admin - PhD Thesis Bangalore

Solving the statistical juggle: Anova, Ancova,Manova, Mancova

It is challenging to keep the difference between the four statistical techniques aligned. These four similar but still different techniques are ANOVA, ANCOVA, MANOVA, MANCOVA. Before we start to appreciate the differences between these four techniques, it is helpful to review the similarities between them.

Almost every researcher would feel trapped when it comes This statistical soup of the four techniques. Most novice researchers feel confused and trapped when it comes to making logical comparisons between ANOVA, ANCOVA, MANOVA, MANCOVA. Let us understand the analogy between them.

ANOVA

The expansion of the term ANOVA is Analysis of Variance. It is the fundamental element of the four techniques. In ANOVA there is only one dependent variable. In statistics, when there is a comparison between two or more than two means at the same time, the technique used for comparison is ANOVA. The values and results given by ANOVA are used to find if there is any relationship between the different variables. If we have to find out if the means of two or more groups are equal, then ANOVA comes to our rescue through a test known as T-test. ANOVA as a statistical technique is extremely helpful in avoiding TYPE 1 Error, especially when one has to carry out multiple, two sample tests. Another very useful feature of ANOVA is that it can compare the scale or interval variables, also known as the continuous variables. There are three distinct models in ANOVA:

Fixed Effect Model: This is subjected to one or more than one treatment to identify whether the value of the response is changing.
Random Effect Model: When the treatment that is applied to the subject is not fixed the random effect model is used.
Mixed Effect Model: This Model has got dual effects, the fixed as well as the random effects and is applied to experimental factors.

Primarily, we see two types of ANOVA being used, One way ANOVA and two-way ANOVA. In one-way ANOVA the levels are compared. We can also call them as groups. But they are of a single factor and are based on a single continuous response variable.

In the case of two-way ANOVA, it compares the levels of two or more factors for the mean differences on a single continuous response variable. In application, the use of one-way ANOVA is more common in practice. So, whenever the term ANOVA is used without specifications, by default the interpretation is one way ANOVA only.

BASIS FOR COMPARISON	ONE WAY ANOVA	TWO WAY ANOVA
Meaning	One way ANOVA is a hypothesis test, used to test the equality of three or more population means simultaneously using variance.	Two way ANOVA is a statistical technique where in, the interaction between factors, influencing variables can be studied.
Independent Variable	One	Two
Compares	Three or more levels of one factor.	Effect of multiple levels of two factors.
Number of Observations	Need not to be the same in each group.	Need to be equal in each group
Design of experiments	Need to satisfy only two principles	All three principles needs to be satisfied

Let us understand better by looking at examples of one way and two-way ANOVA

Example of one-way ANOVA

A class of 90 students is randomly split into three different groups. All the three groups are assigned for a test after fifteen days. The test will be for the same syllabus and a common question paper. However, the studying technique given to all the three groups is different. The purpose of the study will be to determine if the technique of study has any impact on the study scores. One-way ANOVA will work here to identify if there is a statistically significant difference between the mean scores of these three student groups. This will help us know if the technique of study has any impact on the scores of the test.

Example of Two Way ANOVA: This is used to determine how two factors impact a response variable, and to find out whether there is an interaction between the two factors on the response variable.

From your research you want to know if your level of fitness regime (no regime, light, moderate or heavy regime) and gender (Male/female) has some kind of impact on weight loss. Here there are two factors involved in the study. These two factors are fitness regime and gender and the response variable here is weight loss. Here two-way ANOVA is conducted to identify the impact of type of fitness regime as well as gender on the weight loss and to know if there exists an interaction re relationship between the independent and dependent variables which are type of exercise, gender, and weight loss. The below flowchart explains this better.

ANCOVA

A layman or someone who has no understanding of statistical techniques would express his understanding of the difference between ANOVA and ANCOVA as the letter “c”. But if they are two different words with different spellings, even if with the slightest of variation then it is with a purpose. Both these techniques are different from each other. ANCOVA is different from ANOVA for it has a single continuous response variable. ANCOVA can make an explicit distinction and comparison between the response variable with both continuous independent variable and factor. The continuous independent variable in ANCOVA is called a covariate. ANCOVA is not limited to comparative analysis but is also seen in getting used with a single response variable, continuous independent variable with no factors attached. This kind of analysis has another nomenclature, which is regression.

Example of ANCOVA:

Taking the same example forward we used a one-way ANOVA, the class of 90 students being split into three groups of 30 students in each group and each group used a different study technique for the same exam to be taken after a period of one month. But if we want to further account for the grade the student already has in the class, the current grade is used as a covariate. ANCOVA is applied to determine if there is a statistically significant difference between the mean scores of the three groups.

This test not only allows us to know if the studying technique has an impact on the scores of the test but it also tests the same after the influence of the covariate has been removed.

Thus, if we find that there is a statistically significant difference in exam scores between the three studying techniques, we can be sure that this difference exists even after accounting for the student’s current performance or grades in the class, which means their present performance in the class is satisfactory or not.

Let us summarize the difference between ANOVA and ANCOVA from this tabular representation

Particulars	ANOVA	ANCOVA
Stands for	Analysis of Variance	Analysis of Covariance
Meaning	It is a statistical method to test the variance or differences between the means of three or more groups	It evaluates the mean of a dependent variable based on a categorical independent variable while considering and controlling the effects of covariates
Uses	Can blend linear and nonlinear models	A linear model is used alone
Involves	Categorical independent variables	Categorical and metric independent variables
Covariate	Neglects the influence of covariates	Considers and controls the effect of covariates

MANOVA

In statistics, MANOVA contains multiple dependent variables. It helps to identify the difference between two or more than two dependent variables at the same time. It helps to surface out the interactions between dependent and independent variables. MANOVA is nothing but another type of ANOVA and it has two or more continuous variables. If the researcher wants to compare two or more continuous response variables with the help of a single response factor, then one-way MANOVA works fine. The need for two-way MANOVA arises when two or more continuous variables are there and they have to be compared with at least two factors. In ANOVA a T test is used when calculating or working with a single response variable or binary factor. But a T test does not have the capability to calculate the distinctions for more than one response variable at the same time. That is the reason it is not being used in MANOVA.

