sample chapter 3 in research

Methods Section: Chapter Three

The methods section , or chapter three, of the dissertation or thesis is often the most challenging for graduate students.  The methodology section, chapter three should reiterate the research questions and hypotheses, present the research design, discuss the participants, the instruments to be used, the procedure, the data analysis plan , and the sample size justification.

Research Questions and Null Hypotheses

Chapter three should begin with a portion that discusses the research questions and null hypotheses.  In the research questions and null hypotheses portion of the methodology chapter, the research questions should be restated in statistical language.  For example, “Is there a difference in GPA by gender?” is a t-test type of question, whereas “Is there a relationship between GPA and income level?” is a correlation type of question.  The important thing to remember is to use the language that foreshadows the data analysis plan .  The null hypotheses are just the research questions stated in the null; for example, “There is no difference in GPA by gender,” or “There is no relationship between GPA and income level.”

Research Design

The next portion of the methods section, chapter three is focused on developing the research design.  The research design has several possibilities. First, you must decide if you are doing quantitative, qualitative, or mixed methods research. In a quantitative study, you are assessing participants’ responses on a measure.  For example, participants can endorse their level of agreement on some scale.  A qualitative design is a typically a semi-structured interview which gets transcribed, and the themes among the participants are derived.  A mixed methods project is a mixture of both a quantitative and qualitative study.

Participants

In the research methodology, the participants are typically a sample of the population you want to study.  You are probably not going to study all school children, but you may sample from the population of school children.  You need to include information about the characteristics of the population in your study (Are you sampling all males? teachers with under five years of experience?).  This represents the participants portion of your methods section, chapter three.

Need help with your methods section?

Schedule a time to speak with an expert using the calendar below.

Instruments

The instruments section is a critical part of the methodology section, chapter three.  The instruments section should include the name of the instruments, the scales or subscales, how the scales are computed, and the reliability and validity of the scales.  The instruments portion should have references to the researchers who created the instruments.

The procedure section of the methods chapter is simply how you are going to administer the instruments that you just described to the participants you are going to select.  You should walk the reader through the procedure in detail so that they can replicate your steps and your study.

Data Analysis Plan

The data analysis plan is just that — how you are going to analyze the data when you get the data from your participants.   It includes the statistical tests you are going to use, the statistical assumptions of these tests, and the justification for the statistical tests.

Sample Size Justification

Another important portion of your methods chapter three, is the sample size justification.  Sample size justification (or power analysis) is selecting how many participants you need to have in your study.  The sample size is based on several criteria:  the power you select (which is typically .80), the alpha level selected (which is typically .05), and the effect size (typically, a large or medium effect size is selected).  Importantly, once these criteria are selected, the sample size is going to be based on the type of statistic: an ANOVA is going to have a different sample size calculation than a multiple regression.

• How it works

Chapter 3 – Dissertation Methodology (example)

Disclaimer: This is not a sample of our professional work. The paper has been produced by a student. You can view samples of our work here . Opinions, suggestions, recommendations and results in this piece are those of the author and should not be taken as our company views.

Type of Academic Paper – Dissertation Chapter

Academic Subject – Marketing

Word Count – 3017 words

Introduction

The current chapter presents developing the research methods needed to complete the experimentation portion of the current study. The chapter will discuss in detail the various stages of developing the methodology of the current study. This includes a detailed discussion of the philosophical background of the research method chosen. In addition to this, the chapter describes the data collection strategy, including the selection of research instrumentation and sampling. The chapter closes with a discussion on the analysis tools used to analyse the data collected.

Selecting an Appropriate Research Approach

Creswall (2013) stated that research approaches are plans and procedures that range from steps, including making broad assumptions to detailed methods of data collection, analysis, and interpretation. The several decisions involved in the process are used to decide which approach should be used in a specific study that is informed using philosophical assumptions brought to the study (Creswall 2013). Included in this are procedures of inquiry or research designs and specific research methods used for data collection, its analysis, and finally, its interpretation. However, Guetterman (2015); Lewis (2015); and Creswall (2013) argue that the selection of the specific research approach is based on the nature of the research problem, or the issue that is being addressed by any study, personal experiences of the researchers’, and even the audience for which the study is being developed for.

There are many ways to customise research approaches to develop an approach most suited for a particular study. However, the main three categories with which research approaches are organised include; qualitative, quantitative, and mixed research methods. Creswall (2013) comments that all three approaches are not considered so discrete or distinct from one another. Creswall (2013) states, “qualitative and quantitative approaches should not be viewed as rigid, distinct categories, polar opposite, or dichotomies” (p.32). Newmand and Benz (1998) pointed out that quantitative and qualitative approaches instead represent different ends on a continuum since a study “tends” to be more quantitative than qualitative or vice versa. Lastly, mixed methods research resides in the middle of the continuum as it can incorporate elements and characteristics of both quantitative and qualitative approaches. Lewis (2015) points out that the main distinction that is often cited between quantitative and qualitative research is that it is framed in terms of using numbers rather than words; or using closed-ended questions for quantitative hypotheses over open-ended questions for qualitative interview questions. Guetterman (2015) points out that a clearer way of viewing gradations of differences between the approaches is to examine the basic philosophical assumptions brought to the study, the kinds of research strategies used, and the particular methods implemented in conducting the strategies.

Underlying Philosophical Assumptions

An important component of defining the research approach involves philosophical assumptions that contribute to the broad research approach of planning or proposing to conduct research. It involves the intersection of philosophy, research designs and specific methods as illustrated in Fig. 1 below.

Figure 3.2-1- Research Onion (Source; Saunders and Tosey 2013)

Slife and Williams (1995) have argued that philosophical ideas have remained hidden within the research. However, they still play an influential role in the research practice, and it is for this reason that it is most identified. Various philosophical assumptions are used to construct or develop a study. Saunders et al. (2009) define research philosophy as a belief about how data about a phenomenon should be gathered, analysed and used. Saunders et al. (2009) identify common research philosophies such as positivism, realism, interpretivism, subjectivism, and pragmatism. Dumke (2002) believes that two views, positivism and phenomenology, mainly characterise research philosophy.

Positivism reflects acceptance in adopting the philosophical stance of natural scientists (Saunders, 2003). According to Remenyi et al. (1998), there is a greater preference in working with an “observable social reality” and that the outcome of such research can be “law-like” generalisations that are the same as those which are produced by physical and natural scientists. Gill and Johnson (1997) add that it will also emphasise a high structure methodology to allow for replication for other studies. Dumke (2002) agrees and explains

that a positivist philosophical assumption produces highly structured methodologies and allows for generalisation and quantification of objectives that can be evaluated by statistical methods. For this philosophical approach, the researcher is considered an objective observer who should not be impacted by or impact the subject of research.

