Tag: Advanced Accounting B.com Notes

Structured questions take many forms and include:

• Single response with nominal or ordinal categories (e.g. From the following list please select the category which includes your household income)
• Multiple responses (e.g. From the following list of pizza toppings please any or all that you regularly use)
• Scaled questions (e.g. The President is doing a good job: Strongly Agree to Strongly Disagree), and
• Numerous variations on these primary types.

Unstructured questions are a bit more qualitative in feel. They do not require pre-defined categories and they allow the respondent to express their views openly. This is their blessing and their curse. Open-ended questions, as they are also known, produce a higher cognitive load in the sense that the respondent has to think harder to come to an answer. This can create a lower response rate and sometimes lesser quality data. On the other hand, they can produce rich insights that provide depth and color to the black and white of structured questions.

Open-ended questions require additional time on the part of the researcher to analyze and code the responses although text-mining software is making this easier. For best results on a survey, keep open-ended questions to a minimum and use them as sub-questions driven by critical responses to a structured question. For example, if someone selects a high or low response to the Net Promoter Score, you can follow up with unstructured questions asking the respondent to elaborate on their score.

Open-ended questions

Open-ended questions are exploratory in nature, and offer the researchers rich, qualitative data. In essence, they provide the researcher with an opportunity to gain insight on all the opinions on a topic they are not familiar with. However, being qualitative in nature makes these types of questions lack the statistical significance needed for conclusive research.

Nevertheless, open-ended questions are incredibly useful in several different ways:

• Expert interviews
• Small population studies
• Preliminary research
• A respondent outlet

1. Expert interviews

Since questions that are open-ended ask for the critical thinking and uncut opinion of the respondent, they are perfect for gaining information from specialists in a field that the researcher is less qualified in. Example: If I wanted to learn the history of Ancient China (something I know very little about), I could create my survey for a selected group of historians whose focus is Ancient China. My survey would then be filled with broad open-ended questions that are designed to receive large amounts of content and provide the freedom for the expert to demonstrate their knowledge.

2. Small population studies

Open-ended questions can be useful for surveys that are targeting a small group of people because there is no need for complex statistical analysis and the qualitative nature of the questions will give you more valuable input from each respondent. The rule here is the group must be small enough for the surveyor to be able to read each unique response and reflect on the information provided. Example: A supervisor who is looking for performance feedback from his/her team of six employees. The supervisor would benefit more from questions that allow the respondents to freely answer rather than forcing them into closed-ended questions that will limit their responses.

3. Preliminary research

As stated in the closed-ended questions section, conclusive research usually requires preliminary research to be conducted in order to design the appropriate research objects, survey structure and questions. Open-ended questions can reveal to the surveyor a variety of opinions and behaviours among the population that they never realized. It is therefore, incredibly useful to use open-ended questions to gain information for further quantitative research.

4. A respondent outlet

It is usually a good idea in any survey, no matter how large, to leave an open-ended comments question at the end. This is especially in the case of a survey asking closed-ended questions on attitudes, opinions, or behaviours. Forcing respondents to answer closed-ended questions asks them to fit in your box of options and can leave them with extra information or concerns that they want to share with you. Providing respondents with the outlet of a comment box is showing them the respect they deserve for taking the time to fill out your survey.

There are a few drawbacks to open-ended questions as well. Though respondent answers are almost always richer in quality, the amount of effort it takes to digest the information provided can sometimes be overwhelming. That is why open-ended questions work best in studies with smaller populations. Furthermore, if your survey sample is a fraction of the population you are studying, you will be looking to find data which can be inferred on the overall population as statistically significant. Unfortunately, open-ended questions cannot be used in this manner, as each response should be seen as a unique opinion.

Closed-ended questions

Closed-ended questions come in a multitude of forms, including: multiple choice, drop down, checkboxes, and ranking questions. Each question type doesn’t allow the respondent to provide unique or unanticipated answers, but rather, choose from a list of pre-selected options. It’s like being offered spaghetti or hamburgers for dinner, instead of being asked “What would you like for dinner?”

Use closed-ended questions for the following:

• When your audience isn’t particularly interested in your survey topic
• When you need quantifiable data
• To categorize respondents

1. When your audience isn’t particularly interested in your survey topic

Closed-ended questions are easier to complete than open-ended questions. Why? Because closed-ended questions lay out all of the possible answers, removing respondents’ task of coming up with their own responses.

So, when you find yourself surveying an audience who may not be excited about what you’re asking them, air on the side of using closed-ended questions. It’ll give them an easier survey-taking experience and, in the process, provide you with a higher completion rate.

2. When you need quantifiable data

If you’re looking for statistically significant stats, closed-ended questions are the way to go. Going back to our earlier example, using a closed-ended question can help us arrive at stats like: 70% of respondents want to eat spaghetti for dinner versus 30% who prefer hamburgers.

Questions that are closed-ended are conclusive in nature as they are designed to create data that is easily quantifiable. The fact that questions of this type are easy to code makes them particularly useful when trying to prove the statistical significance of a survey’s results. Furthermore, the information gained by closed-ended questions allows researchers to categorize respondents into groups based on the options they have selected.

3. To categorize respondents

In other words, they allow you to conduct demographic studies. Why is this valuable?

