On 21st august 2019 our workshop commenced under the guidance of Dr. Sajan , Asst. professor N.S.S. Training College, Ottapalam. It was a five day workshop which was completed on 26th August. It was really a wonderful experience which enabled us to know many useful applications and websites which helped us to enhance our knowledge in the field of Information and Technology. It helps us to start a blog which we considered as our new step for this new world of technology so that we can share our notes and ideas to a waste receivers. I really thank our beloved Sir who was so curious to share his ideas and listened patiently to solve our individual problems that we face during our workshop. We really feel enriched so that we can update our knowledge in this world of technology. I thank God for giving us a chance to be a part of this wonderful institution.
Monday, August 26, 2019
WORKSHOP REPORT
On 21st august 2019 our workshop commenced under the guidance of Dr. Sajan , Asst. professor N.S.S. Training College, Ottapalam. It was a five day workshop which was completed on 26th August. It was really a wonderful experience which enabled us to know many useful applications and websites which helped us to enhance our knowledge in the field of Information and Technology. It helps us to start a blog which we considered as our new step for this new world of technology so that we can share our notes and ideas to a waste receivers. I really thank our beloved Sir who was so curious to share his ideas and listened patiently to solve our individual problems that we face during our workshop. We really feel enriched so that we can update our knowledge in this world of technology. I thank God for giving us a chance to be a part of this wonderful institution.
Sunday, August 25, 2019
DIFFERENT TYPES OF VARIABLES
Variables in Research
In scientific research,
scientists, technicians and researchers utilize a variety of methods and
variables when conducting their experiments. In simple terms, a variable
represents a measurable attribute that changes or varies across the experiment
whether comparing results between multiple groups, multiple people or even when
using a single person in an experiment conducted over time. In all, there are
six common variable types.
Variables represent the
measurable traits that can change over the course of a scientific experiment.
In all there are six basic variable types: dependent, independent, intervening,
moderator, controlled and extraneous variables.
Independent
and Dependent Variables
In general, experiments
purposefully change one variable, which is the independent variable. But a
variable that changes in direct response to the independent variable is the
dependent variable. Say there’s an experiment to test whether changing the
position of an ice cube affects its ability to melt. The change in an ice
cube's position represents the independent variable. The result of whether the
ice cube melts or not is the dependent variable.
Table 1
Different
names of independent and dependent variables
Independent
variable
|
Dependent
variable
|
|
Exposure
variable
|
Outcome
variable
|
|
Control
variable
|
Controlled
variable
|
|
Explanatory
variable
|
Explained
variable
|
|
Manipulated
variable
|
Response
variables
|
Intervening
and Moderator Variables
Intervening variables
link the independent and dependent variables, but as abstract processes, they
are not directly observable during the experiment. For example, if studying the
use of a specific teaching technique for its effectiveness, the technique
represents the independent variable, while the completion of the technique's
objectives by the study participants represents the dependent variable, while
the actual processes used internally by the students to learn the subject
matter represents the intervening variables.
By modifying the effect
of the intervening variables -- the unseen processes -- moderator variables
influence the relationship between the independent and dependent variables.
Researchers measure moderator variables and take them into consideration during
the experiment.
Constant
or Controllable Variable
Sometimes certain
characteristics of the objects under scrutiny are deliberately left unchanged.
These are known as constant or controlled variables. In the ice cube
experiment, one constant or controllable variable could be the size and shape
of the cube. By keeping the ice cubes' sizes and shapes the same, it's easier
to measure the differences between the cubes as they melt after shifting their
positions, as they all started out as the same size.
Extraneous
Variables
A well-designed experiment
eliminates as many unmeasured extraneous variables as possible. This makes it
easier to observe the relationship between the independent and dependent
variables. These extraneous variables, also known as unforeseen factors, can
affect the interpretation of experimental results. Lurking variables, as a
subset of extraneous variables represent the unforeseen factors in the
experiment.
Another type of lurking
variable includes the confounding variable, which can render the results of the
experiment useless or invalid. Sometimes a confounding variable could be a
variable not previously considered. Not being aware of the confounding
variable’s influence skews the experimental results. For example, say the
surface chosen to conduct the ice-cube experiment was on a salted road, but the
experimenters did not realize the salt was there and sprinkled unevenly,
causing some ice cubes to melt faster.
Figure1. Extraneous
variables
A
list of common and uncommon types of variables. A
“variable” in algebra really just means one thing—an unknown value. However, in
statistics, you’ll come across dozens of types of variables in statistics. In
most cases, the word still means that you’re dealing with something that’s
unknown, but—unlike in algebra—that unknown isn’t always a number. Some
variable types are used more than others. For example, you’ll be much more
likely to come across continuous variables than you would dummy variables.