On the other hand, more phenomenological approaches agree that the social world of business and management is too complex to develop theories and laws similar to natural sciences. Saunders et al. (2000) argue that this is the reason why reducing observations in the real world to simple laws and generalisations produces a sense of reality which is a bit superficial and doesn’t present the complexity of it.

The current study chooses positivistic assumptions due to the literature review’s discussion of the importance of Big Data in industrial domains and the need for measuring its success in the operations of the business. The current study aims to examine the impact that Big Data has on automobile companies’ operations. To identify a positive relationship between Big Data usage and beneficial business outcomes, the theory needs to be used to generate hypotheses that can later be tested of the relationship which would allow for explanations of laws that can later be assessed (Bryman and Bell, 2015).

Selecting Interpretive Research Approach

Interpretive research approaches are derived from the research philosophy that is adopted. According to Dumke (2002), the two main research approaches are deductive and inductive. The inductive approach is commonly referred to when theory is derived from observations. Thus, the research begins with specific observations and measures. It is then from detecting some pattern that a hypothesis is developed. Dumke (2002) argues that researchers who use an inductive approach usually work with qualitative data and apply various methods to gather specific information that places different views. From the philosophical assumptions discussed in the previous section, it is reasonable to use the deductive approach for the current study. It is also considered the most commonly used theory to establish a relationship between theory and research. The figure below illustrates the steps used for the process of deduction.

Data Collection

• confirmed or rejected
• Revision of theory

Based on what is known about a specific domain, the theoretical considerations encompassing it a hypothesis or hypotheses are deduced that will later be subjected to empirical enquiry (Daum, 2013). Through these hypotheses, concepts of the subject of interest will be translated into entities that are rational for a study. Researchers are then able to deduce their hypotheses and convert them into operational terms.

Hire an Expert Dissertation Chapter Writer

Orders completed by our expert writers are

• Formally drafted in an academic style
• Free Amendments and 100% Plagiarism Free – or your money back!
• 100% Confidential and Timely Delivery!
• Free anti-plagiarism report
• Appreciated by thousands of clients. Check client reviews

Justifying the Use of Quantitative Research Method

Saunders (2003) notes that almost all research will involve some numerical data or even contain data quantified to help a researcher answer their research questions and meet the study’s objectives. However, quantitative data refers to all data that can be a product of all research strategies (Bryman and Bell, 2015; Guetterman, 2015; Lewis, 2015; Saunders, 2003). Based on the philosophical assumptions and interpretive research approach, a quantitative research method is the best suited for the current study. Haq (2014) explains that quantitative research is about collecting numerical data and then analysing it through statistical methods to explain a specific phenomenon. Mujis (2010) defends the use of quantitative research because, unlike qualitative research, which argues that there is no pre-existing reality, quantitative assumes that there is only a single reality about social conditions that researchers cannot influence in any way. Also, qualitative research is commonly used when there is little to no knowledge of a phenomenon, whereas quantitative research is used to find the cause and effect relationship between variables to either verify or nullify some theory or hypothesis (Creswall 2002; Feilzer 2010; Teddlie and Tashakkori 2012).

Selecting an Appropriate Research Strategy

There are many strategies available to implement in a study, as evidenced from Fig. 1. There are many mono-quantitative methods, such as telephone interviews, web-based surveys, postal surveys, and structured questionnaires (Haq 2014). Each instrument has its own pros and cons in terms of quality, time, and data cost. Brymand (2006); Driscoll et al. (2007); Edwards et al. (2002); and Newby et al. (2003) note that most researchers use structured questionnaires for data collection they are unable to control or influence respondents, which leads to low response rates but more accurate data obtained. Saunders and Tosey (2015) have argued that quantitative data is simpler to obtain and more concise to present. Therefore, the current study uses a survey-based questionnaire (See Appendix A).

Justifying the use of Survey Based Questionnaire

Surveys are considered the most traditional forms of research and are used in non-experimental descriptive designs that describe some reality. Survey-based questionnaires are often restricted to a representative sample of a potential group of the study’s interest. In this case, it is the executives currently working for automobile companies in the UK. The survey instrument is then chosen for its effectiveness at being practical and inexpensive (Kelley et al., 2003). Due to the philosophical assumptions, interpretive approach, and methodological approach, the survey design for the current study is considered the best instrument in line with these premises, besides being cost-effective.

Empirical Research Methodology

Research design.

This section describes how research is designed to use the techniques used for data collection, sampling strategy, and data analysis for a quantitative method. Before going into the strategies of data collection and analysis, a set of hypotheses were developed.

Hypotheses Development

The current study uses a quantitative research approach, making it essential to develop a set of hypotheses that will be used as a test standard for the mono-method quantitative design. The following are a set of hypotheses that have been developed from the examination of the literature review.

H1- The greater the company’s budget for Big Data initiatives (More than 1 million GBP), the greater its ability to monetise and generate new revenues.

H2- The greater the company’s budget for Big Data initiatives (More than 1 million GBP) the more decrease in expenses in found.

H3- The greatest impact of Big Data on a company is changing the way business is done.

H4- Big Data integrating with a company has resulted in competitive significance.

H5- The analytical abilities of a company allows for achieved measurable results.

H6- Investing in Big Data will lead to highly successful business results.

H7- A business’s operations function is fuelling Big Data initiatives and effecting change in operations.

H8- The implementation of Big Data in the company has positive impacts on business.

This section includes the sampling method used to collect the number of respondents needed to provide information, then analysed after collection.

Sampling Method

Collis (2009) explains that there are many kinds of sampling methods that can be used for creating a specific target sample from a population. This current study uses simple random sampling to acquire respondents with which the survey will be conducted. Simple random sampling is considered the most basic form of probability sampling. Under the method, elements are taken from the population at random, with all elements having an equal chance of being selected. According to () as of 2014, there are about thirty-five active British car manufacturers in the UK, each having an employee population of 150 or more. This is why the total population of employees in car manufacturers is estimated to be 5,250 employees. The sample, therefore, developed used the following equation;

2  ×   (1 −   )

+(   2 × (1−  ) )  2

Where; N is the population size,  e  is the margin of error (as a decimal),  z  is confidence level (as a z-score), and  p  is percentage value (as a decimal). Thus, the sample size is with a normal distribution of 50%. With the above equation, a population of 5,250; with a 95% confidence level and 5% margin of error, the total sample size needed for the current equals 300. Therefore, N=300, which is the sample size of the current study.