Imagine that the manager of a designer clothing store believes that certain types of people are more likely to visit their store and purchase their clothing than others. To decipher which segment groups are most likely to be their customers, the manager could design a survey for anyone who has been a visitor. This survey could include closed-ended questions on gender, age, employment status, and any other demographic information they’d like to know. Then, it would be followed by questions on how often they visit the store and the amount of money they spend annually. Since all the questions are closed-ended, the store manager could easily quantify the responses and determine the profile of their typical customer. In this case, the manager may learn that her most frequent customers are female students, ages 18-25. This knowledge would allow her to move forward with an action plan on how to cater to this niche better or break into other target demographics.

The major drawback to closed-ended questions is that a researcher must already have a clear understanding of the topic of his/her questions and how they tie into the overall research problem before they are created. Without this, closed-ended questions will lead to insufficient options for respondents to select from, questions that do not properly reflect the research’s purpose, and limited or erroneous information.

For example, if I asked the question, “do you get to work by driving, busing, or walking?” I would have accidentally omitted carpooling, biking, cartwheeling or any other form of transportation I am unaware of. Instead, it would have been better for me to ask the open-ended question of “how do you get to work?” to learn all the different types of answer before forcing the selection based on a list of several options.

Types of question: Structured/Close-end, Unstructured/open-end

Structured questions take many forms and include:

• Single response with nominal or ordinal categories (e.g. From the following list please select the category which includes your household income)
• Multiple responses (e.g. From the following list of pizza toppings please any or all that you regularly use)
• Scaled questions (e.g. The President is doing a good job: Strongly Agree to Strongly Disagree), and
• Numerous variations on these primary types.

Unstructured questions are a bit more qualitative in feel. They do not require pre-defined categories and they allow the respondent to express their views openly. This is their blessing and their curse. Open-ended questions, as they are also known, produce a higher cognitive load in the sense that the respondent has to think harder to come to an answer. This can create a lower response rate and sometimes lesser quality data. On the other hand, they can produce rich insights that provide depth and color to the black and white of structured questions.

Open-ended questions require additional time on the part of the researcher to analyze and code the responses although text-mining software is making this easier. For best results on a survey, keep open-ended questions to a minimum and use them as sub-questions driven by critical responses to a structured question. For example, if someone selects a high or low response to the Net Promoter Score, you can follow up with unstructured questions asking the respondent to elaborate on their score.

Open-ended questions

Open-ended questions are exploratory in nature, and offer the researchers rich, qualitative data. In essence, they provide the researcher with an opportunity to gain insight on all the opinions on a topic they are not familiar with. However, being qualitative in nature makes these types of questions lack the statistical significance needed for conclusive research.

Nevertheless, open-ended questions are incredibly useful in several different ways:

• Expert interviews
• Small population studies
• Preliminary research
• A respondent outlet

1. Expert interviews

Since questions that are open-ended ask for the critical thinking and uncut opinion of the respondent, they are perfect for gaining information from specialists in a field that the researcher is less qualified in. Example: If I wanted to learn the history of Ancient China (something I know very little about), I could create my survey for a selected group of historians whose focus is Ancient China. My survey would then be filled with broad open-ended questions that are designed to receive large amounts of content and provide the freedom for the expert to demonstrate their knowledge.

2. Small population studies

Open-ended questions can be useful for surveys that are targeting a small group of people because there is no need for complex statistical analysis and the qualitative nature of the questions will give you more valuable input from each respondent. The rule here is the group must be small enough for the surveyor to be able to read each unique response and reflect on the information provided. Example: A supervisor who is looking for performance feedback from his/her team of six employees. The supervisor would benefit more from questions that allow the respondents to freely answer rather than forcing them into closed-ended questions that will limit their responses.

3. Preliminary research

As stated in the closed-ended questions section, conclusive research usually requires preliminary research to be conducted in order to design the appropriate research objects, survey structure and questions. Open-ended questions can reveal to the surveyor a variety of opinions and behaviours among the population that they never realized. It is therefore, incredibly useful to use open-ended questions to gain information for further quantitative research.

4. A respondent outlet

It is usually a good idea in any survey, no matter how large, to leave an open-ended comments question at the end. This is especially in the case of a survey asking closed-ended questions on attitudes, opinions, or behaviours. Forcing respondents to answer closed-ended questions asks them to fit in your box of options and can leave them with extra information or concerns that they want to share with you. Providing respondents with the outlet of a comment box is showing them the respect they deserve for taking the time to fill out your survey.

There are a few drawbacks to open-ended questions as well. Though respondent answers are almost always richer in quality, the amount of effort it takes to digest the information provided can sometimes be overwhelming. That is why open-ended questions work best in studies with smaller populations. Furthermore, if your survey sample is a fraction of the population you are studying, you will be looking to find data which can be inferred on the overall population as statistically significant. Unfortunately, open-ended questions cannot be used in this manner, as each response should be seen as a unique opinion.

Closed-ended questions

Closed-ended questions come in a multitude of forms, including: multiple choice, drop down, checkboxes, and ranking questions. Each question type doesn’t allow the respondent to provide unique or unanticipated answers, but rather, choose from a list of pre-selected options. It’s like being offered spaghetti or hamburgers for dinner, instead of being asked “What would you like for dinner?”

