What is research design?

Explain features, objectives and methods used in different research designs,

Research design refers to the plan or strategy that a researcher uses to conduct a study or research project. It
outlines the specific methods and procedures that will be used to collect and analyze data, as well as the overall
structure of the study.

Research design can include many different components, such as the research question or hypothesis, the
selection of participants or subjects, the methods used to collect data (such as surveys, experiments, observations,
or interviews), the measures used to assess variables of interest, and the statistical techniques used to analyze the
data.

A good research design is essential for ensuring that a study is rigorous, valid, and reliable. It allows researchers to
control for potential confounding variables and to draw meaningful conclusions from their data.

Research design refers to the overall plan or strategy for conducting a research study. It outlines the methods and
procedures that will be used to gather and analyze data in order to answer research questions or test hypotheses.

Features of Research Design:

The research design should be structured and systematic in order to minimize bias and increase the reliability and
validity of the findings.
The research design should be flexible enough to allow for adjustments and modifications as needed.
The research design should be appropriate for the research question, the population being studied, and the
resources available.
The research design should be ethical, ensuring that the study does not cause harm to participants or violate their
rights.
Objectives of Research Design:

To identify the research problem or question and develop a plan to address it.
To specify the data collection methods and procedures that will be used to gather information. To establish
criteria for selecting participants or subjects and ensuring their informed consent. To determine the statistical
methods that will be used to analyze the data and test the hypotheses.
To outline the timeline and budget for the study. Methods used
in different research designs:

Experimental Design: This design involves manipulating an independent variable and measuring the effect on
a dependent variable while controlling for extraneous variables. The methods used in experimental design are
randomized control trials, pre-post-test design, post-test only design.

Quasi-Experimental Design: This design is similar to experimental design but lacks random assignment to
treatment groups. The methods used in quasi-experimental design are
non-equivalent control group design, time series design, regression discontinuity design.
Correlational Design: This design involves measuring the degree of relationship between two or more variables.
The methods used in correlational design are cross-sectional design, longitudinal design.

Descriptive Design: This design involves describing the characteristics of a population or phenomenon. The
methods used in descriptive design are surveys, observational studies, case studies.
Exploratory Design: This design is used when there is limited knowledge about a topic or phenomenon. The
methods used in exploratory design are focus groups, in-depth interviews, and observation.

In conclusion, research design is a crucial aspect of research methodology. The selection of an appropriate
research design should be based on the research question, the population being studied, and the resources
available. The features, objectives, and methods used in different research designs vary depending on the
research question and methodology being use
What is Secondary data? Explain various five sources of secondary data. Also explain advantages of
secondary data in today's business.

Secondary data is data that has been collected and published by someone else or for some other purpose, but is
used by researchers or businesses for their own purposes. It is also known as second-hand data or desk
research.
There are various sources of secondary data, including:
Government sources: The government collects a vast amount of data on demographics, economics, health,
education, and other topics that can be useful for researchers and businesses.
Industry sources: Industry associations and trade groups often collect and publish data related to their respective
industries, such as market size, trends, and competitive analysis.

Academic sources: Scholars and academic researchers conduct studies and publish research papers on various
topics that can be used as secondary data.

Media sources: Newspapers, magazines, and other media outlets often publish articles that contain data and
statistics that can be used as secondary data.

Online sources: The internet is a rich source of secondary data, including websites, blogs, forums, and social
media platforms that contain information on a wide range of topics.

Some advantages of using secondary data in today's business include:

Cost-effective: Secondary data is usually cheaper than collecting primary data through surveys or experiments.

Time-saving: Secondary data is readily available and can be obtained quickly, without the need for data collection
and analysis.

Reliable: Secondary data is often collected by reputable organizations or researchers, making it more reliable than
self-reported data.

Wide range of data: Secondary data sources cover a wide range of topics and industries, making it easier to find
data on specific research questions.

Comparison: Secondary data can be used for benchmarking and comparing trends over time or across different
markets, which can help businesses make more informed decisions.

ADVANTAGES OF SECONDARY DATA

1 Cost-Effective. Secondary research is often less expensive than primary research. ...
2 Wide Range of Information. In this online world, it is pretty easy to gather an extensive amount of
3 information. ...
4 Prevents Duplicate Information If Planning Additional Research. ...
Conclusion.
Explain probability sampling. What is non-probability sampling?
Probability sampling and non-probability sampling are two methods of selecting a sample from a population in
statistics. Probability sampling refers to the process of selecting a sample from a population in a way that every
individual in the population has an equal chance of being selected. Non-probability sampling refers to the
process of selecting a sample from a population in a way that not every individual in the population has an
equal chance of being selected.
Probability Sampling:
There are four types of probability sampling methods:
Simple random sampling: In this method, each member of the population has an equal chance of being
selected. The sample is selected randomly from the population.