The survey develops (see Appendix A) has a total of three sections, A, B, and C, with a total of 39 questions. Each section has its own set of questions to accomplish. The survey is a mix of closed-end questions that look to comprehend the respondents’ demographic makeup, the Big Data initiatives of the company, and the impact that Big Data was having on their company. The survey is designed to take no longer than twenty minutes. The survey was constructed on Survey Monkey.com, and an online survey provided website. The survey was left on the website for a duration of 40 days to ensure that the maximum number of respondents were able to answer the survey. The only way that the survey was allowed for a respondent is if they passed a security question as if they were working for an automobile company in the UK when taking the survey. Gupta et al. (2004) believe that web surveys are visual stimuli, and the respondent has complete control about whether or how each question is read and understood. That is why Dillman (2000) argued that web questionnaires are expected to resemble those taken through the mail/postal services closely.

Data Analysis

The collected data is then analysed through the Statistical Package for Social Science (SPSS) version 24 for descriptive analysis. The demographic section of the survey will be analysed using descriptive statistics. Further analysis of the data includes regression analysis. Simple regression analysis includes only one independent variable and one dependent variable. Farrar and Glauber (1967) assert that the purpose of regression analysis is to estimate the parameters of dependency, and it should not be used to determine the interdependency of a relationship.

Need a Dissertation Chapter On a Similar Topic?

Conclusions.

The chapter provides a descriptive and in-depth discussion of the methods involved in the current study’s research. The current study is looking towards a quantitative approach that considers positivism as its philosophical undertaking, using deductive reasoning for its interpretive approach, is a mono-quantitative method that involves the use of a survey instrument for data collection. The methodology chapter also provided the data analysis technique, which is descriptive statistics through frequency analysis and regression analysis.

Examples of results;

Question 8- Of these staff, are mostly working in or for your consumer-facing (B2C) businesses, your commercial or wholesale (B2B) businesses, or both?

Based on the illustration, nineteen (19) respondents indicated that 501-1000 employees are dedicated to analytics for both B2B and B2C. The category of using Big Data analytics for both B2B and B2C comprises the most agreement of respondents with 72 of 132 indicated.

The figure above represents the respondents’ answers to their automobile company’s plan for measuring Big Data’s success. Of the 132 participants, 44.70 per cent responded that the company is planning on using quantitative metrics associated with business performance to analyse if Big Data is actually successful. Another, 30.30 per cent indicated that their company was planning on using qualitative metrics tied to business performance. Using business performance to analyse the success of Big Data is coherent to the results of the literature review that indicated previous studies of doing such. As an automobile company, they need to know the results of using Big Data analytics, and that is only by using business performance indicators regardless of being qualitative or quantitative.

Fig. 4.3-6 portrays the response of participants in regards to actually achieving measurable results from Big Data. According to 68.18 per cent of respondents, the company that they worked for did indeed show measurable results from their investments in Big Data. However, 31.82 per cent indicated that there was indeed no measurable result in investing in Big Data.

Bryman, A., Bell, E., 2015. Business Research Methods. Oxford University Press.

Daum, P., 2013. International Synergy Management: A Strategic Approach for Raising Efficiencies in the Cross-border Interaction Process. Anchor Academic Publishing (aap_verlag).

Dümke, R., 2002. Corporate Reputation and its Importance for Business Success: A European

Perspective and its Implication for Public Relations Consultancies. diplom.de.

Guetterman, T.C., 2015. Descriptions of Sampling Practices Within Five Approaches to Qualitative Research in Education and the Health Sciences. Forum Qualitative Sozialforschung /

Forum: Qualitative Social Research 16.

Haq, M., 2014. A Comparative Analysis of Qualitative and Quantitative Research Methods and a Justification for Adopting Mixed Methods in Social Research (PDF Download Available).

ResearchGate 1–22. doi:http://dx.doi.org/10.13140/RG.2.1.1945.8640

Kelley, K., Clark, B., Brown, V., Sitzia, J., 2003. Good practice in the conduct and reporting of survey research. Int J Qual Health Care 15, 261–266. doi:10.1093/intqhc/mzg031

Lewis, S., 2015. Qualitative Inquiry and Research Design: Choosing Among Five Approaches.

Health Promotion Practice 16, 473–475. doi:10.1177/1524839915580941

Saunders, M., 2003. Research Methods for Business Students. Pearson Education India.

Saunders, M.N.K., Tosey, P., 2015. Handbook of Research Methods on Human Resource

Development. Edward Elgar Publishing.

DMCA / Removal Request

If you are the original writer of this Dissertation Chapter and no longer wish to have it published on the www.ResearchProspect.com then please:

Request The Removal Of This Dissertation Chapter

Frequently Asked Questions

How to write methodology chapter of a dissertation.

To write the methodology chapter of a dissertation:

• Describe research design & approach.
• Explain data collection methods.
• Justify chosen methods.
• Address limitations.
• Analyse data.
• Ensure replicability.

USEFUL LINKS

LEARNING RESOURCES

COMPANY DETAILS

• How It Works

• 249,814 Views

HOW TO WRITE CHAPTER THREE OF YOUR RESEARCH PROJECT (RESEARCH METHODOLOGY) | ResearchWap Blog

• Posted: Friday, 17 April 2020
• By: ResearchWap Admin

How To Write Chapter Three Of Your Research Project (Research Methodology)

Methodology In Research Paper

Chapter three of the research project or the research methodology is another significant part of the research project writing. In developing the chapter three of the research project, you state the purpose of research, research method you wish to adopt, the instruments to be used, where you will collect your data, types of data collection, and how you collected it.

This chapter explains the different methods to be used in the research project. Here you mention the procedures and strategies you will employ in the study such as research design, study design in research, research area (area of the study), the population of the study, etc.

You also tell the reader your research design methods, why you chose a particular method, method of analysis, how you planned to analyze your data. Your methodology should be written in a simple language such that other researchers can follow the method and arrive at the same conclusion or findings.

You can choose a survey design when you want to survey a particular location or behavior by administering instruments such as structured questionnaires, interviews, or experimental; if you intend manipulating some variables.

The purpose of chapter three (research methodology) is to give an experienced investigator enough information to replicate the study. Some supervisors do not understand this and require students to write what is in effect, a textbook.

A research design is used to structure the research and to show how all of the major parts of the research project, including the sample, measures, and methods of assignment, work together to address the central research questions in the study. The chapter three should begin with a paragraph reiterating the purpose of research.