Example of One-Way MANOVA:

Like explained above, MANOVA is used when one factor and two response variables. So let us understand this better with an example. We want to understand how the level of academic qualification of a person (high School, Undergraduate, Masters, Doctoral degree) impacts the annual income and the amount of student loan debt. Here there is a single factor which is the level of qualification of a student and two response variables which are the annual income and student loan or debt, so one-way MANOVA will be used here.

Two-Way MANOVA Example: Here we intend to find out how level of education and gender has an impact on both annual income and amount of student loan debt. In this case, we have two factors (level of education and gender) and two response variables (annual income and student loan debt), so we need to conduct a two-way MANOVA.

MANCOVA

Like the slight variation in spelling that exists between ANOVA and ANCOVA, the similar difference exists between MANOVA and MANCOVA. “C” Here also the addition of C makes the application of two words distinct. “C” stands for covariance. Both MANOVA and MANCOVA show two or more response variables but the main difference between them is the characteristics of the Independent Variables. MANCOVA compares two or more continuous response variables by levels of factor variables along with a covariate.

Assumptions

The Assumptions of MANCOVA are the same as the assumptions for MANOVA, with an inclusion of a few more for covariance. As one can imagine for a complex test in comparison to a relatively easier one, such as the Z test, these assumptions are lengthy and somewhat complex. This is one reason why these tests are nearly always performed using software, as most statistical software will test for these assumptions before running the test.

Covariates and dependent variables are Assumptions
The assumptions for MANCOVA are the same as the assumptions for MANOVA, with the addition of a couple more for covariance. As you would expect with a complex test (compared to a much simpler test like a z-test), these assumptions are lengthy and somewhat complex. This is one reason why these tests are nearly always performed using software, as most statistical software will test for these assumptions before running the test.
Covariates and dependent variables are continuous and ratio/ ordinal.
Covariance matrices should be equal (reduces Type I error).
Independent variables are categorical.
Independence of variables: the variables do not influence each other.
Random sampling: the data was collected using a random selection method.
Normality: the dependent variables follow a (multinomial) normal distribution for each group.
Absence of multicollinearity — the dependent variables shouldn’t be significantly correlated.
Homogeneity of variance between groups.
Covariance Matrices should be equal
The independent variables are categorical
The variables are independent of each other and do not influence each other
The sampling technique used is simple random sampling
There is normal distribution of the dependent variable for each of the groups
There should not be a lot of correlation between the dependent variables: multicollinearity should not be there
There should be homogeneity between the groups

Example One-way MANCOVA

In MANCOVA, which is like MANOVA, the only difference being that we add a covariate. So, taking the same example we took for MANOVA, we intend to find out how a student’s level of education impacts both their annual income and amount of student loan debt. In addition to that we also want to include the annual income of the parents into consideration. So, here we have one factor (level of education), one covariate (annual income of the students’ parents) and two response variables (annual income of student and student loan debt), so we need to conduct a one-way MANCOVA.

Example Two-Way MANCOVA

We want to know how a student’s level of education and their gender impacts both their annual income and amount of student loan debt. However, we want to account for the annual income of the students’ parents as well. In this case, we have two factors (level of education and gender), one covariate (annual income of the students’ parents) and two response variables (annual income of student and student loan debt), so we need to conduct a two-way MANCOVA.

Conclusion

Easy hack, how to distinguish one test from the other

MANCOVA, MANOVA, ANOVA, ANCOVA: it can all get a little confusing to remember and distinguish one from the other. However, all the tests can be thought of as variants of the MANCOVA, if you register that the “M” in MANCOVA stands for Multiple and the “C” stands for Covariates. Tests can be thought of as a MACOVA…

ANOVA: … without multiple dependent variables and covariates (hence the missing M and C).
ANCOVA: …without multiple dependent variables (hence the missing M).
MANOVA: …without covariates (hence the missing C).

How to Select a questionnaire method based on your research topics ?

Conducting research often involves gathering data through questionnaires, which are valuable tools for collecting information from respondents. However, selecting the most appropriate questionnaire design or method for your research topic is a critical decision that can significantly impact the quality and reliability of your findings. The choice of the questionnaire method depends on various factors, including the research objectives, target population, nature of the research topic, and available resources.

Selecting the appropriate questionnaire method is crucial because it directly affects the validity and accuracy of the data collected. Different research topics require different approaches to ensure that the questionnaire method aligns with the specific research goals and objectives. By selecting the right method, researchers can obtain meaningful and relevant data that effectively address their research questions.

Now, there are various open and close-ended questions which we need to know how to select the best questionnaire method based on your research topics. The questions are described below:

Open-ended questions:

1. What factors should researchers consider when selecting a questionnaire method for their research ?

When selecting a questionnaire method for research, researchers should consider several factors to ensure the effectiveness and reliability of their study. Here are some important factors to consider:

Research Objectives: Researchers should start by clearly defining their research objectives and what they aim to achieve through the questionnaire. This will help determine the type of data needed and guide the selection of an appropriate questionnaire method.
Target Population: Consider the characteristics of the target population, including their demographics, language proficiency, cultural background, and accessibility. Ensure that the questionnaire method is suitable for the specific population under study.

2. How can the research objectives influence the choice of the questionnaire method?

The research objectives have a significant influence on the choice of the questionnaire method. Here’s how they can impact the selection process:

Data Required: The research objectives determine the type of data needed to address the research questions. For example, if the objective is to gather quantitative data, a structured questionnaire with closed-ended questions may be suitable. On the other hand, if the objective is to gather qualitative data or explore complex topics in-depth, an open-ended questionnaire or a mix of open-ended and closed-ended questions may be preferred.
Validity and Reliability: Different questionnaire methods have varying levels of validity and reliability. Depending on the research objectives, researchers need to select a method that ensures the accuracy and consistency of the data collected. For instance, if the objective is to compare responses across different groups or time points, a standardized questionnaire with established validity and reliability measures may be necessary.

3. What are the advantages and disadvantages of using open-ended questions in a questionnaire ?

Using open-ended questions in a questionnaire offers several advantages and disadvantages. Here are some of the key points to consider:

Advantages of Open-ended Questions:

In-depth Responses: Open-ended questions allow participants to provide detailed and nuanced responses, providing richer insights into their thoughts, feelings, and experiences. This can be particularly useful for exploratory research or when seeking to uncover new perspectives or ideas.
Flexibility: Open-ended questions provide participants with the flexibility to express their thoughts in their own words, without being constrained by predefined response options. This allows for a more personalized and authentic response, enabling researchers to capture diverse viewpoints.

