Describe different types of research
When a person has a strong will or keen nature to know about something he/she starts thinking :
- Why did this happen ?
- What will happen if it is not happened in this manner ?
- Is there any alternative that can be followed to replace? If yes, what is it ? If not, then why?
And to answer all these queries, all these why, how, what an individual starts to think about that particular thing (depending upon his/her area of interest) and this process continues till he/she comes out with some satisfactory result.
TYPES OF RESEARCH :
Types of research is a very broad concept because almost everything in this world can be a area of research depending upon one's own interest.
(ADDITION BY High school Dropout)
I pulled this from a study guide I made awhile back for research methods I. Perhaps it will be helpful, although I'm sure there are many more varieties (and comprehensive definitions) than just these. Actually, the whole study guide pertains to research, so here's a short version of the whole thing. Forgive me for any typos.
Research Methods Study Guide #1
Part I: Culture of Research and Basic Science Concepts
a. Basic Vs Applied Research - Applied research is targeted at specific practical questions, Basic research targets knowledge for knowledge's sake.
b. "Objective Measurement" - A way of obtaining data without bias. (objectivity = when a thing is the same whoever looks, which can be seen as nothing more than consensual subjectivity).
c. Inductive research - taking specific pieces of collected data to a general conclusion.
d. "Reviewing the Literature" - finding out about previous research pertinent to the endeavor. You do this to hopefully avoid repeating a prior experiment and wasting time.
e. Replication - Being able to reproduce the same results yourself or by other researchers.
f. Causal Relationships - a direct relationship between one event (cause) and another event (effect) which is the consequence (result) of the first.
g. Empirical Research - by direct observation.
h. Theories Vs Hypotheses - A theory is a broad and comprehensive explanatory framework that generates numerous hypotheses, whereas a hypothesis is a specific prediction typically derived from a theory.
i. Operational definition - a completely explicit description of the means and criteria used to measure the concept. Must give in all research articles.
j. Empirical questions - a question that can be answered through direct observation.
k. Deductive Research - having a general conclusion then looking for the data.
l. "Publish or Perish" - must submit research to the scrutiny of your scientific peers.
B. Concept Q's
a. What is the difference between basic and applied research. Which is most likely to produce breakthroughs in knowledge and theory? Basic research is more likely to produce breakthroughs. I imagine the reasoning behind this is that when you undertake research purely to gain information, there is a chance that you will encounter something completely new. Whereas within applied research the motive seems primarily to enhance the answers we have for specific practical questions. Applied research would seem geared toward efficiency, while basic research would seem geared toward pushing the envelope and encountering perhaps murky, but brand new ideas.
b. What distinguishes an empirical question from a non empirical question? An empirical question is one that is objective, and that can be answered through direct observation; while a non empirical question would be more subjective and conceptual.
c. What is an operational definition, and how is that distinct from a merely conceptual definition? An operation definition is a complete, thorough, and explicit description of the means and criteria used to measure the concept. The conceptual definition is the broader idea, and the operational definition is the replicable terms of how one has attempted to measure it.
d. What is it about an operational definition that makes observations "objectively quantifiable"?
By stating their operation definitions researchers make it possible for other researchers to use, criticize, or refine the measurement technique, or to compare results with other researchers who used different operational definitions to measure the same thing. Therefore, what makes observations objectively quantifiable is merely that the researcher has provided an operational definition that makes the measurement technique explicit, public, and subject to examination by the scientific community.
Why is quantification (turning observations into numbers or categories) necessary for operational definitions? Once quantified, the operational definition becomes a mathematical formula that anyone can attempt. Quantification translates an operational definition into the language of mathematics which is fairly universal.
Part II: Three Basic Types of Research and the Fundamentals of Experimental Design
a. Naturalistic Observation - Observing something within its natural environment without the subject being aware, or more importantly changing its behavior because of the observation.
b. Independent Variable - The variable manipulated by the experimenter.
c. Levels of an IV - The different groups within an experiment.
d. Individual Difference Variables - Organismic Variables.
e. Within Subject Experiments - recycles subjects (uses subjects in all/both conditions). Can be complicated by sequence, practice, and fatigue effects.
f. Between Subject Experiments - involves random assignment of subjects to conditions. Each subject is used for only one condition.
g. Correlations Study - A study in which the researchers measure the type and strength of relationships among variables that are not under the researchers control. Cannot prove causation.
h. Dependant Variable DV - Variable measured to see the effect of the IV.
i. Organismic Variable - The individual differences of people in a study.
j. External Validity - Typically derived from field based research; applicable to outside world but hard to prove cause and effect; generalizable.
k. Negative or Inverse Correlation - (-1.00) means that the two things never had in conjunction.
l. Reluctance - changes that come about by being watched.
m. Experiment - Researcher manipulates a variable and measures its effect on another variable (typically to prove cause and effect).
n. Extraneous Variable - the normality of uncontrollable difference (EV).
o. Internal Validity - Typically derived from lab experiments, looks to prove cause and effect, but hard to apply to real world.
q. Inter observer Reliability - assesses the degree to which different raters/observers give consistent estimates of the same phenomenon. (on a subjective thing like an essay, do teachers all give within 5 or so points?)
r. Participant Observer Technique - Using a researcher in the experiment as a "fly on the wall" to participate and observer from inside the experiment. (case studies)
s. Correlations Coefficient - Pearson's R. The measure of one things relation with another.
t. Confounding Variable - an EV with additional properties that are correlated with the IV.
u. Positive Correlation - +1.00 the things almost always happen in conjunction.
v. Hawthorne Effect - specific version of reluctance in which the IV doesn't cause the effect, but the awareness of change causes the effect and increases productivity.
w. Pearson's R - (Pearson's product-moment correlation) (expressed as r) between -1.00 and +1.00, the correlation coefficient.
x. Quasi-Experiment - wannabe experiments, typically wanting to prove cause and effect, but do not have control of critical variables that are needed for a true experiment.
Three methods of eliminating/reducing Confounding Variables:
i. Random Assignment - Randomizes EV's across conditions
ii. Hold EV's constant - involves making sure that some factors are the
same across conditions and groups.
iii. Manipulate EV's into IV's - makes a more complex but informative
Part III: Sampling Theory, Measurement Theory, and Statistical Significance
a. Population - A group of interest.
c. Random Sampling Vs Random Assignment - Random sampling is an issue of external validity, because if you don't randomly sample you cannot generalize your results to the rest of the world. Random assignment is an issue of internal validity; if you do not randomly assign your subjects to conditions you may create CV's that threaten the experiments internal validity and proof of causality.
d. Reliability - The operational definition must be free of excessive amounts of random measurement error.
e. Population Mean (or pop true score) - (pop x_) often hypothetical target
f. Statistical Significance - When the researcher can be at least 95% that the effect is real. Does not equal validity.
g. Sample - means of drawing, randomly or not, people from a given group into an experiment.
h. Representativeness - Does the sample represent the population?
i. Validity (in the context of measurement theory) - Does the operational definition measure what it is supposed to?
j. Stratified Random Sampling - randomly sampling from a specific group.
k. Sample Size - The higher the sample size the more likely the results are to be accurate. Must be at least 20 to be considered an accurate representation of any given group.
l. Sampling Error - sample mean minus population mean.