Use closed-ended questions for the following:

• When your audience isn’t particularly interested in your survey topic
• When you need quantifiable data
• To categorize respondents

1. When your audience isn’t particularly interested in your survey topic

Closed-ended questions are easier to complete than open-ended questions. Why? Because closed-ended questions lay out all of the possible answers, removing respondents’ task of coming up with their own responses.

So, when you find yourself surveying an audience who may not be excited about what you’re asking them, air on the side of using closed-ended questions. It’ll give them an easier survey-taking experience and, in the process, provide you with a higher completion rate.

2. When you need quantifiable data

If you’re looking for statistically significant stats, closed-ended questions are the way to go. Going back to our earlier example, using a closed-ended question can help us arrive at stats like: 70% of respondents want to eat spaghetti for dinner versus 30% who prefer hamburgers.

Questions that are closed-ended are conclusive in nature as they are designed to create data that is easily quantifiable. The fact that questions of this type are easy to code makes them particularly useful when trying to prove the statistical significance of a survey’s results. Furthermore, the information gained by closed-ended questions allows researchers to categorize respondents into groups based on the options they have selected.

3. To categorize respondents

In other words, they allow you to conduct demographic studies. Why is this valuable?

Imagine that the manager of a designer clothing store believes that certain types of people are more likely to visit their store and purchase their clothing than others. To decipher which segment groups are most likely to be their customers, the manager could design a survey for anyone who has been a visitor. This survey could include closed-ended questions on gender, age, employment status, and any other demographic information they’d like to know. Then, it would be followed by questions on how often they visit the store and the amount of money they spend annually. Since all the questions are closed-ended, the store manager could easily quantify the responses and determine the profile of their typical customer. In this case, the manager may learn that her most frequent customers are female students, ages 18-25. This knowledge would allow her to move forward with an action plan on how to cater to this niche better or break into other target demographics.

The major drawback to closed-ended questions is that a researcher must already have a clear understanding of the topic of his/her questions and how they tie into the overall research problem before they are created. Without this, closed-ended questions will lead to insufficient options for respondents to select from, questions that do not properly reflect the research’s purpose, and limited or erroneous information.

For example, if I asked the question, “do you get to work by driving, busing, or walking?” I would have accidentally omitted carpooling, biking, cartwheeling or any other form of transportation I am unaware of. Instead, it would have been better for me to ask the open-ended question of “how do you get to work?” to learn all the different types of answer before forcing the selection based on a list of several options.

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of effect.

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example, If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, reduces the risk of many diseases such as heart disease, high blood pressure and some cancers.

Null Hypothesis

It provides the statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “HO”.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, causal hypothesis proposes a cause and effect interaction between two or more variables.

Sources of Hypothesis

• The resemblance between the phenomenon.
• Observations from past studies, present-day experiences and from the competitors.
• Scientific theories.
• General patterns that influence the thinking process of people.

1. General Culture in which a Science Develops:

A cultural pattern influences the thinking process of the people and the hypothesis may be formulated to test one or more of these ideas. Cultural values serve to direct research interests. The function of culture has been responsible for developing today’s science to a great dimension. In the words of Goode and Hatt, “to say that the hypotheses are the product of the cultural values does not make them scientifically less important than others, but it does at least indicate that attention has been called to them by the culture itself.

For example, in the Western society race is thought to be an important determinant of human behaviour. Such a proposition can be used to formulate a hypothesis. We may also cite metaphysical bias and metaphysical ideas of Indian culture to have been responsible for the formulation of certain types of hypotheses. It implies that cultural elements of common cultural pattern may form a source of the formulation of hypotheses.

2. Scientific Theory:

A major source of hypothesis is theory. A theory binds a large body of facts by positing a consistent and lawful relationship among a set of general concepts representing those facts. Further generalizations are formed on the basis of the knowledge of theory. Corollaries are drawn from the theories.

These generalizations or corollaries constitute a part of hypothesis. Since theories deal with abstractions which cannot be directly observed and can only remain in the thought process, a scientific hypothesis which is concerned with observable facts and observable relationship between facts can only be used for the purpose of selecting some of the facts as concrete instances of the concepts and for making a tentative statement about the existence of a relation among the selected facts with the purpose of subjecting the relation to an empirical test.”

A hypothesis emerges as a deduction from theory. Hence, hypotheses become “working instruments of theory” Every worthwhile theory provides for the formulation of additional hypothesis. “The hypothesis is the backbone of all scientific theory construction; without it, confirmation or rejection of theories would be impossible.”

The hypotheses when tested are “either proved or disproved and in turn constitute further tests of the original theory.” Thus the hypothetical type of verbal proposition forms the link between the empirical propositions or facts and the theories. The validity of a theory can be examined only by means of scientific predictions or experimental hypothesis.

3. Analogies:

Observation of a similarity between two phenomena may be a source of formation of a hypothesis aimed at testing similarity in any other respect. Julian Huxley has pointed out that “casual observation in nature or in the framework of another science may be a fertile source of hypothesis. The success of a system in one discipline can be used in other discipline also. The theory of ecology is based on the observation of certain plants in certain geographical conditions. As such, it remains in the domain of Botany. On the basis of that the hypothesis of human ecology could be conceived.