Disadvantages of Open-ended Questions:

Time-consuming: Analyzing open-ended responses can be time-consuming and resource-intensive. Researchers need to read, interpret, and code each response manually, which can be a time-consuming process, especially when dealing with a large sample size.
Subjectivity: Interpreting open-ended responses is subjective to some extent. Researchers’ biases and preconceptions may influence their analysis and interpretation of the data. Ensuring inter-rater reliability and using rigorous coding techniques can help mitigate this issue.

4. Can you provide examples of research topics where qualitative questionnaire methods would be more appropriate ?

Qualitative questionnaire methods are often more appropriate for research topics that aim to explore in-depth understanding, subjective experiences, and nuanced perspectives. Here are some examples of research topics where qualitative questionnaire methods may be suitable:

Exploring Attitudes and Beliefs: Qualitative questionnaires can be used to investigate people’s attitudes, beliefs, and opinions on various social, cultural, or political issues. For instance, studying public opinions on climate change, gender equality, or immigration policies can benefit from qualitative questionnaires to capture diverse perspectives and understand underlying motivations.
Investigating Personal Experiences: Research topics that focus on personal experiences, such as mental health, caregiving, or patient satisfaction, can benefit from qualitative questionnaires. Open-ended questions can elicit detailed narratives, allowing participants to express their thoughts, emotions, and experiences in their own words.

5. In what ways can the selected questionnaire method impact the reliability and validity of the research findings ?

The selected questionnaire method can have a significant impact on the reliability and validity of research findings. Here’s how:

Reliability: Reliability refers to the consistency and stability of the measurement. The questionnaire method can influence the reliability of the research findings in the following ways:

– Consistency of Administration: The method of questionnaire administration should be standardized to ensure consistent delivery across participants. Variations in administration (e.g., in-person interviews vs. online surveys) can introduce variability in responses, affecting reliability.

– Clear Instructions and Response Options: The questionnaire should have clear and unambiguous instructions to minimize response errors or misunderstandings. Ambiguous or confusing questions can lead to inconsistent responses, reducing reliability.

Validity: Validity refers to the extent to which a questionnaire measures what it intends to measure. The questionnaire method can influence the validity of the research findings in the following ways:

– Content Validity: The questionnaire method should adequately cover all relevant aspects of the research topic. Content validity can be enhanced by conducting a thorough literature review, expert reviews, or pilot testing to ensure the questionnaire captures the intended constructs or variables.

– Construct Validity: The questionnaire method should accurately measure the constructs or variables of interest. This can be assessed by examining the relationships between questionnaire items and other measures that theoretically relate to the construct.

Close-ended questions:

1. Do you believe that the selection of a questionnaire design or method should be influenced by the nature of the research topic? (Yes/No)

2. Are quantitative questionnaire methods more suitable for research that aims to measure numerical data? (Yes/No)

3. Which of the following factors do you consider important when choosing a questionnaire method: ease of administration, response rate, or data analysis requirements? (Select all that apply)

– Ease of administration

– Response rate

– Data analysis requirements

4. Would you prefer to use a Likert scale or a ranking scale for a questionnaire that aims to measure attitudes? (Likert scale/Ranking scale/Not sure)

5. Do you believe that using closed-ended questions limits the depth of understanding in research? (Yes/No)

Selecting the right questionnaire method

Selecting a questionnaire method for your research topics involves considering various factors. Here are some guidelines to help you make an informed decision:

Tips to consider before selecting the right questionnaire method

Research goals: Clearly define your research goals and objectives. Determine what information you need to gather and what specific aspects you want to explore.
Research questions: Develop clear and concise research questions that align with your objectives. This will help you identify the type of data you need to collect.
Sample characteristics: Consider the characteristics of your target population or sample. Factors such as demographics, literacy levels, and cultural background can influence the choice of the questionnaire method.
Data type: Determine whether you need quantitative or qualitative data. Quantitative data involves numerical responses, while qualitative data capture subjective insights and opinions.
The complexity of information: Assess the complexity of the information you are seeking. If the subject matter is intricate or requires detailed explanations, consider using open-ended questions or interviews to allow respondents to provide in-depth responses.
Time and resources: Evaluate the available time and resources for data collection. Questionnaires can be administered in different ways, such as face-to-face interviews, online surveys, or postal/mail surveys. Consider the logistics, costs, and convenience associated with each method.
Response rate and bias: Consider potential response rates and sources of bias. Certain questionnaire methods may yield higher response rates or minimize response bias, while others may be more prone to non-response bias due to self-selection.
Existing research: Review previous studies in your field to identify commonly used questionnaire methods. Consider the strengths and limitations of these methods and their suitability for your research topic.
Pilot testing: Before finalizing your questionnaire method, conduct pilot testing to evaluate its clarity, relevance, and effectiveness. Make necessary revisions based on feedback from a small sample before proceeding with the full-scale data collection.

By considering these factors, you can select a questionnaire method that aligns with your research goals, captures the desired data type, suits your target population, and optimizes the quality and reliability of your research findings.

Problems of selecting a questionnaire method

While selecting a questionnaire method based on your research topics can be effective, there are some potential problems you may encounter:

Bias: The design and wording of the questionnaire can introduce bias and influence respondents’ answers. Biased questions may lead to inaccurate or misleading data.
Response rate: Depending on the chosen method, you may face challenges in obtaining a high response rate. Low response rates can affect the representativeness of your sample and introduce potential biases.
Non-response bias: If certain groups of people are less likely to respond to the questionnaire, non-response bias can occur, leading to skewed results and limited generalizability.
Limited flexibility: Questionnaires may lack the flexibility to capture complex or nuanced information. Closed-ended questions restrict respondents to predetermined response options, potentially missing out on important insights.
Social desirability bias: Respondents may provide socially desirable answers rather than their true opinions or behaviors, leading to an inaccurate representation of reality.
Lack of context: Questionnaires may not capture the full context or nuances of participants’ experiences or perspectives, especially in qualitative research.
Misinterpretation or misunderstanding: Poorly designed or ambiguous questions can lead to misinterpretation or misunderstanding by respondents, resulting in unreliable or invalid data.
Inadequate sample representation: Depending on the method used, it may be challenging to reach a diverse and representative sample. This can limit the generalizability of your findings.
Resource and logistical constraints: Certain questionnaire methods, such as face-to-face interviews or postal surveys, can be time-consuming, expensive, or require extensive logistical arrangements, which may pose challenges in terms of resources and feasibility.