Stratified random sampling: This method involves dividing the population into different groups, called strata,
and then selecting a random sample from each stratum. This ensures that the sample is representative of the
population.

Systematic random sampling: This method involves selecting every nth member of the population after
randomly selecting the first member. The value of n is determined by dividing the population size by the
sample size.

Cluster sampling: This method involves dividing the population into clusters, selecting a few clusters randomly,
and then selecting all the individuals within those clusters.

Explain any three non-probability sampling methods with suitable example

Non-probability sampling methods are statistical sampling techniques in which the selection of samples is not
based on randomization. Instead, the researcher selects the sample based on their judgment, convenience, or
availability. Here are three non-probability sampling methods with suitable examples:

1) Convenience Sampling:
Convenience sampling is a non-probability sampling method in which the researcher selects the sample based on
their convenience. The researcher selects individuals or groups that are readily available and easy to access.
Convenience sampling is commonly used in social science research where researchers often have limited
resources and time to conduct their research.

For example, a researcher is studying the eating habits of college students. The researcher decides to conduct
their study on a college campus and randomly selects students who are available in the cafeteria during lunchtime.
The researcher selects students who are easily accessible and are available at that particular time.

2) Purposive Sampling:
Purposive sampling is a non-probability sampling method in which the researcher selects individuals or groups who
are most likely to provide the required data. The researcher selects participants based on a specific purpose or
criterion. Purposive sampling is commonly used in qualitative research, where researchers aim to obtain in-depth
knowledge about a particular phenomenon.

For example, a researcher is studying the impact of social media on adolescent mental health. The researcher
selects participants who have reported experiencing anxiety or depression due to social media usage. The
researcher purposefully selects participants who fit the specific criterion and can provide valuable insights into the
research question.
3) Snowball Sampling:
Snowball sampling is a non-probability sampling method in which the researcher selects initial participants who
meet a specific criterion, and then those participants refer other potential participants. This sampling method is
commonly used in social science research when the population under study is difficult to reach or is relatively
small.
For example, a researcher is studying the experience of homeless people in a particular city. The researcher
selects initial participants who are homeless and then asks them to refer other homeless people they know to
participate in the study. The researcher then selects these referred participants to participate in the study. This
process continues until the researcher has obtained a sufficient number of participants.
 What is scaling? Describe the various scaling techniquesin Business Research?

Scaling in business research refers to the process of assigning numerical values to qualitative data in order to
measure and analyze it. Scaling is important in business research as it allows researchers to quantify and compare
data, making it easier to draw meaningful insights and conclusions.

There are four main types of scaling techniques in business research:

Nominal scaling: This is the simplest form of scaling, where data is assigned numerical values for identification
purposes only. Nominal scaling does not imply any order or hierarchy between the variables being measured. For
example, assigning the values 1, 2, and 3 to the colors red, green, and blue, respectively.

Ordinal scaling: This type of scaling assigns numerical values to variables in a way that reflects a natural order or
hierarchy between them. For example, assigning the values 1, 2, and 3 to small, medium, and large sizes,
respectively. However, ordinal scaling does not imply that the distance between each variable is equal.

Interval scaling: This type of scaling assigns numerical values to variables that have equal distances between
them, but no true zero point. For example, temperature measured in Celsius or Fahrenheit. The zero point in these
scales is arbitrary, and the difference between each value is constant, but the absence of temperature (i.e. zero
degrees) does not indicate the complete absence of heat.

Ratio scaling: This type of scaling assigns numerical values to variables that have equal distances between them
and a true zero point. For example, length or weight. In ratio scaling, the absence of the variable (i.e. zero length
or weight) indicates the complete absence of the attribute being measured.

Overall, scaling is a critical process in business research as it allows researchers to analyze and compare data in a
meaningful way. The choice of scaling technique used will depend on the nature of the data being collected and
the research question being asked.

LISt the stages in the Research process.