It is very important that before choosing design methods, try and ask yourself the following questions:

Will I generate enough information that will help me to solve the research problem by adopting this method?

Method vs Methodology

I think the most appropriate in methods versus methodology is to think in terms of their inter-connectedness and relationship between both. You should not beging thinking so much about research methods without thinking of developing a research methodology.

Metodologia or methodology is the consideration of your research objectives and the most effective method  and approach to meet those objectives. That is to say that methodology in research paper is the first step in planning a research project work. 

Design Methodology: Methodological Approach                

Example of methodology in research paper, you are attempting to identify the influence of personality on a road accident, you may wish to look at different personality types, you may also look at accident records from the FRSC, you may also wish to look at the personality of drivers that are accident victims, once you adopt this method, you are already doing a survey, and that becomes your  metodologia or methodology .

Your methodology should aim to provide you with the information to allow you to come to some conclusions about the personalities that are susceptible to a road accident or those personality types that are likely to have a road accident. The following subjects may or may not be in the order required by a particular institution of higher education, but all of the subjects constitute a defensible in metodologia or methodology chapter.

 READ ALSO:  HOW TO WRITE EFFECTIVE RESEARCH PROJECT ABSTRACT

Methodology

A  methodology  is the rationale for the research approach, and the lens through which the analysis occurs. Said another way, a methodology describes the “general research strategy that outlines the way in which research is to be undertaken” The methodology should impact which method(s) for a research endeavor are selected in order to generate the compelling data.

Example Of Methodology In Research Paper :

• Phenomenology: describes the “lived experience” of a particular phenomenon
• Ethnography: explores the social world or culture, shared beliefs and behaviors
• Participatory: views the participants as active researchers
• Ethno methodology: examines how people use dialogue and body language to construct a world view
• Grounding theory*: assumes a blank slate and uses an inductive approach to develop a new theory

A  method  is simply the tool used to answer your research questions — how, in short, you will go about collecting your data.

Methods Section Of Research Paper Example :

• Contextual inquiry
• Usability study
• Diary study

If you are choosing among these, you might say “what method should I use?” and settle on one or more methods to answer your research question.

 READ ALSO:  HOW TO DEVELOP EFFECTIVE AND UNIQUE PROJECT TOPICS

Research Design Definition: WRITING A RESEARCH DESIGN

A qualitative study does not have variables. A scientific study has variables, which are sometimes mentioned in Chapter 1 and defined in more depth in Chapter 3. Spell out the independent and dependent, variables. An unfortunate trend in some institutions is to repeat the research questions and/or hypotheses in both Chapter 1 and Chapter 3. Sometimes an operational statement of the research hypotheses in the null form is given to set the stage for later statistical inferences. In a quantitative study, state the level of significance that will be used to accept or reject the hypotheses.

Pilot Study

In a quantitative study, a survey instrument that the researcher designed needs a pilot study to validate the effectiveness of the instrument, and the value of the questions to elicit the right information to answer the primary research questions in. In a scientific study, a pilot study may precede the main observation to correct any problems with the instrumentation or other elements in the data collection technique. Describe the pilot study as it relates to the research design, development of the instrument, data collection procedures, or characteristics of the sample.

Instruments

In a research study, the instrument used to collect data may be created by the researcher or based on an existing instrument. If the instrument is the researcher created, the process used to select the questions should be described and justified. If an existing instrument is used, the background of the instrument is described including who originated it, and what measures were used to validate it.

If a Likert scale is used, the scale should be described. If the study involves interviews, an interview protocol should be developed that will result in a consistent process of data collection across all interviews. Two types of questions are found in an interview protocol: the primary research questions, which are not asked of the participants, and the interview questions that are based on the primary research questions and are asked of the participants.

In a qualitative study, this is the section where most of the appendices are itemized, starting with letters of permission to conduct the study and letters of invitation to participate with the attached consent forms. Sample: this has to do with the number of your participants or subjects as the case may be. Analysis (how are you planning to analyze the results?)

ALSO READ:  TRENDING PROJECT TOPICS FOR FINAL YEAR STUDENTS AT A GLANCE

EFFECTIVE GUIDE AND METHODOLOGY SAMPLES

This chapter deals effectively with the research methods to be adopted in conducting the research, and it is organized under the following sub-headings:

• Research Design
• Area of Study

The population of the Study

• Sample and Sampling Techniques
• Instruments for Data Collection

The validity of the Instrument

Reliability of the Instrument

• Administration of the instruments
• Scoring the instruments

Method of Data Collection

Method of Data Analysis

Research Design:

This has to do with the structure of the research instrument to be used in collecting data. It could be in sections depending on different variables that form the construct for the entire topic of the research problems. A reliable instrument with a wrong research design will adversely affect the reliability and generalization of the research. The choice of design suitable for each research is determined by many factors among which are: kind of research, research hypothesis, the scope of the research, and the sensitive nature of the research.

Area of Study:

Research Area; this has to do with the geographical environment of the study area where the places are located, the historical background when necessary and commercial activities of that geographical area. For example, the area of the study is Ebonyi State University. At the creation of Ebonyi State in 1996, the Abakaliki campus of the then ESUT was upgraded to Ebonyi State University College by Edict no. 5 of Ebonyi State, 1998 still affiliated to ESUT with Prof. Fidelis Ogah, former ESUT Deputy Vice-Chancellor as the first Rector. In 1997, the Faculty of Applied and Natural Sciences with 8 departments was added to the fledging University, and later in 1998 when the ESUT Pre-Science Programme was relocated to Nsukka, the EBSUC Pre-Degree School commenced lectures in both Science and Arts in replacement of the former. This study focused on the students of the Business Education department in Ebonyi state university.

The population is regarded in research work as the type of people and the group of people under investigation. It has to be specific or specified. For example educational study teachers in Lagos state. Once the population is chosen, the next thing is to choose the samples from the population.

According to Uma (2007), the population is referred to as the totality of items or object which the researcher is interested in. It can also be the total number of people in an area of study. Hence, the population of this study comprised of all the students in the department of Business Education, Ebonyi State University which is made up of year one to four totaling 482. The actual number for the study was ascertained using Yaro-Yamane's formula which stated thus:

n   =        N

N is the Population

1 is constant

e is the error margin

Then, n   =         482

1+482(0.05)2

= 214.35 approximately 214

Sample and sampling technique:

It may not be possible to reach out to the number of people that form the entire population for the study to either interview, observe, or serve them with copies of the questionnaire. To be realistic, the sample should be up to 20% of the total population. Two sampling techniques are popular among all the sampling techniques. These are random and stratified random sampling techniques. (A). in Random Sampling, the writers select any specific number from a place like a school, village, etc. (B). In Stratified Random Sampling, one has to indicate a specific number from a stratum which could be a group of people according to age, qualification, etc. or different groups from different locations and different considerations attached.