To mitigate these problems, it is crucial to carefully design and test your questionnaire, consider potential biases and limitations, and supplement the questionnaire method with other research methods, such as interviews or observations, to gain a more comprehensive understanding of your research topics.

In conclusion, selecting a questionnaire method for your research topics is a critical decision that requires careful consideration. By following a systematic approach, you can choose a method that aligns with your research goals, captures the desired data type, and suits your target population. However, it is important to be aware of potential problems such as bias, low response rates, non-response bias, limited flexibility, social desirability bias, lack of context, misinterpretation, inadequate sample representation, and resource constraints.

To address these challenges, researchers should focus on questionnaire design, ensuring clarity, relevance, and neutrality of questions to minimize bias. Pilot testing can help identify and rectify any issues before full-scale data collection. Additionally, researchers should be mindful of supplementing questionnaire methods with other research approaches to enhance the depth and validity of findings.

By acknowledging the potential limitations and considering alternative research methods, researchers can maximize the quality and reliability of their research outcomes. Ultimately, a well-chosen questionnaire method, complemented by appropriate research strategies, will contribute to obtaining valuable insights and advancing knowledge in the chosen field.

Thank you for reading this blog.

Exploring the Grounded Theory Approach from data to theory for your qualitative research

Grounded theory is a qualitative research approach that attempts to uncover the meanings of people’s social actions, interactions, and experiences. These explanations are called ‘grounded’ because they are grounded in the participants’ own explanations or interpretations.

Barney Glaser and Anselm Strauss originated this method in their 1967 book, The Discovery of Grounded Theory. The grounded theory approach has been used by researchers in various disciplines, including sociology, anthropology, psychology, economics, and public health.

Grounded theory qualitative research was considered path-breaking in many respects upon its arrival. The inductive method allowed the analysis of data during the collection process. It also shifted focus away from the existing practice of verification, which researchers felt didn’t always produce rigorous results.

Let’s take a closer look at grounded theory research.

Meaning of Grounded Theory
Process of construction Grounded Theory Research
Features of Grounded Theory
Application of Grounded theory with examples
Advantages of Grounded Theory
Disadvantages of Grounded Theory

1) Meaning of Grounded Theory:

Grounded theory is a qualitative method designed to help arrive at new theories and deductions. Researchers collect data through any means they prefer and then analyze the facts to arrive at concepts. Through a comparison of these concepts, they plan theories. They continue until they reach sample saturation, in which no new information upsets the theory they have formulated. Then they put forth their final theory.

In grounded theory research, the framework description guides the researcher’s own interpretation of data. A data description is the researcher’s algorithm for collecting and organizing data while also constructing a conceptual model that can be tested against new observations.

Grounded theory doesn’t assume that there’s a single meaning of an event, object, or concept. In grounded theory, you interpret all data as information or materials that fit into categories your research team creates.

2) Process of conducting Grounded Theory Research

Now that we’ve examined what is grounded theory, let’s inspect how it’s conducted. There are four steps involved in grounded theory research:

Step1: Culling out concepts from interviews, Observation and Reflection

Step 2: Organizing the data into categories representing sub themes or sub plots
Step 3: Comparing the developed categories with one another to identify two or more theories thar compete
Step 4: Designing the construction of the research hypothesis statement or the concept map.

Grounded theory is a relatively recent addition to the tools at a researcher’s disposal. There are several methods of conducting grounded theory research. The following processes are common features:

Coding: Codes are sets of words that are used for describing the meaning of a concept. Usually they are recorded through interviews, observation, and other data sources. Grounded theory starts with codes and after making the codes, the researcher must select concepts that represent each code.

Memoing: The researcher must identify some interesting existing theories and understand them. He must further develop a connection between these existing theories and the new research. It is an internal process and is usually done to form concepts and verify the validity of the research.

Putting together the findings: Once a new theory has been developed from data, the findings must be written. This is the final step. The researcher can write a tentative hypothesis from their research findings.

3) Features of Grounded Theory:

The grounded theory is unlike other research techniques and has some unique features that make it distinct from others. Some of its characteristics are

Personalized Interaction: This theory is all about personal interaction between the researcher and the participants. The researcher in this method is supposed to ask questions from participants, spend time with them, observe them in situations and interview them, whether in group or personally. They must ask each participant about anything that is related to their research. It could be experiences, observations, or anything else. The purpose is to decipher the opinion of the respondent. This might not happen in a single interaction and it’s a possibility that the researcher might have to convince the participant to meet him and give him time and. Effort until the researcher is not convinced that he has understood the perspective of the respondent. In some situation, to make the respondent participative, the researcher might have to give monetary or non-monetary benefits or rewards to the respondent for giving his effort and participation in answering the questions or participating in the survey.
Easy to Mold: Being flexible is one of the most important tenets of grounded theory. This is because the grounded theory is supposed to focus on the participants, their interpretations, and explanations. These cannot be standardized and there is a lot of scope for subjectivity here. Each respondent or participant is a distinct personality and may have his own opinion and preferences. The grounded theory needs to be flexible enough to incorporate the distinctiveness in each response and eventually compile them together under categories with similar responses. Many times, a researcher cannot get to any conclusion about the preference or behavior of a respondent by in one interaction or direct questioning. One or more interpretations may be derived which were previously unknown. These interpretations are called as constructs.
It begins with a case study: The grounded theory approach often starts with a case study. A group or an individual is observed here, and the researcher develops a tentative definition of their constructs through the case analysis. Later case analysis is used to create a hypothesis which explains the construct. The validity of all the hypotheses needs to be proven for the purpose of acceptance and explanation.
Continuous Assessment of Data: Since the grounded theory deals with interactions with the respondents an interview guide is a prerequisite. It is a set of questions which are asked in such a way that that the meaning of the construct is made clear and elaborated. The gathered data is looked upon by the researcher to see whether the construct is true, false, or partially applicable. This becomes a long and continuous process as when more and more data keep coming in it keeps adding on to the constructs and new theories can developed in the process.