Hypothesis of social physics is also based on analogy. “When the hypothesis was born out by social observation, the same term was taken into sociology. It has become an important idea in sociological theory”. Although analogy is not always considered, at the time of formulation of hypothesis; it is generally satisfactory when it has some structural analogies to other well established theories. For the systematic simplicity of our knowledge, the analogy of a hypothesis becomes inversely helpful. Formulation of an analogous hypothesis is construed as an achievement because by doing so its interpretation is made easy.

4. Consequences of Personal, Idiosyncratic Experience as the Sources of Hypothesis:

Not only culture, scientific theory and analogies provide the sources of hypothesis, but also the way in which the individual reacts to each of these is also a factor in the statement of hypotheses. Certain facts are present, but every one of us is not able to observe them and formulate a hypothesis.

Referring to Fleming’s discovery of penicillin, Backrach has maintained that such discovery is possible only when the scientist is prepared to be impressed by the ‘unusual’. An unusual event struck Fleming when he noted that the dish containing bacteria had a green mould and the bacteria were dead. Usually he would have washed the dish and have attempted once again to culture the bacteria.

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But normally, he was moved to bring the live bacteria in close contact with the green mould, resulting in the discovery of penicillin. The example of Sir Issac Newton, the discoverer of the theory of Gravitation, is another glaring example of this type of ‘personal experience’. Although prior to Newton’s observation, several persons had witnessed the falling of the apple, he was the right man to formulate the theory of gravitation on the basis of this phenomenon.

Thus, emergence of a hypothesis is a creative manner. To quote Mc Guigan, “to formulate a useful and valuable hypothesis, a scientist needs first sufficient experience in that area, and second the quality of the genius.” In the field of social sciences, an illustration of individual perspective may be visualized in Veblen’s work. Thorstein Veblen’s own community background was replete with negative experiences concerning the functioning of economy and he was a ‘marginal man’, capable of looking at the capitalist system objectively.

Thus, he could be able to attack the fundamental concepts and postulates of classical economics and in real terms Veblen could experience differently to bear upon the economic world, resulting in the making of a penetrating analysis of our society. Such an excellent contribution of Veblen has, no doubt, influenced social science since those days.

Applied research is a methodology used to solve a specific, practical issue affecting an individual or group. This scientific method of study and research is used in business, medicine, and education in order to find solutions that may improve health, solve scientific problems or develop new technology. Examples of applied research topics will show you how this method can be used to address everyday problems.

Characteristics of Applied Research in Education

• It clearly highlights generalizations and hypotheses that inform the research findings.
• It relies on empirical evidence.
• It is set at providing solutions to a defined problem.
• It requires accurate observation and description.

Examples of Applied Research

The following are examples for applied research. You can notice that each of these studies aim to resolve a specific and an immediate problem.

• A study into the ways of improving the levels of customer retention for D-Mart in India.
• An investigation into the ways of improving employee motivation in Taj Hotel, Mumbai
• Development of strategies to introduce change in Starbucks global supply-chain management with the view on cost reduction
• A study into the ways of fostering creative deviance amongst employees without compromising respect for authority.

Types of Applied Research

There are 3 types of applied research. These are evaluation research, research and development, and action research.

• Evaluation Research

Evaluation research is a type of applied research that analyses existing information about a research subject to arrive at objective research outcomes or reach informed decisions. This type of applied research is mostly applied in business contexts, for example, an organisation may adopt evaluation research to determine how to cut down overhead costs.

• Research and Development

Research and development is a type of applied research that is focused on developing new products and services based on the needs of target markets. It focuses on gathering information about marketing needs and finding ways to improve on an existing product or create new products that satisfy the identified needs.

• Action Research

Action research is a type of applied research that is set on providing practical solutions to specific business problems by pointing the business in the right directions. Typically, action research is a process of reflective inquiry that is limited to specific contexts and situational in nature.

Advantages and Disadvantages of Applied Research

The advantages and disadvantages of applied and fundamental research mirror and contrast each other. On the positive side, applied research can be helpful in solving specific problems in business and other settings.

On the negative side, findings of applied research cannot be usually generalized. In other words, applicability of the new knowledge generated as a result of applied research is limited to the research problem. Moreover, applied studies usually have tight deadlines which are not flexible.

Empirical research is research using empirical evidence. It is also a way of gaining knowledge by means of direct and indirect observation or experience. Empiricism values some research more than other kinds. Empirical evidence (the record of one’s direct observations or experiences) can be analyzed quantitatively or qualitatively. Quantifying the evidence or making sense of it in qualitative form, a researcher can answer empirical questions, which should be clearly defined and answerable with the evidence collected (usually called data). Research design varies by field and by the question being investigated. Many researchers combine qualitative and quantitative forms of analysis to better answer questions which cannot be studied in laboratory settings, particularly in the social sciences and in education.

In some fields, quantitative research may begin with a research question (e.g., “Does listening to vocal music during the learning of a word list have an effect on later memory for these words?”) which is tested through experimentation. Usually, the researcher has a certain theory regarding the topic under investigation. Based on this theory, statements or hypotheses will be proposed (e.g., “Listening to vocal music has a negative effect on learning a word list.”). From these hypotheses, predictions about specific events are derived (e.g., “People who study a word list while listening to vocal music will remember fewer words on a later memory test than people who study a word list in silence.”). These predictions can then be tested with a suitable experiment. Depending on the outcomes of the experiment, the theory on which the hypotheses and predictions were based will be supported or not, or may need to be modified and then subjected to further testing.