Instruments for Data Collection:

This is a device or different devices used in collecting data. Example: interview, questionnaire, checklist, etc. instrument is prepared in sets or subsections, each set should be an entity thus asking questions about a particular variable to be tested after collecting data. The type of instrument used will determine the responses expected. All questions should be well set so as to determine the reliability of the instrument.

This has to do with different measures in order to determine the validity and reliability of the research instrument. For example, presenting the drafted questionnaire to the supervisor for scrutiny. Giving the questionnaire to the supervisor for useful comments and corrections would help to validate the instrument.

The test-retest reliability method is one of the simplest ways of testing the stability and reliability of an instrument over time. The test-retest approach was adopted by the researcher in establishing the reliability of the instrument. In doing this 25 copies of the questionnaire were administered on twenty-five selected respondents. After two weeks another 25 copies of the same questionnaire were re-administered on the same group. Their responses on the two occasions were correlated using Parsons Product Moment Correlation. A co-efficient of 0.81 was gotten and this was high enough to consider the instrument reliable.

Administration of the instruments:

Here, the writer states whether he or she administers the test personally or through an assistant. He also indicates the rate of return of the copies of the questionnaire administered.

Scoring the instruments:

Here items on the questionnaire or any other device used must be assigned numerical values. For example, 4 points to strongly agree, 3 points to agree, 2 points to disagree, and 1 point to strongly disagree.

Table of Analysis

The researcher collected data using the questionnaire. Copies of the questionnaire were administered by the researcher on the respondents. All the respondents were expected to give maximum co-operation, as the information on the questionnaire is all on things that revolve around their study. Hence, enough time was taken to explain how to tick or indicate their opinion on the items stated in the research questionnaire.

In this study, the mean was used to analyze the data collected. A four (4) point Likert scale was used to analyze each of the questionnaire items.

The weighing was as follows:

VGE—————- Very Great Extent (4 points)

GE—————– Great Extent (3 points)

LE—————– Little Extent (2 points)

VLE—————- Very Little Extent (1 point)

SA—————– Strongly Agree (4 points)

A——————- Agree (3 points)

D—————— Disagree (2 points)

SD—————- Strongly Disagree (1 point)

The mean of the scale will then be determined by summing up the points and dividing their number as follows with the formula:

Where; x= mean

f= frequency

X= Nominal value of the option

∑= summation

N= Total Number

Therefore, the mean of the scale is 2.5.

This means that any item statement with a mean of 2.50 and above is considered agreed by the respondents and any item statement below 2.5 is considered disagreed.

EDITORS SOURCE: How To Write Chapter Three Of Your Research Project (Research Methodology)

Tags: Research project, chapter three, methodology, project topics,

Project Categories

• AFRICAN LANGUAGES AND LINGUISTIC
• ACCOUNTING EDUCATION
• ACTUARIAL SCIENCE
• ADULT EDUCATION
• AGRICULTURAL ECONOMICS
• AGRICULTURAL EXTENSION
• ANIMAL SCIENCE
• ARCHITECTURE
• BANKING AND FINANCE
• BIBLICAL AND THEOLOGY
• BIOCHEMISTRY
• BREWING SCIENCE AND TECHNOLOGY
• BUILDING TECHNOLOGY
• BUSINESS ADMINISTRATION
• BUSINESS EDUCATION

SEE MORE PROJECT CATEGORIES

Copyright © 2024. All rights reserved researchwap.com - Free Project Topics, Research Materials, and Educational Resources

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

CHAPTER 3 - RESEARCH METHODOLOGY: Data collection method and Research tools

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Not Just Data : Analysing Visual Narratives of Children in Research and the Quest for “Micro-ethical” Moments

• Open Access
• First Online: 11 July 2024

Cite this chapter

You have full access to this open access chapter

• Maria Dardanou   ORCID: orcid.org/0000-0003-3247-158X 10 ,
• Ioanna Palaiologou   ORCID: orcid.org/0000-0003-0666-3959 11 &
• Sarika Kewalramani   ORCID: orcid.org/0000-0002-4937-3364 12  

Part of the book series: Perspectives in Cultural-Historical Research ((PCHR,volume 13))

463 Accesses

2 Altmetric

The chapter is based on a transnational small-scale research project in England, Norway, and Australia. The project aimed to examine how children are using IoToys at home with make-believe play and to investigate types of interactions/behaviours within their make-believe play in digital playscapes. Using digital methods for data collection based on visual methodologies principles, underpinning the synergy of cultural-historical theory and schema play concepts, we analyse digital episodes of children’s play with IoToys to demonstrate our analytical protocol. We discuss the complexities of visually capturing children’s lived experiences. Finally, we examine some of the challenges of analysing audio and visual recordings and conclude by suggesting that visual methodologies offer potentialities for rich data that capture the lived experiences of children but require to be approached as a cultural tool where the researchers should seek for signs, schemes, symbols and ethical “micro-moments”.

You have full access to this open access chapter,  Download chapter PDF

• Internet of toys (IoToys)
• Digital methods
• Visual methodologies
• Ethical “micro-moments”

1 Introduction

This chapter is based on a research project on the integration of Internet of Toys (IoToys) at home and in early childhood education (ECE) settings in three countries: England, Norway, and Australia. Aspects of the findings of this research have been reported previously in Kewalramani et al. ( 2020a , b , 2021a , b , c , 2023 ), Palaiologou et al. ( 2021 ).

The research employed qualitative methodology and focused mainly on visual methods, as will be explained below. The chapter has four sections. The first section explains the context of the research and the methods. The second discusses our theoretical conceptualisation and explains the synergy between sociocultural theory, cognition, and social ecology. The third section explains how we analysed our visual data. Finally, this chapter reflects upon the visual methodologies we used to discuss the benefits of using such an approach, as well as the challenges to conclude that when analysing digital data researchers should seek for signs, schemes, symbols and ethical “micro-moments”.

