Selected Topics from Survival Statistics
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Basic Types of Research Designs
Defining a research problem provides a
format for further investigation. A
well-defined problem points to a method
of investigation. There is no one best
method of research for all situations.
Rather, there are a wide variety of
techniques for the researcher to choose
from. Often, the selection of a technique
involves a series of trade-offs. For
example, there is often a trade-off
between cost and the quality of
information obtained. Time constraints
sometimes force a trade-off with the
overall research design. Budget and time
constraints must always be considered as
part of the design process.
There are three basic methods of
research: 1) survey, 2) observation, and
3) experiment. Each method has its
advantages and disadvantages.
The survey is the most common method of gathering information in the social sciences. It can be a face-to-face interview, telephone, mail, or internet survey. A personal interview is one of the best methods obtaining personal, detailed, or in-depth information. It usually involves a lengthy questionnaire that the interviewer fills out while asking questions. It allows for extensive probing by the interviewer and gives respondents the ability to elaborate their answers. Telephone interviews are similar to face-to-face interviews. They are more efficient in terms of time and cost, however, they are limited in the amount of in-depth probing that can be accomplished, and the amount of time that can be allocated to the interview. Mail surveys and internet surveys are generally the most cost effective interview methods. The researcher can obtain opinions, but trying to meaningfully probe opinions is very difficult.
Observation research monitors
respondents' actions without directly
interacting with them. It has been used
for many years by A.C. Nielsen to monitor
television viewing habits. Focus groups
often use one-way mirrors to study
behavior. Anthropologists and social
scientists often study societal and group
behaviors by simply observing them. The
fastest growing form of observation
research has been made possible by the
bar code scanners at cash registers,
where purchasing habits of consumers can
now be automatically monitored and
summarized.
In an experiment, the
investigator changes one or more
variables over the course of the
research. When all other variables are
held constant (except the one being
manipulated), changes in the dependent
variable can be explained by the change
in the independent variable. It is
usually very difficult to control all the
variables in the environment. Therefore,
experiments are generally restricted to
laboratory models where the investigator
has more control over all the variables.
Goal Definition
Defining the goals and objectives of a
research project is one of the most
important steps in the research process.
Do not underestimate the importance of
this step. Clearly stated goals keep a
research project focused. The process of
goal definition usually begins by writing
down the broad and general goals of the
study. As the process continues, the
goals become more clearly defined and the
research issues are narrowed.
Research Questions, Hypotheses, and Null Hypotheses
The goals of the study are easily
transformed into research questions.
There are basically two kinds of research
questions: testable and non-testable.
Neither is better than the other, and
both have a place in business research.
Examples of non-testable questions
are:
What are managers attitudes towards
the revised advertising budget?
What do customers feel is fair
price range for the new product?
What do residents feel are the most
important problems facing the community?
Respondents' answers to these
questions could be summarized in
descriptive tables and the results might
be extremely valuable to administrators
and planners. Business and social science
researchers often ask non-testable
research questions. The shortcoming with
these types of questions is that they do
not provide objective cut-off points for
decision-makers.
Business research usually seeks to
answer one or more testable research
questions. Nearly all testable research
questions begin with one of the following
two phrases:
Is there a significant difference
between ...?
Is there a significant relationship
between ...?
For example:
Is there a significant relationship
between the corporate level of managers
and their attitudes towards the revised
advertising budget?
Is there a significant relationship
between perceived need for the new
product and the price that customers
would be willing to pay for it?
Is there a significant difference
between white and minority residents with
respect to what they feel are the most
important problems facing the community?
A research hypothesis is a testable
statement of opinion. It is created from
the research question by replacing the
words "Is there" with
the words "There is",
and also replacing the question mark with
a period. The hypotheses for the three
sample research questions would be:
There is a significant relationship
between the corporate level of managers
and their attitudes towards the revised
advertising budget.
There is a significant relationship
between perceived need for the new
product and the price that customers
would be willing to pay for it.