Characteristics

• A research question, which will determine research objectives.
• A particular and planned design for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
• The gathering of primary data, which is then analysed.
• A particular methodology for collecting and analysing the data, such as an experiment or survey.
• The limitation of the data to a particular group, area or time scale, known as a sample: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
• The ability to recreate the study and test the results. This is known as reliability.
• The ability to generalise from the findings to a larger sample and to other situations.

Usage

The researcher attempts to describe accurately the interaction between the instrument (or the human senses) and the entity being observed. If instrumentation is involved, the researcher is expected to calibrate his/her instrument by applying it to known standard objects and documenting the results before applying it to unknown objects. In other words, it describes the research that has not taken place before and their results.

In practice, the accumulation of evidence for or against any particular theory involves planned research designs for the collection of empirical data, and academic rigor plays a large part of judging the merits of research design. Several typologies for such designs have been suggested, one of the most popular of which comes from Campbell and Stanley. They are responsible for popularizing the widely cited distinction among pre-experimental, experimental, and quasi-experimental designs and are staunch advocates of the central role of randomized experiments in educational research.

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

• Quantitative research: Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables. These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
• Qualitative research: Qualitative research methods are used to gather non numerical data. It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Quantitative research methods

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

• Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.
• Experimental research: In experimental research, an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.
• Correlational research: Correlational research is used to find relation between two set of variables. Regression is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.
• Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.
• Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause-and-effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.
• Causal-Comparative research: This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

Qualitative research methods

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research method are used to derive a conclusion to support the theory or hypothesis being studied.

• Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real-life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.
• Textual Analysis: This primarily involves the process of describing, interpreting, and understanding textual content. It typically seeks to connect the text to a broader artistic, cultural, political, or social context (Fairclough, 2003).

A relatively new research method, textual analysis is often used nowadays to elaborate on the trends and patterns of media content, especially social media. Data obtained from this approach are primarily used to determine customer buying habits and preferences for product development, and designing marketing campaigns.

• Focus Groups:

A focus group is a thoroughly planned discussion guided by a moderator and conducted to derive opinions on a designated topic. Essentially a group interview or collective conversation, this method offers a notably meaningful approach to think through particular issues or concerns.

This research method is used when a researcher wants to know the answers to “how,” “what,” and “why” questions. Nowadays, focus groups are among the most widely used methods by consumer product producers for designing and/or improving products that people prefer.

• Observational method: Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.
• One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

Historical Research

Historical research data is subject to external criticism (verification of genuineness or validity of the source) and internal criticism (exploring the meaning of the source). Historical research has time and place dimensions. Simple chronology is not considered historical research because it does not interpret the meaning of events.

Historical research is a qualitative technique. Historical research studies the meaning of past events in an attempt to interpret the facts and explain the cause of events, and their effect in the present events. In doing so, researchers rely heavily on primary historical data (direct accounts of events, archival data – official documents, personal records, and records of eyewitnesses) and less frequently on secondary historical data.

Advantages

• The research is not involved in the situation that is studied
• The researchers do not interact with the subjects of study
• Analysis of historical data may help explain current and future events

Shortcomings

• Historical data is incomplete and vulnerable to time (documents can be destroyed by wars or over time)
• It can also be biased and corrupt (e.g. diaries, letters, etc. are influenced by the person writing them)
• Historical research is a complex and broad category because the topics of research (e.g. the study of a society) are affected by numerous factors that need to be considered and analysed.

Exploratory Research

Exploratory research is “the preliminary research to clarify the exact nature of the problem to be solved.” It is used to ensure additional research is taken into consideration during an experiment as well as determining research priorities, collecting data and honing in on certain subjects which may be difficult to take note of without exploratory research. It can include techniques, such as:

• Secondary research, such as reviewing available literature and/or data
• Informal qualitative approaches, such as discussions with consumers, employees, management or competitors
• Formal qualitative research through in-depth interviews, focus groups, projective methods, case studies or pilot studies

Advantages

• Flexibility and adaptability to change
• Exploratory research is effective in laying the groundwork that will lead to future studies.
• Exploratory studies can potentially save time and other resources by determining at the earlier stages the types of research that are worth pursuing

Disadvantages

• Exploratory studies generate qualitative information and interpretation of such type of information is subject to bias
• These types of studies usually make use of a modest number of samples that may not adequately represent the target population. Accordingly, findings of exploratory research cannot be generalized to a wider population.
• Findings of such type of studies are not usually useful in decision making in a practical level.

Exploratory research Steps

• Identify the problem: A researcher identifies the subject of research and the problem is addressed by carrying out multiple methods to answer the questions.
• Create the hypothesis: When the researcher has found out that there are no prior studies and the problem is not precisely resolved, the researcher will create a hypothesis based on the questions obtained while identifying the problem.
• Further research: Once the data has been obtained, the researcher will continue his study through descriptive investigation. Qualitative methods are used to further study the subject in detail and find out if the information is true or not.