2 The Context of the Research

This research project started in 2018 and was completed in 2021. It employed qualitative methodology to examine how children are using IoToys at home and in ECE settings and to explore to what extent and how they can be integrated in pedagogy in England, Norway, and Australia. As IoToys are relatively new technological developments, it is important here to define them. These are tangible, physical toys connected with the internet which have intended pre-programme functions and represent either anthropomorphised characters or real/imaginary animals. Children can control these devices which offer opportunities for programming the interface to create interactive projects and actions involving children’s communication and expression (Palaiologou et al., 2021 ; Kewalramani et al., 2023 ).

As mentioned earlier, our research methods deployed visual methodologies. Visual researchers in the social sciences are using tools such as videos, photographs, drawings as modes of inquiry “modes of representation and modes of dissemination” (Mitchell, 2011 , p. xi). Visual research is producing immediate visual text or, as Fiske ( 1991 ) has described, primary text. However, central to this is a need to interpret and analyse the images which does not come without its challenges.

In our research, we used multi methods to collect data. The main methods were participant and non-participant observations, collection of videos with children’s activities, photographs and photovoice videos that parents and ECE educators were sending us that they thought would be of interest in our research. It is important to say here that we did not aim to have cross-cultural research, thus the methods of data collection varied among our countries. The focus of our research was to understand how children interact with haptic (involving sound, digital touch, movement) technology at home and in ECE settings, and how this technology can be integrated to enhance children’s development and learning. The analysis of data subsequently followed a commonly agreed protocol and was derived from our theoretical lenses.

At this point, we have to explain that our project was affected by the outbreak of the COVID-19 crisis as data collection was suspended. In England and Norway data collection stopped in March 2020 but in Australia data collection continued via Zoom. In England parents carried on sending short videos of their children in the first phase of the lockdowns (March 2020–June 2020), however after a while and with the continuing nature of this crisis, they stopped and it was impossible to resume data collection after the lockdowns (the national lockdown finished 19th July 2021). In Norway, during COVID-19 the structure of dividing groups changed, and the groups of children wit one educator became smaller (with one educator for a group of 4–5 children while previously 3 educators for 18 children). This temporary form/structure did not offer opportunities for data collection as this would have added an extra demand on the educators. In Australia, during the COVID-19 pandemic, the data collection pivoted to remote data collection methods. These involved data via children’s live zoom-based play and conversations with the researcher where the adult was the ‘silent partner’ during the live play session (occurred once a week staggered around totalling 10 weeks in 2020–2021). In negotiation with the educators and parents, observations at home and in EC settings were gathered, but the nature of the observations varied across contexts. Parents and educators submitted multimedia (through a private WhatsApp group) messages (pictures, videos, short written reflections from parents) of children’s play in the home. For the EC settings, the researcher provided remotely recorded stories involving empathy-based scenarios involving the robot or the digital game characters that were used as inquiry starters for the by the educators in their own EC setting. Hence the data also involved a combination of narrative observations from the researcher and self-submitted video.

As discussed elsewhere in the book, the pandemic brought dramatic changes in our everyday life affecting directly educational research with children at all levels and, across the world. There was a rapid move to use online tools and, in our research, we needed to develop online practices relying as ethically possible on visual data only. In a crisis individuals or group of people are impacted physically, emotionally, socially, cognitively, spiritually as it brings a disequilibrium to the normal daily routines (Male et al., 2024 ). In line with the ideology of the book that crisis offers opportunities for developing other possible ways and tools, for our project it offered the opportunity to utilise digital methods and examine in depth ways of not only collecting data but analysing these digital data with multimodal lenses as it will be discussed later.

3 Theoretical Conceptualisation: Vygotsky and Piaget: Beyond Dichotomies Seeking for Harmonies

In line with the aims of this book, we view play as a complex construct that can be studied by many disciplines such as psychology, sociology, anthropology. Equally trying to capture and analyse children’s lived experiences in research comes with complexities especially when digital data tries to document and interpret these experiences. As will be explained below, in order to understand these complexities, our theorisation is seeking to harmonise the psychological dimensions of play within children’s social and cultural contexts. Thus, as our aim was to examine children’s play in the digital age, with emphasis on IoToys, our theoretical conceptualisation is based on synergy between psychological theories and social ecological theories of play.

Building on previous work from our research (Palaiologou et al., 2021 ), we developed synergistic lenses between the work of Vygotsky ( 1978 ) and Piaget ( 1977 ). Although we do not ignore the differences between Piaget and Vygotsky, we aligned with the work of Glassman ( 1994 ) who argued “that whilst Piagetian and Vygotskian psychology might have epistemological tensions due to different ideas, actually these differences are the key to help us to understand child cognitive development” (Palaiologou et al., 2021 ). In that sense we argue that both are examining the internal psychological functions (e.g., the role of thought, attention, object representation, imagination, symbolism) that leads to play. Both view that intellectual development occurs as a sequence of hierarchical levels. Piaget in his own words explains: “we do in fact find, in the analysis of forms of social equilibrium, these same three structures […] [just as the] cognitive mechanisms in children involve three distinct systems” (Piaget, 1995 , pp. 56, 279). Similarly, Vygotsky ( 1994 , p. 216) suggests “Development consists in three intrinsic stages”. Nevertheless, both acknowledged that “The stages of development are far from being just the manifestation of internal organic maturation” (Piaget, 1995 , p.296).

We must, therefore, distinguish the main lines in the development of the child’s behaviour. First, there is the line of natural development which is closely bound up with the process of general organic growth and maturation (Vygotsky, 1994 , p.57).

Despite his critics, Piaget ( 1971 , p.155; 1986 , p. 312) explicitly noted the open nature of development of knowledge. Moreover, despite the dominant ideas in the English-speaking literature that the Piagetian work ignored the social and cultural variables of development, both endorsed these:

Human intelligence is subject to the action of social life at all levels of development from the first to the last day of life. (Piaget, 1995 , p. 278)

The entire history of the child’s psychological development shows us that, from the very first days of development, its adaptation to the environment is achieved by social means (Vygotsky, 1994 , p. 116).

In Vygotsky’s work it is obvious that his ideas moved from social to cultural dimensions of development. Equally Piaget ( 1995 , pp. 41–47) showed a commitment with due attention to social relationships and the cultural availability of knowledge and values (Piaget, 1955 ).

Thus, in our work, we draw upon Vygotsky’s view that play and its influence on child development conveys accurately that the child learns to act in a mental, rather than an externally visible situation, relying “not on motives and incentives supplied by external things” (Vygotsky, 1966 /2016). Thus, play is “the source of development” (Vygotsky, 1978 , p. 138) of “the most authentic, truest creativity” (Vygotsky, 1930/2004 , p. 11); something that is instrumental in furthering children’s thinking and fostering the development of ideas that “bridges the gap between real events in the changing world and the imagination within one’s head” (Preissler, 2006 , p. 233). Consequently, “we can view play from a holistic perspective that captures it as a genuinely social activity—which means not only an interactive activity but also a cultural and imaginative one” (Nikolopoulou, 1993 , p. 13).