1.Identify the research problem or question: This involves identifying a research topic or question that you want
to investigate.
2.Conduct a literature review: This involves reviewing the existing research and literature on your chosen topic.
This will help you to identify gaps in the existing knowledge and to formulate your research questions.
3.Formulate a research hypothesis or research questions: Based on the research problem, you will need to
develop a hypothesis or a set of research questions that you will attempt to answer through your research.
4.Design the research methodology: This involves selecting the appropriate research design, data collection
methods, and data analysis techniques that will be used to answer your research questions.
5.Collect data: This involves gathering the necessary data through primary or secondary sources, using the
selected data collection methods.
6.Analyze the data: Once the data is collected, it is analyzed using the selected data analysis techniques.
7.Interpret the findings: This involves interpreting the results of the data analysis in the context of the research
question or hypothesis.
8.Draw conclusions: Based on the findings, you will draw conclusions about the research question or hypothesis.
Communicate the results: Finally, you will communicate the results of your research to others, typically through a
research report, presentation, or publication.
Under what circumstance would you recommend stratified and cluster probability sampling.
Stratified and cluster probability sampling are two common methods used to obtain representative samples from a
larger population. The choice between these two methods depends on the characteristics of the population being
studied and the research objectives.
Stratified sampling involves dividing the population into subgroups or strata based on one or more variables (such
as age, gender, income, education, etc.) and then randomly selecting participants from each stratum. This method
ensures that each stratum is proportionately represented in the sample and can be used to improve the precision of
the estimates for each stratum.
Cluster sampling involves dividing the population into clusters or groups based on geographic location or other
natural grouping factors, randomly selecting a few of these clusters, and then sampling all members of the selected
clusters. This method is useful when the population is large and widely dispersed, and it is not feasible to sample all
individuals or households. Cluster sampling can also be cost-effective and convenient.
Here are some circumstances under which stratified and cluster probability sampling would be recommended:
1.When the population being studied is large and geographically dispersed, cluster sampling can be more efficient
than simple random sampling. For example, in a survey of households in a country, it may be more practical to
divide the country into clusters of cities or provinces and randomly select a few clusters to survey rather than trying
to survey all households in the country.
2.When the population being studied has subgroups that vary significantly in size or characteristics, stratified
sampling can bemore effective than simple random sampling. For example, in a survey of voters, it may be
important to ensure that each age group is proportionally represented in the sample, as older voters may have
different voting patterns than younger voters.
3. When the research objective is to compare subgroups of the population based on different variables,
stratified sampling can help ensure that the sample size for each subgroup is large enough to draw meaningful
conclusions. For example, in a study of healthcare outcomes among patients with different chronic diseases,
stratifying the sample by disease type can help ensure that there are enough patients in each disease group to
draw meaningful conclusions.
In summary, stratified and cluster probability sampling are recommended when the population being studied is
large and diverse, or when there are subgroups that vary significantly in size or characteristics that need to be
adequately represented in the sample.

Mention two major differences between descriptive type of research and Exploratory type of research
1.Purpose: Descriptive research is conducted to describe a phenomenon or a group of phenomena, whereas
exploratory research is conducted to gain an understanding of a problem or to generate initial insights into a
topic.
2.Methodology: Descriptive research involves collecting and analyzing data that already exists, typically
through surveys, observations, or secondary sources. Exploratory research, on the other hand, may involve a
variety of methods, such as focus groups, interviews, or observations, to collect qualitative data that can be
used to generate new ideas or theories.

In summary, descriptive research is focused on describing existing phenomena, while exploratory research is
focused on understanding new phenomena or generating new ideas.
Descriptive research uses quantitative methods to collect and analyze data, while exploratory research uses
qualitative methods to explore new ideas or theories.
Explain in short about cross-sectional study design.
A cross-sectional study is a type of observational research that looks at a specific point in time to gather
information about a population or a subgroup of interest.

In a cross-sectional study, researchers collect data on a particular outcome or variable of interest from a
sample of individuals or groups at a single point in time. The data collected can include demographic
information, health behaviors, health status, and other relevant factors.

The primary advantage of cross-sectional studies is their efficiency and convenience in data collection, as
they can be conducted quickly and at a relatively low cost. However, cross- sectional studies are
limited in their ability to establish cause- and-effect relationships between variables, as they only provide a
snapshot of a population at a specific moment in time, and cannot account for changes or
developments that occur over time.
What do you understand about extraneous variables?Extraneous variables are variables that are not
of interest to a study but can have an effect on the outcome of the study. These variables are typically
uncontrolled or unmeasured, and their presence can obscure or distort the relationship between the
variables of interest.

In experimental research, extraneous variables can be controlled through randomization, manipulation, or

other experimental design techniques. However, in observational research or other non-experimental
designs, extraneous variables are more difficult to control for, and their impact on the outcome of the
study must be assessed and accounted for through statistical analysis or other methods.

