s) and the participants are unaware of
which the control group is and which is the treatment group. This is often done
in psychology studies to further reduce potential bias created by the
participants and the researchers.

**Hypothesis:**

Definition:
A testable theory. For example, “My hypothesis is that if I water my plants
regularly and give them lots of sunshine, they will grow healthily”.

**Independent Variable:**

Definition:
The variable in an experiment that is manipulated by the researchers. It also
refers to a variable that is not affected by, but does affect, a dependent
variable. In the example given under “dependent variable”, “age” would be an
independent variable. “Employment” does not affect “age”, but “age” may have an
impact on “employment”.

**Meta-Analysis:**

Definition:
A term used to describe the method of combining and analysing data from
multiple studies on the same subject.

**Null hypothesis:**

Definition: This term represents the assumption that the variables of an experiment may have no effect on the results. In the example given for “hypothesis”, the null hypothesis would be that “regular water and lots of sunshine will not help plants grow more healthily”.

**P-Value:**

Definition:
P-Value refers to the idea that the results from a study may have been down to
chance, usually represented by a lower case

**(for probability). For example, if you see in a study***p***< 0.05, this tells you that there is an equal to or less than 1 in 20 chance that the results were down to luck. Most researchers assume that a***p.***value greater than 0.05 means the results were not statistically significant or are too prone to chance to be considered viable.***p*

**Parameter:**

Definition:
“Parameter” refers to a summary – usually a percentage or average – that
describes the entire population.

**Population:**

Definition:
“Population” refers to any large group of objects or individuals about which
information is desired, such as Germans, flowers or insects.

**Random Sampling:**

Definition:
A sampling technique where individuals from a population are picked at random.

**Regression Analysis:**

Definition:
A method of statistical analysis used to examine relationships between
variables. Perhaps the best way to think of this is to think of a scatter chart
(a chart where the data points are marked with little dots). The regression
analysis is represented by the central line drawn through the data to mark out
the average.

**Sampling error:**

Definition:
A term used to describe the level in which results from a sample are different
from results that is expected to be obtained from the larger population.

**Statistically significant:**

Definition:
A term used to explain that a difference in results did not occur by chance.

**Variable:**

Definition:
A characteristic or trait that varies between any group of objects or people.
Race, gender, age and education are all examples of variables.

**Weighted sample:**

Definition:
A correcting technique used to adjust responses given by survey respondents to
match the larger population. Typically this is done when certain demographics
(based on race, age, etc) are over or under-represented in a survey and the
researchers wish to have their results reflect the larger population.

__A word of caution: Correlation Vs Causation__
A trap many
people (and even some researchers) can fall into is to confuse correlation in
data with causation. Although a trend may exist, it does not mean that one
causes the other.

Correlation
can be understood as recognising when two or more variables show a tendency to fluctuate
together, but a change in one does not necessarily cause a change in another.

Causation
can be understood as recognising when two or more variables show a tendency to
fluctuate together and a change in one WILL cause a change in another.

As an
example, we could look at profits from ice cream sales and warm weather. As the
weather becomes warmer, we would expect to see companies sell more ice creams,
thus make more profit. The correlation in this example is that ice cream sales
increase with rising temperatures. The causation is that selling more ice cream
leads to a rise in profits.

__Ending Notes__
Hopefully
you have found the above piece useful and leaves you feeling more confident in
being able to read research papers for yourself.

In the near
future, I will begin tackling claims made by different people as well as
research pieces of interest. If there are any pieces you wish examined in more
detail, please leave a comment below or contact me on twitter (@DrewRoanEgaFem).

## No comments:

## Post a Comment