There is a significant difference
between white and minority residents with
respect to what they feel are the most
important problems facing the community.
It is not possible to test a
hypothesis directly. Instead, you must
turn the hypothesis into a null
hypothesis. The null hypothesis is
created from the hypothesis by adding the
words "no" or "not"
to the statement. For example, the null
hypotheses for the three examples would be:
There is no significant
relationship between the corporate level
of managers and their attitudes towards
the revised advertising budget.
There is no significant
relationship between perceived need for
the new product and the price that
customers would be willing to pay for it.
There is no significant difference
between white and minority residents with
respect to what they feel are the most
important problems facing the community.
All statistical testing is done on the
null hypothesis...never the hypothesis.
The result of a statistical test will
enable you to either 1) reject the null
hypothesis, or 2) fail to reject the null
hypothesis. Never use the words
"accept the null hypothesis".
When you say that you "reject the
null hypothesis", it means that you
are reasonably certain that the null
hypothesis is wrong. When you say that
you "fail to reject the null
hypothesis", it means that you do
not have enough evidence to claim that
the null hypothesis is wrong.
Validity and Reliability
Validity refers to the accuracy
or truthfulness of a measurement. Are we
measuring what we think we are? This is a
simple concept, but in reality, it is
extremely difficult to determine if a
measure is valid. Generally, validity is
based solely on the judgment of the
researcher. When an instrument is
developed, each question is scrutinized
and modified until the researcher is
satisfied that it is an accurate measure
of the desired construct, and that there
is adequate coverage of each area to be
investigated.
Reliability is synonymous with
repeatability. A measurement that yields
consistent results over time is said to
be reliable. When a measurement is prone
to random error, it lacks reliability.
The reliability of an instrument places
an upper limit on its validity. A
measurement that lacks reliability will
also lack validity. There are three basic
methods to test reliability: test-retest,
equivalent form, and internal
consistency.
A test-retest measure of
reliability can be obtained by
administering the same instrument to the
same group of people at two different
points in time. The degree to which both
administrations are in agreement is a
measure of the reliability of the
instrument. This technique for assessing
reliability suffers two possible
drawbacks. First, a person may have
changed between the first and second
measurement. Second, the initial
administration of an instrument might in
itself induce a person to answer
differently on the second administration.
The second method of determining
reliability is called the equivalent-form
technique. The researcher creates two
different instruments designed to measure
identical constructs. The degree of
correlation between the instruments is a
measure of equivalent-form reliability.
The difficulty in using this method is
that it may be very difficult (and/or
prohibitively expensive) to create a
totally equivalent instrument.
The most popular methods of estimating
reliability use measures of internal
consistency. When an instrument
includes a series of questions designed
to examine the same construct, the
questions can be arbitrarily split into
two groups. The correlation between the
two subsets of questions is called the split-half
reliability. The problem is that this
measure of reliability changes depending
on how the questions are split. A better
statistic, known as Cronbach's alpha, is
based on the mean (absolute value)
interitem correlation for all possible
variable pairs. It provides a
conservative estimate of reliability, and
generally represents the lower bound to
the reliability of a scale of items. For
dichotomous nominal data, the KR-20
(Kuder-Richardson) is used instead of
Cronbach's alpha.
Variability and Error
Most research is an attempt to
understand and explain variability.
Variability refers to the dispersion of
scores. If every respondent gives the
same answer to an item, there is no
variability, and when a measurement lacks
variability, no statistical tests can be
(or need be) performed. If there is great
diversity in respondents' answers, then
we say that there is high variability.
Ideally, when a researcher finds
differences between respondents, they are
due to true difference on the variable
being measured. However, the combination
of systematic and random errors can
dilute the accuracy of a measurement. Systematic
error is introduced through a
constant bias in a measurement. It can
usually be traced to a fault in the
sampling procedure or in the design of a
questionnaire. Random error does
not occur in any consistent pattern, and
it is not controllable by the researcher.
How to Order Survival Statistics
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