Extraneous variables can also be referred to as confounding variables or third variables. These terms
are used interchangeably, and they all refer to variables that are not part of the main research question
but can affect the relationship between the variables of interest.
What are the scientific differences between nominal, ordinal, interval and ratio scale?
Nominal, ordinal, interval, and ratio scales are four different types of measurement scales used in scientific
research. The main
differences between these four scales are the level of measurement, the type of data that they represent, and
the mathematical operations that can be performed on the data.
1.Nominal Scale: A nominal scale is the simplest type of measurement scale. It is used to categorize data into
distinct groups or categories. Nominal data is qualitative in nature and cannot be measured numerically.
Examples of nominal data include gender, religion, and race. No mathematical operations can be performed on
nominal data.
2.Ordinal Scale: An ordinal scale is used to rank data in a specific order or sequence. Ordinal data is
qualitative in nature and can be measured numerically. However, the numerical values assigned to the data
only reflect the order or ranking of the data, not the magnitude of the differences between them. Examples of
ordinal data include rank in a competition, rating scales (e.g. Likert scale), and level of education. Basic
mathematical operations like addition and subtraction can be performed on ordinal data.
3.Interval Scale: An interval scale is used to measure data with equal intervals between the values. Interval
data is quantitative in nature and can be measured numerically. However, the numerical values assigned to the
data do not have a true zero point. Examples of interval data include temperature in Celsius and Fahrenheit
scales and calendar dates. Basic mathematical operations like addition, subtraction, and multiplication can be
performed on interval data.
Ratio Scale: A ratio scale is used to measure data with a true zero point, which means that a value of zero
indicates the complete absence of the variable being measured. Ratio data is quantitative in nature and can be
measured numerically. Examples of ratio data include height, weight, distance, and time. All mathematical
operations, including division, can be performed on ratio data
In summary, the main differences between these four scales are the level of measurement, the type of
data that they represent, and the mathematical operations that can be performed on the data. Nominal
data is qualitative and cannot be measured numerically, ordinal data can be measured numerically but
only reflects the order or ranking of the data, interval data has equal intervals but no true zero point, and
ratio data has a true zero point and all mathematical operations can be performed on it.
What is research hypothesis? What are the good qualities of a hypothesis? Give one example of a
hypothesis
A research hypothesis is a statement or prediction that suggests the possible relationship between variables,
which can be tested through research. It is an educated guess about what the researcher expects to find out or
observe in the research process.
A research hypothesis is a statement or prediction that suggests the possible relationship between variables,
which can be tested through research. It is an educated guess about what the researcher expects to find out or
observe in the research process.
1.Testable: A hypothesis must be falsifiable and testable through empirical data.
2.Specific: A hypothesis should be clear and specific in identifying the variables and their relationships.
3.Relevant: A hypothesis must be relevant to the research question or problem and based on existing literature
and knowledge.
4.Plausible: A hypothesis should be reasonable and logically consistent with existing theories and evidence.
5.Predictive: A hypothesis should have predictive value, i.e., it should be able to make predictions about future
observations or outcomes.
Example of a hypothesis: "Increased physical activity leads to a decrease in body weight among
adults." This hypothesis is specific, testable, relevant, plausible, and predictive. It suggests that there is
a relationship between two variables (physical activity and body weight) and that this relationship can be
tested through an experiment or study.
2 marks questions
Define sample.In statistics, a sample is a subset of a population. It is a group of individuals, items, or
events that are selected from a larger population for the purpose of studying and making inferences
about the population.

b) Define sampling frame.A sampling frame is a list or a set of items or individuals from which a sample
is drawn. It is a complete list of all the members of a population that is available for sampling

c) List the steps involved in the process of Business Research.1.Problem Identification: Define the
research problem and establish research objectives.
2.Research Design: Develop a research plan and select a research method.
3.Sampling: Identify the sampling technique and the sampling size.
4.Data Collection: Collect data from various sources, including primary and secondary data sources.
5.Data Analysis: Analyze and interpret the data collected.
6.Reporting: Report the research findings and draw conclusions.
7.Implementation: Implement the recommendations resulting from the research.