4) Application of Grounded Theory with Examples:

Organizations used grounded theory to create advantage from the competitors and its application is getting acceptable globally in corporations for decision making in different domains.

Some of the applications of grounded theory are.

Usually, the marketing team in an organization uses the grounded theory to get information from employees, particularly the marketing executives to understand how the product or the service could be further improved in a better and more structured way.
The HR department may use this theory to understand the causes of dissatisfaction or frustration amongst the employees. Employees can explain what they feel is lacking in the organizational policy for employees. This data that the HR gathers, upon analysis can help them to reach to the root cause of the problem ad also identify effective solutions.
The organization can take effective branding decisions based on this theory. Such as creating more appealing logos, tag lines or promotional strategies. The marketing department may interview existing and potential customers about their preferences, likes and dislikes. They will gather coded data that relates back to the interviews taken and use it for second iteration.

These are only some of the applications and examples of the uses of the grounded theory in business setting. This theory can be applied in various other important aspects of decision making in an organization.

5) Advantages Of Grounded Theory:

The grounded theory is extremely flexible in its uses, and this makes it a widely acceptable theory. Other than the flexibility advantage, there are a few more advantages of this theory. These are:

· This theory is based in the quest for finding the meaning. It does not rely upon what has been done in the past. Rather, researchers are more interested in what the participants are saying about their likes, dislikes, experiences. This adds a lot of novelty and subjectivity to the theory.

· It allows the researchers to use inductive reasoning. This makes the theory away from prejudices and allows the researcher to view the opinion and perspective of the respondent. This gives an advantage of objectivity to the process and takes it away from biases when it comes to data collection and analysis of data.

· This theory gives the platform of constant comparison of data to concepts. This refines the theory as the research proceeds. There are some methods that only look for verifying existing hypotheses. This theory is more advanced and contrast to those.

· This theory allows the researcher to conduct experiments. This gives a support to their research hypotheses. Through the experiment researcher can put to test the applicability of ideas and provide support to the hypotheses and the theory development with the help of the results of the experiments.

· It produces a clear theoretical model which is far from being abstract. It gives the opportunity to the researcher to establish connections between cases and understand how each case fits with the other.

· With grounded theory researchers can produce analysis that is more detailed than with any other method.

· The grounded theory lays a lot of emphasis on objective interpretation of data. Researchers in this theory get the freedom to introspect their own preconceived ideas about a topic and analyze them critically to understand their usage and applicability.

6) Disadvantages of Grounded Theory:

Like any other method or theory, the grounded theory also has some disadvantages, and the researcher must be aware and should consider them.

· The grounded theory does not promote the concept of consensus and hence there are always competing view on the same concept. This may sometime defer or come in the way of the acceptance of any research done by this theory by the community.

· It is open ended in nature where the responses and results are theoretical in nature and not concerned with true or false but more with individual perspective where all can be right in their own way. The subjectivity element here makes it overly theoretical in nature.

· To understand and apply the grounded theory, the researcher must be highly skills and knowledgeable and have critical thinking skills developed. A novice researcher may not be able to justice to this theory as he or she is supposed to be objective in their approach, be unbiased ad conduct the interviews without any biases and personal agendas influencing the results.

Conclusion

Thus, to conclude we can say that the grounded theory is a systematic methodology that has found its application in qualitative research that is the forte of social scientists. It is inductive reasoning where the construction of hypotheses and theories is done after the collection and analyses of data. This contrasts with the deductive model which has been predominantly used in traditional scientific research.

Any study undertaken for Grounded theory begins with a collection of qualitative data. As the researchers review that data that has been collected, the concepts start becoming apparent to him. These ideas and concepts emerge out from the data. To structure these concepts and tags, researchers give them codes. As data keeps getting collected as a continuous process the grouping of codes is done and they get formulated into higher level concepts and eventually into categories. These categories become the foundation of the hypotheses or a new theory. As an inductive approach, the hypotheses are formed in the end after the analyses of data is done and that is what makes it unique, flexible, and widely applicable.

The grounded theory approach is a strong analytical tool and can be of great help to researchers and when there are decisions to be made a workplace. In the present times, knowledge and application of analytical tools is the most sought-after skill in the professional world. Managers who can apply these tools, such as the grounded theory in the research the more value addition they are able to make to their organization.

Developing a Framework for Evaluating the Feasibility of a PhD Research Topic in Computer Science

As technology continues to advance at a rapid pace, computer science remains a dynamic field that offers a wealth of research opportunities. For those pursuing a PhD in computer science, choosing a feasible research topic is a critical first step towards a successful dissertation. However, the process of evaluating the feasibility of a research topic can be complex and requires careful consideration of a variety of factors, including ethical considerations, potential challenges, and limitations. In this blog, we will explore some key questions and answers related to the feasibility of a PhD research topic in computer science, providing valuable insights for those embarking on this exciting journey. Now it’s getting hard to choose the best PhD Topic Consultation Services. But there are some things you can do such as check their reputation, expertise, service quality, cost, consultation approach and communication.

To develop a framework for evaluating the feasibility of a PhD Research topic in Computer Science, we need to know the steps included to develop the framework through a series of questions and answers. These insights can not only help us to develop the framework but also we can get more in-depth knowledge about the topic so it becomes easier for us to evaluate the feasibility. So, let’s get started.

The first question is What are the key factors to consider when evaluating the feasibility of a PhD research topic in computer science?

When evaluating the feasibility of a PhD research topic in computer science, there are several key factors to consider. These include:

Research question: The research question should be clear and specific, and should have the potential to contribute to the existing body of knowledge in computer science. It is important to ensure that the research question is feasible, relevant, and manageable within the timeframe and resources available.

Literature review: A comprehensive literature review should be conducted to assess the current state of research in the area of interest. This can help to identify any gaps in the literature and provide a foundation for the research.

Methodology: The methodology should be appropriate for the research question and should be feasible within the resources and time available. It is important to consider the availability of data, equipment, and expertise required for the chosen methodology.