As Vygotsky ( 1976 , 1978 ) has emphasised the importance of play in children’s development, we cannot ignore that play is “an internal mental function for children to explore their world and the objects around them defined by a set of broad terms encompassing motivational, cognitive, social and emotional aspects of behaviour and psychology” (Neale et al., 2017 , p.4). Similarly, Piaget ( 1977 , 1985 ) argued that as children develop through stages, they construct schemata (= “a cohesive, repeatable action sequence possessing component actions that are tightly interconnected and governed by a core meaning” (Piaget, 1952 , p. 7) to make meaning of the world around them through their experiences and central to this was play.

Thus, in this research, we view play as “an accomplished union of mental functions and sociocultural world” (Palaiologou, 2017 , p. 1262) that shapes children’s lives (e.g . Prout, 2010 ; Rogers, 2010 ) and is context specific and inseparable from the social world which drives the play experience (Brooker et al., 2014 ). In that sense, play cannot be examined in isolation from the developmental, social, cultural and ecological contexts of childhood, nor “be broken into parts for separate consideration” but needs to be seen as “moulded into one piece” (Alanen, 1988 , p. 54). Therefore, in our research we examine children’s play in a way that attempts “to find a relationship to both children’s own activity [internal mental functions] and to the social processes which shape and constrain [or extend] children’s lives” (Prout & James, 1997 , p. 30). In doing so we seek to create a synergy between the developmental approaches to play as mental function and the ecological views of the child as part of a “ moulded” social digital landscape.

As children are part of multiple social landscapes, we cannot serve research effectively by decontextualising play. Thus, we draw upon social ecology theory (Bookchin, 1993 , 1995a , 1995b ) to understand what happens at an internal and social level when children are interacting with IoToys. Social ecology examines ecological phenomena within society that “require a way of thinking that recognises that ‘what-is’ as it seems to lie before our eyes is always developing into ‘what -it-is-not’, that is engaged in a continual self-organising process in which past and present, seen as a richly differentiated but shared continuum, give rise to a new potentiality for a future, ever richer degree of wholenes s [original emphasis]” (Bookchin, 1993 , p.5). It seeks to unify the study of natural (human development) and the social world “in a comprehensive theory that sees human beings and the natural world as potentially complementary, not antagonistic” (Best, 1998 , p. 335). In that sense it goes beyond just studying phenomena within interface systems and is concerned with the holistic richness of them (Bookchin, 1995a ). We position this project within social ecology as embedding a complementarity of psychological research and sociological research, creating synergy to examine “ what -is ” when children play with IoToys. We view social ecology as stressing the need for embodying complementarity between the internal (mental functions) and the social worlds that will give active meaning to the wholeness of play (digital and non-digital) across children’s social landscapes. Such a lens allows us to examine children’s play across digital and non-digital within the social and cultural context that this play takes place. Moreover, it helps to draw from the psychological dimensions of play as discussed by Vygotsky and Piaget to analyse the characteristics of play and within the social cultural lenses to locate this play in its social and cultural context. Thus, we built on this in order to offer us indicators and indications of how we can approach our digital data, as will be demonstrated below.

4 Data Analysis: Seeing the Unseen

In our research we sought to utilise a multimodal approach to analysis, emphasising the importance of using critical lenses that align with our theoretical conceptualisation. For example, a multimodal analytical approach allows researchers to consider multiple modes (speech, sound, text, digital touch, movement), wherein children’s play is transformative as children move back and forth within their physical and digital play spaces (Edwards, 2021 ).

As our project was multi-dimensional to answer each research question several analytical methods were used. Preliminarily we used thematic analysis (Braun & Clarke, 2021 , 2022 ). However, due to the nature of the research we also employed either an inductive or a deductive approach, based on our theoretical conceptualisation, so we could understand the psychological and social dimensions of children’s interactions with IoToys. In cases where we wanted to analyse explicitly the content of the play, we used semantic and latent approaches (see Palaiologou et al., 2021 ). As mentioned already, our data was mainly visual either in the form of videos or photovoice videos from parents and photographs.

Visual data does not come without its challenges. It does require interpretation, thus we had to find ways the ensure that we interpret the data faithfully and ethically. We built on Palaiologou’s ( 2019 ) suggestion that in order to analyse visual data we do need to set in advance indicators (coding) and indications (themes).

In line with our conceptualisation, we drew upon the psychological characterises of play as our indicators, these included:

Creation of imaginary situations with the use of objects (IoToys);

Use of objects to attribute/project properties to other artefacts;

Projecting imitation schemata onto other objects;

Transformation of the objects into symbolic actions;

Actions to represent something else rather than the intended functions of the objects;

Creation of rules;

Meaning-making;

Child takes the role of the other—imitation (see Palaiologou et al., 2021 ).

The second stage was to focus on indications that evolved around the physical, social, emotional, linguistic and cultural-environmental dimensions of play. The example below demonstrates the process of the data analysis.

Example of data and analytical process:

This is a 1:02 min photovoice video that the parents sent to us (English data). The parents did not put any narrative as they wanted to leave the researcher to hear the children’s dialogues, they just explained the context, that the boy who is older is helping his cousin who is younger to play with OSMO.

In this video (Fig. 10.1 presents screenshots of the video), we see that the older boy helps the younger girl and guides her using his fingers and language to help the girl to put the pieces in the right place. Sometimes he takes the piece and places it in the right place using language to explain what needs doing. The girl is showing attention and follows the instructions given to her by the boy. When the puzzle is completed, alongside the celebrating music from the OSMO, the boy claps his hands as an indication that he applauds her efforts and praises the girl for completing it. They both smile (see Fig. 10.2 , Table 10.1 ).

Two children play with OSMO

Both the children celebrate the completion of the puzzle

Reflecting our theoretical conceptualisation, we extended our interpretation to conclude that in this play episode we see a flow of play where through problem solving (where the pieces need to be used to create a house), negotiation, social interaction between the two children, the boy supported the young girl as most experienced peer (zone of proximal development) to construct a new schema to understand the environmental culture of this particular play (OSMO and its rules) which enables the girl to feel achievement (completion of the puzzle), control (changed the image of the puzzle), develops sense of self -esteem and worth (the boy applauded her and she had a big smile at the end). Our analysis was communicated with the family, so the family could engage with our analytical protocol and, most importantly, to ensure that our narrative of the video relates to their narrative. Thus, following member checking process (Ary et al., 2010 ), to ensure truthfulness of our interpretation.