Common Types of Variables
Categorical
variable: variables than can be put into categories.
For example, the category “Toothpaste Brands” might contain the variables
Colgate and Aqua fresh.
Confounding
variable: extra variables that have a hidden
effect on your experimental results.
Continuous
variable: a variable with infinite number of
values, like “time” or “weight”.
Control
variable: a factor in an experiment which must
be held constant. For example, in an experiment to determine whether light
makes plants grow faster, you would have to control for soil quality and water.
Dependent
variable: the outcome of an experiment. As you
change the independent variable, you watch what happens to the dependent
variable.
Discrete
variable: a variable that can only take on a
certain number of values. For example, “number of cars in a parking lot” is
discrete because a car park can only hold so many cars.
Independent
variable: A variable that is not affected by
anything that the researcher does.
Lurking
variable: A “hidden” variable the affects the
relationship between the independent and dependent variables..
Nominal
variable: another name for categorical variable.
Ordinal
variable: similar to a categorical variable, but
there is a clear order. For example, income levels of low, middle, and high
could be considered ordinal.
Qualitative
variable: a broad category for any variable that
can’t be counted (i.e. has no numerical value). Nominal and ordinal variables
fall under this umbrella term.
Quantitative
variable: A broad category that includes any
variable that can be counted, or has a numerical value associated with it.
Examples of variables that fall into this category include discrete variables
and ratio variables.
Ratio
variables: similar to interval variables, but has
a meaningful zero(‘Understanding
the different types of variable in statistics’, n.d.).
Less Common Types of Variables
Active
Variable: a variable that is manipulated by the
researcher.
Antecedent
Variable: a variable that comes before the
independent variable.
Attribute
variable: another name for a categorical
variable (in statistical software) or a variable that isn’t manipulated (in
design of experiments).
Binary
variable: a variable that can only take on two
values, usually 0/1. Could also be yes/no, tall/short or some other
two-variable combination.
Collider
Variable: a variable represented by a node on a
causal graph that has paths pointing in as well as out.
Covariate
variable: similar to an independent variable, it
has an effect on the dependent variable but is usually not the variable of
interest..
Criterion
variable: another name for a dependent variable,
when the variable is used in non-experimental situations.
Dichotomous
variable: Another name for a binary variable.
Dummy
Variables: used in regression analysis when you
want to assign relationships to unconnected categorical variables. For example,
if you had the categories “has dogs” and “owns a car” you might assign a 1 to
mean “has dogs” and 0 to mean “owns a car.”
Endogenous
variable: similar to dependent variables, they
are affected by other variables in the system. Used almost exclusively in
econometrics.
Exogenous
variable: variables that affect others in the
system.
Explanatory
Variable: a type of independent variable. When a
variable is independent, it is not affected at all by any other variables. When
a variable isn’t independent for certain, it’s an explanatory variable.
Identifier
Variables: variables used to uniquely identify
situations.
Indicator
variable: another name for a dummy variable.
Interval
variable: a meaningful measurement between two
variables. Also sometimes used as another name for a continuous variable.
Intervening
variable: a variable that is used to explain the
relationship between variables.
Latent
Variable: a hidden variable that can’t be
measured or observed directly.
Manifest
variable: a variable that can be directly
observed or measured.
Manipulated
variable: another name for independent variable.
Moderating
variable: changes the strength of an effect
between independent and dependent variables. For example, psychotherapy may
reduce stress levels for women more than men, so sex moderates the effect
between psychotherapy and stress levels.
Nuisance
Variable: an extraneous variable that increases
variability overall.
Observed
Variable: a measured variable (usually used in
SEM).
Outcome variable: similar in meaning to
a dependent variable, but used in a non-experimental study.
Polychotomous
variables: variables that can have more than two
values.
Predictor
variable: similar in meaning to the independent
variable, but used in regression and in non-experimental studies.
Responding
variable: an informal term for dependent variable
usually used in science fairs.
Scale
Variable: basically, another name for a
measurement variable.
Study
Variable (Research Variable): can mean any
variable used in a study, but does have a more formal definition when used in a
clinical trial.
Test
Variable: another name for the Dependent
Variable.
Treatment
variable: another name for independent variable(‘Types
of Variables in Statistics and Research’, n.d.).
Figure 2. Types of variables
Reference
Types of Variables in Statistics and Research. (n.d.). Retrieved 20
August 2019, from Statistics How To website:
https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/types-of-variables/
Understanding the different types of
variable in statistics. (n.d.). Retrieved 20 August 2019, from
https://statistics.laerd.com/statistical-guides/types-of-variable.php
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