Data availability: The availability and accessibility of data should be considered when evaluating the feasibility of a research topic in computer science. It is important to ensure that the data is of sufficient quality and quantity to address the research question.

Funding: The availability of funding should also be considered when evaluating the feasibility of a research topic in computer science. It is important to ensure that there is sufficient funding to cover the cost of data collection, equipment, and analysis.

Timeframe: The timeframe for the research should be feasible and realistic. It is important to consider the time required for data collection, analysis, and write-up.

The second question is: How can a literature review be used to evaluate the feasibility of a PhD research topic in computer science?

A literature review is a critical analysis of the existing literature on a particular research topic. It is an essential component of any PhD research project in computer science, and it can be used to evaluate the feasibility of a research topic in the following ways:

Identify gaps in the literature: A literature review can help to identify gaps in the existing literature on a particular research topic. This can help to determine if there is a need for further research and if the proposed research topic is feasible.

Evaluate the existing research: It can help to evaluate the existing research in the area of interest. This can help to determine the feasibility of the research topic and identify any challenges or limitations.

Identify research methodologies: It also can help to identify research methodologies that have been used in previous research on the topic. This can aid in evaluating the methodology’s viability and highlighting any potential difficulties.

Determine the scope of the research: A literature review can help to determine the scope of the proposed research. This can help to determine if the research topic is feasible and if it can be addressed within the available resources and time frame.

Identify potential research questions: We can help to identify potential research questions that have not been addressed in previous research by using the literature review. It portrays an essential role in identifying the feasibility of a research topic.

Identify potential research contributions: A literature review can help to identify potential contributions that the proposed research can make to the existing body of knowledge. This can help to determine if the proposed research topic is feasible and if it can add value to the field of computer science.

The third question is What are the best practices for conducting a feasibility study for a PhD research topic in computer science?

When conducting a feasibility study for a PhD research topic in computer science, there are several best practices to follow. These include:

Define the research question: Clearly define the research question to ensure that the study is focused and targeted. The research question should be specific, relevant, and feasible.

Conduct a comprehensive literature review: Conduct a thorough literature review to identify the existing body of knowledge on the research topic. This can help to identify potential gaps in the literature and inform the design of the study.

Identify the research methodology: Identify the research methodology that will be used to answer the research question. This should be based on the research question and the available resources and expertise.

Identify the sample size: Identify the sample size required for the study. The sample size should be based on the research question and the available resources.

Identify the data collection methods: Identify the data collection methods that will be used to collect the data. This should be based on the research question and the available resources.

Consider ethical issues: Consider ethical issues such as data privacy, informed consent, and participant safety when designing the study. Ensure that the study is conducted in an ethical and responsible manner.

Pilot study: Conduct a pilot study to test the feasibility of the study design and identify any potential issues or challenges. This can help to refine the study design and ensure that the study is feasible.

Data analysis: Identify the data analysis methods that will be used to analyze the data. This should be based on the research question and the available resources and expertise.

Timeframe: Develop a realistic timeframe for the study. This should include time for data collection, data analysis, and write-up.

Consider resources: Consider the resources required for the study, including funding, equipment, and expertise. Ensure that the resources required for the study are available and feasible.

One of the most important questions is how to choose a PhD research topic. For that, you need to identify your interests, explore the literature, consult with the supervisors, consider the resources, focus on a specific research question and most importantly, ensure that the research topic aligns with your career goals.

Number four in this list is How can a pilot study be used to evaluate the feasibility of a PhD research topic in computer science?

A pilot study is a small-scale version of the proposed research study, conducted to test the feasibility of the research topic and refine the study design. When conducting a pilot study for a PhD research topic in computer science, it can be used to evaluate the feasibility in the following ways:

Refine the research question: A pilot study can help to refine the research question by identifying any issues or challenges that may arise during the study. This can help to ensure that the research question is specific, relevant, and feasible.

Test the research methodology: The study can help to test the research methodology to ensure that it is appropriate and feasible for the study. This can help to identify any problems with the research methodology and refine it if necessary.

Identify potential issues: It can assist in evaluating any potential problems or difficulties that could emerge during the investigation, such as issues with data collection or analysis. This can help to refine the study design and ensure that the study is feasible.

Identify the appropriate sample size: A pilot study can help to identify the appropriate sample size for the study. This can help to ensure that the sample size is appropriate for the research question and the available resources.

Test the data collection methods: The study can assist in testing the data gathering techniques to make sure they are suitable and practical for the investigation. By doing so, it will be easier to spot any problems or difficulties with the data collection techniques and make any necessary adjustments.

Test the data analysis methods: To make sure the data analysis techniques are relevant and viable for the study, a pilot study might help test them. By doing so, it will be easier to see any problems or difficulties with the data analysis techniques and make any necessary adjustments.

The fifth question is What are the ethical considerations to take into account when evaluating the feasibility of a PhD research topic in computer science?

When evaluating the feasibility of a PhD research topic in computer science, there are several ethical considerations that researchers need to take into account. These include:

Informed consent: Researchers should obtain informed consent from study participants, explaining the purpose and procedures of the study, potential risks and benefits, and the right to withdraw at any time.

Confidentiality and data privacy: They should ensure that the data collected is kept confidential and secure, and should follow appropriate data protection regulations and guidelines.

Risk assessment: The Researchers should conduct a risk assessment to identify any potential risks to the participants, the researcher, or the broader community. They should take steps to minimize these risks and ensure the safety of all involved.

Bias and fairness: They should ensure that their study design and data collection methods are fair and unbiased, avoiding any potential conflicts of interest.

Respect for human dignity and autonomy: Researchers should respect the dignity and autonomy of study participants, and ensure that their participation is voluntary and free from coercion.

Minimizing harm: The Researchers should take steps to minimize any harm to study participants, including physical, emotional, or social harm.

Responsible use of technology: They should ensure that any technology used in the study is used in a responsible and ethical manner and that any potential risks are identified and addressed.

Ethical approval: The Researchers should get ethical approval from the relevant authorities, such as an institutional review board or ethics committee, before conducting the study.

The sixth question is What are the potential challenges in conducting a feasibility study for a PhD research topic in computer science?

Conducting a feasibility study for a PhD research topic in computer science can be a complex process that involves various challenges.