Descriptive Research

Descriptive research is used to describe characteristics of a population or phenomenon being studied. It does not answer questions about how/when/why the characteristics occurred. Rather it addresses the “what” question (what are the characteristics of the population or situation being studied?). The characteristics used to describe the situation or population are usually some kind of categorical scheme also known as descriptive categories. For example, the periodic table categorizes the elements. Scientists use knowledge about the nature of electrons, protons and neutrons to devise this categorical scheme. We now take for granted the periodic table, yet it took descriptive research to devise it. Descriptive research generally precedes explanatory research. For example, over time the periodic table’s description of the elements allowed scientists to explain chemical reaction and make sound prediction when elements were combined.

Hence, descriptive research cannot describe what caused a situation. Thus, descriptive research cannot be used as the basis of a causal relationship, where one variable affects another. In other words, descriptive research can be said to have a low requirement for internal validity.

The description is used for frequencies, averages and other statistical calculations. Often the best approach, prior to writing descriptive research, is to conduct a survey investigation. Qualitative research often has the aim of description and researchers may follow-up with examinations of why the observations exist and what the implications of the findings are.

Types of Descriptive Research

Descriptive research is classified into different types according to the kind of approach that is used in conducting descriptive research. The different types of descriptive research are highlighted below:

• Descriptive-survey

Descriptive-survey research uses surveys to gather data about varying subjects. This data aims to know the extent to which different conditions can be obtained among these subjects.

For example, a researcher wants to determine the qualification of employed professionals in Maryland. He uses a survey as his research instrument, and each item on the survey related to qualifications is subjected to a Yes/No answer.

This way, the researcher can describe the qualifications possessed by the employed demographics of this community.

• Descriptive-normative survey

This is an extension of the descriptive-survey, with the addition being the normative element. In the descriptive-normative survey, the results of the study should be compared with the norm.

For example, an organization that wishes to test the skills of its employees by a team may have them take a skills test. The skills tests are the evaluation tool in this case, and the result of this test is compared with the norm of each role.

If the score of the team is one standard deviation above the mean, it is very satisfactory, if within the mean, satisfactory, and one standard deviation below the mean is unsatisfactory.

• Descriptive-status

This is a quantitative description technique that seeks to answer questions about real-life situations. For example, a researcher researching the income of the employees in a company, and the relationship with their performance.

A survey will be carried out to gather enough data about the income of the employees, then their performance will be evaluated and compared to their income. This will help determine whether a higher income means better performance and low income means lower performance or vice versa.

• Descriptive-analysis

Descriptive-analysis method of research describes a subject by further analyzing it, which in this case involves dividing it into 2 parts. For example, the HR personnel of a company that wishes to analyze the job role of each employee of the company may divide the employees into the people that work at the Headquarters in the US and those that work from Oslo, Norway office.

A questionnaire is devised to analyze the job role of employees with similar salaries and work in similar positions.

• Descriptive classification

This method is employed in biological sciences for the classification of plants and animals. A researcher who wishes to classify the sea animals into different species will collect samples from various search stations, then classify them accordingly.

• Descriptive-comparative

In descriptive-comparative research, the researcher considers 2 variables which are not manipulated, and establish a formal procedure to conclude that one is better than the other. For example, an examination body wants to determine the better method of conducting tests between paper-based and computer-based tests.

A random sample of potential participants of the test may be asked to use the 2 different methods, and factors like failure rates, time factors, and others will be evaluated to arrive at the best method.

• Correlative Survey

Correlative used to determine whether the relationship between 2 variables is positive, negative, or neutral. That is, if 2 variables, say X and Y are directly proportional, inversely proportional or are not related to each other.

Characteristics of descriptive research

The term descriptive research then refers to research questions, design of the study, and data analysis conducted on that topic. We call it an observational research method because none of the research study variables are influenced in any capacity.

Some distinctive characteristics of descriptive research are:

• Quantitative research: Descriptive research is a quantitative research method that attempts to collect quantifiable information for statistical analysis of the population sample. It is a popular market research tool that allows us to collect and describe the demographic segment’s nature.
• Uncontrolled variables: In descriptive research, none of the variables are influenced in any way. This uses observational methods to conduct the research. Hence, the nature of the variables or their behavior is not in the hands of the researcher.
• Cross-sectional studies: Descriptive research is generally a cross-sectional study where different sections belonging to the same group are studied.
• The basis for further research: Researchers further research the data collected and analyzed from descriptive research using different research techniques. The data can also help point towards the types of research methods used for the subsequent research.

Casual Research

Causal research, also called explanatory research, is the investigation of (research into) cause-and-effect relationships. To determine causality, it is important to observe variation in the variable assumed to cause the change in the other variables, and then measure the changes in the other variables. Other confounding influences must be controlled for so they don’t distort the results, either by holding them constant in the experimental creation of data, or by using statistical methods. This type of research is very complex and the researcher can never be completely certain that there are no other factors influencing the causal relationship, especially when dealing with people’s attitudes and motivations. There are often much deeper psychological considerations that even the respondent may not be aware of.

There are two research methods for exploring the cause-and-effect relationship between variables: experimentation (e.g., in a laboratory) and statistical research.