d) Write any two example of ukert scale

1.Likert scale: A scale that measures attitudes and opinions, typically with five or seven response options
ranging from strongly agree to strongly disagree.
2.Semantic differential scale: A scale that measures the connotative meaning of objects or concepts, using
pairs of adjectives such as good/bad, happy/sad, or weak/strong..
e) Define Hypothesis.A hypothesis is a statement or assumption about a population that is tested through
research. It is a tentative explanation for an observation, phenomenon, or scientific problem that can be
tested by further investigation
f) What are the type of descriptive Research?:-
Case study research: It involves an in-depth analysis of a particular case or situation.
Survey research: It involves collecting data from a sample of respondents using a structured questionnaire.

g) Define Research Design.:- Research design refers to the overall plan or strategy for conducting a
research study. It outlines the procedures and methods that will be used to collect and analyze data,
and it determines how the research results will be interpreted and reported. The research design is based on
the research objectives and the nature of the research problem. It includes details about the sampling
technique, data collection methods, data analysis methods, and the research instruments that will be
used.

Under what circumstance would you recommend qualitative and quantitative research. Explain with
suitable example.
 Both qualitative and quantitative research have their unique strengths and weaknesses, and the choice of
research method depends on the research question, the nature of the data, and the objectives of the study.
Here are some circumstances where I would recommend qualitative and quantitative research:
Qualitative research:When exploring complex phenomena: Qualitative research is useful when exploring
complex social phenomena that require an in-depth understanding of the context and the perspectives of the
participants. For example, a study on the experiences of cancer patients may involve conducting in-depth
interviews with
participants to gain a nuanced understanding of their emotional, social, and physical challenges.
2.When generating new ideas: Qualitative research is also useful when generating new ideas or hypotheses.
For example, a study exploring the reasons for declining voter turnout in a particular region may involve
conducting focus groups to understand the different perspectives and attitudes of the participants.
3.When studying small groups: Qualitative research is also ideal for studying small groups or communities that
may be difficult to access or understand using quantitative methods. For example, a study on the culture and
practices of a remote indigenous community may involve conducting participant observation and in-depth
interviews to gain a holistic understanding of their way of life.
Quantitative research:1.When testing hypotheses: Quantitative research is useful when testing specific
hypotheses or relationships between variables. For example, a study examining the relationship between
smoking and lung cancer may involve collecting data from a large sample of participants and using statistical
analyses to test the hypothesis.
2.When generalizing findings: Quantitative research is also useful when the aim is to generalize findings to a
larger population. For example, a study on the prevalence of mental health disorders in a particular country
may involve conducting a national survey to collect data from a representative sample of the population.
When studying trends over time: Quantitative research is ideal for studying trends over time, such as changes
in demographic patterns or shifts in public opinion. For example, a study on the changing attitudes towards
same-sex marriage in a particular country may involve analyzing survey data collected over several years to
understand the evolving trends
In summary, both qualitative and quantitative research have their unique strengths and weaknesses,
and the choice of research method depends on the research question, the nature of the data, and the
objectives of the study.
differentiate between Nominal & internal scale with example
Nominal scale and interval scale are two types of measurement scales in statistics. The main difference
between them lies in the nature of the data they measure and how they can be analyzed.
1. Nominal Scale: The nominal scale is a categorical scale where the data is sorted into distinct categories
without any particular order or ranking. In other words, it is a naming scale where the data is divided into
categories or groups based on certain characteristics, but the numbers assigned to these categories donot
imply any numerical order or value. For example, the colors of a rainbow can be considered as nominal data,
as each color is a distinct category without any inherent ranking. Other examples include gender (male,
female), religion (Christianity, Islam, Hinduism, etc.), or marital status (single, married, divorced).
2. Interval Scale: The interval scale is a numerical scale where the data is measured on a scale of equal
intervals, where the difference between each value is equal. In other words, it is a scale where the numbers
have a meaningful value and can be compared with each other. For example, the temperature scale in Celsius
or Fahrenheit can be considered as an interval scale because the difference between each value (1 degree) is
equal and meaningful. Another example is the IQ score, where the difference between an IQ score of 110 and
120 is the same as the difference between 90 and 100
In summary, the main difference between nominal and interval scales is that nominal data is categorical,
while interval data is numerical and has a meaningful value.Example: Suppose we are conducting a
survey of favorite colors among a group of people. The data we collect can be classified into two types
of scales:Nominal scale: The colors selected by each individual are a nominal scale. For instance, someone
may say their favorite color is red, while another person may say their favorite color is blue. These answers
cannot be ordered or ranked based on any inherent value Interval scale: If we asked people to rate their
preference for each color on a scale of 1-10, with 1 being the least preferred and 10 being the most preferred,
this would be an interval scale. In this case, numerical data that has a meaningful value and can be
compared with each other.