Here are some potential challenges that researchers may face:

Identifying the scope of the research topic: It can be challenging to identify the scope of the research topic and determine its feasibility. Researchers need to be clear about their research questions and objectives and ensure that their research is original and contributes to the existing body of knowledge.

Access to resources: Conducting a feasibility study may require significant resources, such as time, funding, and access to equipment or software. Researchers may need to identify potential sources of funding or collaboration with industry partners to support their research.

Identifying appropriate research methods: Researchers need to choose appropriate research methods that can effectively address their research questions and objectives. It can be challenging to identify the most appropriate research methods, given the complexity of the research topic and the available resources.

Recruitment and retention of study participants: It can be challenging to recruit and retain study participants, especially for research topics that involve sensitive or complex issues. Researchers may need to develop effective recruitment strategies and ensure that their study procedures are ethical and respectful.

Ethical considerations: Researchers need to consider the ethical implications of their research, including issues related to informed consent, confidentiality, data privacy, and risk assessment. Researchers may need to obtain ethical approval from the relevant authorities before conducting their research.

Handling data: Collecting and analyzing data can be challenging, especially for research topics that involve large or complex datasets. Researchers may need to develop effective data management strategies and ensure that their data collection and analysis methods are accurate and reliable.

Potential for unexpected results: Researchers may encounter unexpected results or findings during their feasibility study, which may require them to adjust their research questions or objectives. Researchers need to be prepared to adapt their research to new information and findings as they arise.

But if you have still not chosen a research topic, you may get this question about how to choose a research topic for PhD in computer science. Follow all the steps described above, only consider current trends and challenges related to computer science and consider the collaboration opportunities.

Number seven in this list is What are the limitations of a feasibility study for a PhD research topic in computer science?

There are several limitations to consider when conducting a feasibility study for a PhD research topic in computer science. Some of these limitations include:

Limited data availability: Depending on the research topic, there may be limited data available to conduct a feasibility study. This can make it challenging to assess the feasibility of the research and make informed decisions about the research direction.

Unpredictable outcomes: Computer science research can often lead to unpredictable outcomes, making it difficult to accurately predict the feasibility of a research topic. This is particularly true for research that involves developing new technologies or techniques.

Time and resource constraints: Conducting a thorough feasibility study can require a significant amount of time and resources. PhD students may have limited time and resources available to conduct a comprehensive feasibility study, which can impact the accuracy of their findings.

Limited scope: Feasibility studies typically have a narrow scope and may not account for all factors that could impact the success of a research topic. This can limit the ability of PhD students to accurately assess the feasibility of their research.

Ethical and legal considerations: Research in computer science may involve ethical and legal considerations, such as data privacy and security. These considerations can impact the feasibility of a research topic and may need to be addressed before the research can proceed.

The final question in this list is How can the results of a feasibility study be used to inform the design and execution of a PhD research project in computer science?

The results of a feasibility study can provide valuable insights that can be used to inform the design and execution of a PhD research project in computer science.

Here are some ways in which these results can be used:

Identifying potential challenges: The feasibility study can help to identify potential challenges and limitations that may impact the design and execution of the research project. This can help the researcher to anticipate and plan for these challenges in advance.

Determining the scope of the research: Based on the results of the feasibility study, the scope of the research project can be defined. This can include identifying the specific research objectives, the target population, and the timeframe for the project.

Selecting research methods: The feasibility study can inform the selection of appropriate research methods based on the availability of data and resources. For example, if the feasibility study identifies a lack of available data, the research methods may need to include data collection through surveys, experiments, or interviews.

Identifying potential collaborators: The results of the feasibility study may identify potential collaborators who can contribute to the research project. This can include other researchers, industry partners, or organizations with relevant data or expertise.

So, all these questions will help you to develop a framework for evaluating the feasibility of a PhD Research topic in Computer Science. If this doesn’t, then you can comment below so that we can help you with that. Now, there are some important questions to know about.

Finally, what are the latest research topics in computer science for PhD? The latest research topics are being made on Artificial Intelligence, Edge computing, blockchain technology, autonomous system, data privacy and security, natural language processing, computational biology and finally augmented and virtual reality.

And finally, thank you so much for reading this article.

A Bad PhD Supervisor: Warning Signs you Must not Ignore as a Research Scholar

The most influential person in your academic life is your PhD supervisor. He plays a diverse and critical role in your PhD journey, that of a mentor, confidant and advisor throughout your PhD degree. Some warning signs you must never ignore when it comes to choosing a good advisor can help you from not making a blunder in getting on to your PhD journey.

His publication record isn’t remarkable:

Publications not only talk a lot about the command on research of the professor, they also help the professor in various ways such as in getting grants, earn tenure and also build their career. If you feel that your potential supervisor is not rich in his publications, this could be a red flag and you should be alert that this could significantly impact your publications during PhD.

The other scholars under him are not able to publish:

The PhD supervisor should be helping his students to publish. If the scholars do not have sufficient publications then it talks a lot about the supervisor and his ability to guide his students to do worthy research. If the senior scholars are not able to publish then there is strong chance that you will not be able to publish easily, either.

He discourages you to connect with his other scholars:

Its always helpful to talk to your supervisor’s other scholars. It helps you to carve your way forward and many times share common issues and concerns and find suitable solutions. If your potential supervisor discourages you to talk to his other scholars or is strict that you cannot interact with them , you must have your alarm bells ringing that there must be issues with his capabilities or his working style because of which he wants to avoid the interaction. In such a case think twice before getting him as your mentor.

What has been the tenure of his previous students who have graduated:

How long has it taken for the previous scholars to graduate and also for that matter, how many of them have graduated so far. If you feel that the supervisor you have under consideration, all his earlier scholars have taken a very long time to graduate or not many scholars have passed out from under him, it’s not suggestive to go ahead with him because its again a red flag on his potential to guide you in a regular manner to spearhead your research.

He is not approachable:

If the supervisor under consideration doesn’t respond to mails or avoids taking your calls, it might be difficult to work under him. Communication is an important element of supervisor and student relationship and the work cannot move forward at a your desired speed if the supervisor is non responsive. This is something you cannot gauge before you enrol yourself under him but in the first few months you
can judge the accessibility to him and consider switching your supervisor if this alerts you in the formative months itself.