Objectives:

• Understanding which variables are the cause, and which variables are the effect. For example, let’s say a city council wanted to reduce car accidents on their streets. They might find through preliminary descriptive and exploratory research that both accidents and road rage have been steadily increasing over the past 5 years. Instead of automatically assuming that road rage is the cause of these accidents, it would be important to measure whether the opposite could be true. Maybe road rage increases in light of more accidents due to lane closures and increased traffic. It could also be the case of the old adage “correlation does not guarantee causation.” Maybe both are increasing due to another reason like construction, lack of proper traffic controls, or an influx of new drivers.
• Determining the nature of the relationship between the causal variables and the effect predicted. Continuing with our example, let’s say the city council proved that road rage had an increasing effect on the number of car accidents in the area. The causal research could be used for two things. First measuring the significance of the effect, like quantifying the percentage increase in accidents that can be contributed by road rage. Second, observing how the relationship between the variables works (i.e., enraged drivers are prone to accelerating dangerously or taking more risks, resulting in more accidents).

Advantages of causal researches

• Causal research helps identify the causes behind processes taking place in the system. Having this knowledge helps the researcher to take necessary actions to fix the problems or to optimize the outcomes.
• Causal research provides the benefits of replication if there is a need for it.
• Causal research helps identify the impacts of changing the processes and existing methods.
• In causal research, the subjects are selected systematically. Because of this, causal research is helpful for higher levels of internal validity.

Disadvantages of causal research

• The causal research is difficult to administer because sometimes it is not possible to control the effects of all extraneous variables.
• Causal research is one of the most expensive research to conduct. The management requires a great deal of money and time to conduct research. Sometimes it costs more than 1 or 2 million dollars to test real-life two advertising campaigns.
• One disadvantage of causal research is that it provides information about your plans to your competitors. For example, they might use the outcomes of your research to identify what you are up to and enter the market before you.
• The findings of causal research are always inaccurate because there will always be a few previous causes or hidden causes that will be affecting the outcome of your research. For example, if you are planning to study the performance of a new advertising campaign in an already established market. Then it is difficult for you to do this as you don’t know the advertising campaign solely influences the performance of your business understudy or it is affected by the previous advertising campaigns.
• The results of your research can be contaminated as there will always be a few people outside your market that might affect the results of your study.
• Another disadvantage of using causal research is that it takes a long time to conduct this research. The accuracy of the causal research is directly proportional to the time you spend on the research as you are required to spend more time to study the long-term effects of a marketing program.
• Coincidence in causal research is the biggest flaw of the research. Sometimes, the coincidence between a cause and an effect can be assumed as a cause and effect relationship.
• You can’t conclude merely depending on the outcomes of the causal research. You are required to conduct other types of research alongside the causal research to confirm its output.
• Sometimes, it is easy for a researcher to identify that two variables are connected, but to determine which variable is the cause and which variable is the effect is challenging for a researcher.

Basic research, also called pure research or fundamental research, is a type of scientific research with the aim of improving scientific theories for better understanding and prediction of natural or other phenomena.

Basic research focuses on the search for truth or the development of theory. Because of this property, basic research is fundamental. Researchers with their fundamental background knowledge “design studies that can test, refine, modify, or develop theories.”

In contrast, applied research uses scientific theories to develop technology or techniques which can be used to intervene and alter natural or other phenomena. Though often driven simply by curiosity, basic research often fuels the technological innovations of applied science. The two aims are often practiced simultaneously in coordinated research and development.

Basic research advances fundamental knowledge about the world. It focuses on creating and refuting or supporting theories that explain observed phenomena. Pure research is the source of most new scientific ideas and ways of thinking about the world. It can be exploratory, descriptive, or explanatory; however, explanatory research is the most common.

Basic research generates new ideas, principles, and theories, which may not be immediately utilized but nonetheless form the basis of progress and development in different fields. Today’s computers, for example, could not exist without research in pure mathematics conducted over a century ago, for which there was no known practical application at the time. Basic research rarely helps practitioners directly with their everyday concerns; nevertheless, it stimulates new ways of thinking that have the potential to revolutionize and dramatically improve how practitioners deal with a problem in the future.

Here are a few examples of questions asked in pure research:

• How did the universe begin?
• What are protons, neutrons, and electrons composed of?
• How do slime molds reproduce?
• How do the Neo-Malthusians view the Malthusian theory?
• What is the specific genetic code of the fruit fly?
• What is the relevance of the dividend theories in the capital market?

Basic Research Method

• Interview

An interview is a common method of data collection in basic research that involves having a one-on-one interaction with an individual in order to gather relevant information about a phenomenon. Interview can be structured, unstructured or semi-structured depending on the research process and objectives. 

In a structured interview, the researcher asks a set of premeditated questions while in an unstructured interview, the researcher does not make use of a set of premeditated questions. Rather he or she depends on spontaneity and follow-up questioning in order to gather relevant information.

On the other hand, a semi-structured interview is a type of interview that allows the researcher to deviate from premeditated questions in order to gather more information about the research subject. You can conduct structured interviews online by creating and administering a survey online on Online tool.

• Observation

Observation is a type of data-gathering method that involves paying close attention to a phenomenon for a specific period of time in order to gather relevant information about its behaviors. When carrying out basic research, the researcher may need to study the research subject for a stipulated period as it interacts with its natural environment.

Observation can be structured or unstructured depending on its procedures and approach. In structured observation, the data collection is carried out using a predefined procedure and in line with a specific schedule while unstructured observation is not restricted to a predetermined procedure.

• Experiment

An experiment is a type of quantitative data-gathering method that seeks to validate or refute a hypothesis and it can also be used to test existing theories. In this method of data collection, the researcher manipulates dependent and independent variables to achieve objective research outcomes.

• Questionnaire

A questionnaire is a data collection tool that is made up of a series of questions to which the research subjects provide answers. It is a cost-effective method of data gathering because it allows you to collect large samples of data from the members of the group simultaneously.

You can create and administer your pure research questionnaire online using Online tool and you can also make use of paper questionnaires; although these are easily susceptible to damage.

A variable is, as the name applies, something that varies. Age, sex, export, income and expenses, family size, country of birth, capital expenditure, class grades, blood pressure readings, preoperative anxiety levels, eye color, and vehicle type are all examples of variables because each of these properties varies or differs from one individual to another.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using.

Types of Variable

Qualitative Variables

An important distinction between variables is between the qualitative variable and the quantitative variable.

Qualitative variables are those that express a qualitative attribute such as hair color, religion, race, gender, social status, method of payment, and so on. The values of a qualitative variable do not imply a meaningful numerical ordering.

The value of the variable ‘religion’ (Muslim, Hindu,  ..,etc.) differs qualitatively; no ordering of religion is implied. Qualitative variables are sometimes referred to as categorical variables.

Categorical variables may again be described as nominal and ordinal.

Ordinal variables are those which can be logically ordered or ranked higher or lower than another but do not necessarily establish a numeric difference between each category, such as examination grades (A+, A, B+, etc., clothing size (Extra-large, large, medium, small).

Nominal variables are those who can neither be ranked nor logically ordered, such as religion, sex, etc.

A qualitative variable is a characteristic that is not capable of being measured but can be categorized to possess or not to possess some characteristics.

Quantitative Variables

Quantitative variables, also called numeric variables, are those variables that are measured in terms of numbers. A simple example of a quantitative variable is a person’s age.

The age can take on different values because a person can be 20 years old, 35 years old, and so on. Likewise, family size is a quantitative variable, because a family might be comprised of one, two, three members, and so on.

That is, each of these properties or characteristics referred to above varies or differs from one individual to another. Note that these variables are expressed in numbers, for which we call them quantitative or sometimes numeric variables.

A quantitative variable is one for which the resulting observations are numeric and thus possesses a natural ordering or ranking.

Discrete and Continuous Variables

Quantitative variables are again of two types: discrete and continuous.

Variables such as some children in a household or number of defective items in a box are discrete variables since the possible scores are discrete on the scale.

Discrete Variable

A discrete variable, restricted to certain values, usually (but not necessarily) consists of whole numbers, such as the family size, number of defective items in a box. They are often the results of enumeration or counting.

Dependent Variable

The variable that is used to describe or measure the problem or outcome under study is called a dependent variable.

In a causal relationship, the cause is the independent variable, and the effect is the dependent variable. If we hypothesize that smoking causes lung cancer, ‘smoking’ is the independent variable and cancer the dependent variable.

Continuous Variable

A continuous variable is one that may take on an infinite number of intermediate values along a specified interval. Examples are:

• The sugar level in the human body
• Blood pressure reading
• Temperature
• Height or weight of the human body
• Rate of bank interest
• Internal rate of return (IRR)

Independent Variable

The variable that is used to describe or measure the factor that is assumed to cause or at least to influence the problem or outcome is called an independent variable.

The definition implies that the experimenter uses the independent variable to describe or explain the influence or effect of it on the dependent variable.

Variability in the dependent variable is presumed to depend on variability in the independent variable.

Dependent and Independent Variables

In many research settings, there are two specific classes of variables that need to be distinguished from one another, independent variable and dependent variable.

Many research studies are aimed at unrevealing and understanding the causes of underlying phenomena or problems with the ultimate goal of establishing a causal relationship between them.

Background Variable

In almost every study, we collect information such as age, sex, educational attainment, socioeconomic status, marital status, religion, place of birth, and the like. These variables are referred to as background variables.

These variables are often related to many independent variables so that they influence the problem indirectly. Hence, they are called background variables.

Extraneous Variable

Most studies concern the identification of a single independent variable and the measurement of its effect on the dependent variable.

But still, several variables might conceivably affect our hypothesized independent-dependent variable relationship, thereby distorting the study. These variables are referred to as extraneous variables.

Moderating Variable

In any statement of relationships of variables, it is normally hypothesized that in some way, the independent variable ’causes’ the dependent variable to occur. In simple relationships, all other variables are extraneous and are ignored. In actual study situations, such a simple one-to-one relationship needs to be revised to take other variables into account to better explain the relationship.

Suppressor Variable

In many cases, we have good reasons to believe that the variables of interest have a relationship within themselves, but our data fail to establish any such relationship. Some hidden factors may be suppressing the true relationship between the two original variables.

Such a factor is referred to as a suppressor variable because it suppresses the actual relationship between the other two variables.

Intervening Variable

Often an apparent relationship between two variables is caused by a third variable.

For example, variables X and Y may be highly correlated, but only because X causes the third variable, Z, which in turn causes Y. In this case, Z is the intervening variable.