There is a negative feedback about him in the academic fraternity:

Before you zero down on the supervisor, it’s important to take his feedback from other students in the campus or professors who have worked or interacted with him. If you get unanimous negative feedback about him from different sources it might conclude that you would also face difficulty to work under him. Issues could be pertaining to potential in research, communication, attitude or even integrity towards work.

You don’t connect with him in the preliminary interview :

Vibes play a very important role and with your supervisor , in the first interview itself you would get an idea about their personality and whether you would be able to connect with him. You must recognise and trust your instincts because your instincts would give you a good idea about him. If in the preliminary interaction, he seems disinterested in your ideas or gets angry or agitated towards you then you would surely find it difficult to adjust with such a person. Always keep in mind that it takes a couple of years to complete your
PhD and not having a pleasing personality in your supervisor can make the journey even more herculean.

They do not want to clarify the monetary funding specifics:

The funding that comes with the course, you must be explicit and sure about it right from the beginning. There is no better person than your supervisor who can do it for you. You need to have a clear idea about your stipend, how much research funds would be allocated to you and whether any other special fund eligibility criteria you are able to qualify. Even if your personality matches well with your supervisor, ultimately if the monetary part
remains unmet or there is misunderstanding on those grounds then it won’t feasible for you to concentrate on your research.

They extend extraordinary praise to you:

Some undeserving PhD supervisors have the habit to extend undeserving praise to their mentees in order to hide their own shortcomings. This is also one of the forms of mistreatment and sometimes its referred to as praise bombing. This might happen more in the beginning stage in order to lure you to join their research group and later once you are a part their behaviour and attitude may both flip over. Such incompetent PhD supervisors, may at a later
stage, when you fail in experiments or faulter in research, belittle you and humiliate you. Other extreme behaviours are signs of in competencies in the supervisor only. You can get a fair idea about such behaviour be interacting with senior students and their experience. Another way to judge the situation is to see the difference in their attitude towards you and other existing scholars, particularly if they are extremely sweet to you and you find them exhibiting a more harsh tone to towards the senior scholars.

They don’t give you the autonomy to work:

Research is not all about your own data collection and thesis writing. It is much more than that. You would want to get the exposure of going to conferences and networking with other researchers to better your prospects. A good supervisor is the one who encourages you and gives you the autonomy to carve your own path and find means to grow professionally and build better future prospects. The ones who intend to exploit you will stop you from attending conferences or meeting others unless every time you seek their permission.
They might feel that you will show more power against them once you get the exposure.

They side track you:

The journey of PhD is extremely daunting and suffocating in its own way. Scholars sometimes feel as victims of the situation. In such a situation sometimes insensitive and harsh supervisors may push students into isolation on the pretext of better productivity. They may curtail your interaction with other faculty and would want complete control over you. In such a situation all the effort of the scholar will go in keeping the supervisor happy rather than his own growth. Its most of the
times not possible to raise your voice also against a supervisor who exploits because it may result in backlashing or gaslighting.

He doesn’t show empathy towards your personal issues:

No one has a life without challenges and problems and personal ups and downs go hand in hand with professional journey. You would want a supervisor who is empathetic in nature but the red flag is when he tells you to keep personal stories at bay and gives no scope of accommodation here. Their only concern is your academic output and they don’t bother with the journey of your parallel life and its impact on your research work.

He scares you that PhD is going to a daunting journey:

Mentors have the prime responsibility to encourage and make the scholars trust their ability to cross through the journey of PhD. Everyone is aware that it’s not an easy journey but if your supervisor constantly makes you feel thar the experience is going to be a nightmare and very scary , he is losing track of his prime responsibility and his contribution in your journey is more negative than positive

He loads you with his own personal work:

More often than not PhD supervisors are looking for scholars who can run their personal errands or do odd jobs for them as it’s a part of their duty. They might even ask you to prepare their lectures and sometimes even baby sit their kids or do their groceries. It’s awkward but once you get into the trap there is more and more work getting on your shoulders. This attitude which is exploitative in nature and shows the self-centred approach of the supervisor is one of
the prominent red flags and you must change your mentor and switch to someone with more honest intentions towards your growth.

Way forward when you get stuck with a bad supervisor……

After being aware of all these issues and being very cautious in choosing your supervisor, if somewhere in the journey of your PhD you have complaints against your mentor, universities should have a mechanism in place to register complaints and find a solution in such a way that the course of the scholar is not impacted in any way.

A scholar places a lot of trust in the supervisor as well as the university when he enrols for a course that will take years to complete and a lot of money from his end. It’s the responsibility of the supervisor as well as the university to support, guide and provide feedback to the scholars and handle issues and concerns they have throughout the tenure.

If you do have an issue with your supervisor that you realise at a later stage of your PhD and you don’t know where to look for a solution, you must speak up for
support and resolving the matter at the grievance handling cell of the university. Remember to be assertive about your concerns. Making a formal complaint is surely going to impact your relationship with your supervisor and you must know this in advance.

Putting in a complaint against your supervisor must be the last resort to resolve the issues and only after you have failed at all other attempts to sort out the matter and you know that despite all efforts your relationship with the supervisor is broken down and there is no way now that you can complete your research work under his guidance.

Another important thing to remember is to keep records and of all your negative experiences with your supervisor with proofs if possible. The committee that sits to resolve your complaints would want all of that in order to validate the issues you have raised against your supervisor.

If possible, always gather testimonials from other scholars to ensure the committee that the problem doesn’t lie in a single student but it comes from the end of the supervisor. This of course may be the end of your journey with a bad guide and be prepared for some adjustments and realignment with a new allocated supervisor if it’s so decided by the grievance redressal committee.

You must check and verify the records of the new supervisor and be doubly sure of not getting into a difficult trap one more time as that would make the situation nearly impossible to resolve. Being calm, proactive and focused in your approach will work here. Don’t hold any personal grudges against the supervisor but rather focus on the issues more because ultimately the academic community is small and composite.

You may have instances in your professional journey where you may still have to come face to face with him in unavoidable circumstances. So, the relationship if it ends at all, should be on a graceful note with courtesies in place and room for mutual dignity and respect so that in all future interactions pleasantries can be exchanged, if not more.

﻿Solving the statistical juggle: Anova, Ancova,Manova, Mancova