5 Discussion: The Case for “Micro-ethical” Moments in Visual Research

As can be seen from the above example, analysing visual data requires the development of strategies that are not different from written data. However, the visual data offers rich data as it captures embodied language that written text cannot always capture. It also offers researchers the “wholeness” of the context as it does not ignore the social/cultural aspects of the environment and the social-emotional interactions that occur within it. Nevertheless, visual data needs to be treated with caution as images do not always tell us/ represent the reality because in research “the invisible matters” (Wyly, 2010 , p. 499). As visual methodologies are complex because the lived experiences of children cannot only be represented only by images, Wyly ( 2010 , pp. 505–507) suggest three conditions:

Conditions of possibility (seeking for the unseen thus visually unknown contexts of the image and image taking process);

Displacement (an image might be removed from its contextual history);

Power of representation (who chooses and is in control of the images).

Thus, we conclude when visual methodologies are used in research with children, the methodology needs to be considered differently, compared to other methodologies. We propose that researchers using visual methodologies need to be guided by the cultural context of the research focus, its historicity and show sensitivity to it. We propose that in the visual research landscapes in early childhood (but and beyond), the focus should be on these sensitivities and these to be considered as micro-ethical moments, where researchers’ judgements are attuned to cultural historical landscapes of children’ s lives. It should be acknowledged that in any interpretation of digital data, no matter how rich and insightful can be, these moments of children’s lives may go unrecognised. Thus, researchers in the digital analysis stage should be intuitive to recognise the micro-ethical moments that emerge.

Core to visual research are the ethical considerations, especially when it comes to anonymity and confidentiality. As children’s images are represented in visual data, researchers when disseminate findings tend to “anonymise” the images of children with covering or blurring their faces. However, this raises the questions as to what extent children want to be anonymised in this way. Anonymised images, instead of eliciting children’s voices, can dehumanise children. As visual data is rich linguistically, physically, socially and emotionally, in anonymising images we run the risk to make the data silent, manipulates the data (e.g., displacement) so it speaks to an audience distant from the children and in some cases, it can lead to unrecognisable and meaningless data as in the examples below:

Similar challenges can be raised regarding confidentiality as in our attempt to align with it we run the risk of depersonalising the lived experiences of young children, rather than capturing them. There are cases where the ethical positioning, protocols, and the ethics of care of the researcher are against the participants’ wishes, the contextual features of the data and children’s empowerment in the research as the example below illustrates (Fig. 10.3 ).

Anonymity vs dehumanization

In the above example with the boy and the girl once the case study was written for dissemination purposes and we showed the family and the children how we had interpreted the photovoice video. The parents were happy, but the boy asked why we did not use his real name and he did not like how his face was covered. We explained the reasons however, the boy insisted to use his real name and not cover his face as he stated, “ I am proud that I helped my cousin and I want everyone to know”. It can be seen these created tensions between our ethical protocol, but then if we had not respected that child’s wishes it would place a question on the participatory nature of our research as well as children’s agency.

Such tensions create challenges at philosophical and practical level. At the philosophical level visual research with young children can result in “silent” ethical conduct, regulated by a self-interest in the investigation. This sums up researchers’ duties and obligations to avoid pursuing an axiomatic argumentation of the ethical terrain of visual research. At a practical level, ethical choices depend on individual’s morality and ideology (such as children’s participation as a frame of reference) and intuitional morality (such as regulations, ethical codes and committees) as well as legal requirements. Thus, we argue that visual research with children should place emphasis on “micro-ethical” moments: i.e. child in context (procedural ethics vs situated ethics) and their voices and choices heard and acknowledged.

Consequently, in reflecting on our theoretical conceptualisation that aimed to synergise psychological and philosophical theories, we propose the following reflective lenses when visual research with young children is considered:

Each visual researcher should reflect on the adopted actions of pursuing their own research investigation that on the surface appear ethical as a ‘betterment’ way of children’s involvement ( rational egoism =  it is rational to act in children’s interest);

In striving to act ethically, the issue of consent, anonymity and confidentiality should take a pragmatic approach, rather than epistemic one, that does not ignore the array of issues that shape axiomatic ethical dilemmas on visual research with young children ( psychological egoism =  researchers can only act in their self-interest);

Each visual research should seek to match ethical criteria set by institutional regulatory bodies and committees with the axiomatic ethical challenges emerging from visual research that does not always sit comfortably with institutional ethical protocols ( ethical egoism =  researchers ought to act in a way that will benefit the children)

6 Conclusions

To conclude, visual research with young children has reached an important point where the legal and institutional requirements for informed consent, anonymity and confidentiality are determining decisions about ethics. In line with cultural-historical theory, we propose that the ethical terrain of visual research ought to be determined by invariable different principles that are depending on context and situational actions . Informed consent in visual research should be constantly negotiated for each visual depiction with the children if we are to represent children’s lives in research.

Axiomatically, it is paradoxical to assure anonymity and confidentiality in visual depictions. Instead, visual research should be accommodated in a context specific visual ethic , moving away from the pragmatic margins of institutional ethical protocols of what is “good” practice (ethical egoism). We propose that ethical visual research should be context specific and should be managed and regulated by reference to reasons appropriate to that context. It should be concerned with an axiomatic understanding of what it is required conducting ethically sound visual research with young children. Finally, this has implications for institutional ethical protocols as they should be negotiated in participatory ways so they can be flexible to focus on micro-ethical moments that lead to ethical decision making and reflect the epistemic positions and axiomatic challenges of visual research with children. To put it simply, in visual research we should publicly debate ethical anxieties and dilemmas and not be haunted by them.

Alanen, L. (1988). Rethinking childhood. Acta Sociologica, 31 (1), 53–67. https://doi.org/10.1177/000169938803100105

Article   Google Scholar  

Ary, D., Jacobs, L. C., Sorensen, C., & Razavieh, A. (2010). Introduction to research in education . Engage Learning.

Google Scholar  

Best, S. (1998). Murray Bookchin’s theory of social ecology: An appraisal of the ecology of freedom. Organization and Environment, 11 (3), 334–353.

Bookchin, M. (1993). What is social ecology? (essay). In M. E. Zimmerman (Ed.), Environmental philosophy: From animal rights to radical ecology (collection of essays with the permission of authors). Prentice Hall.

Bookchin, M. (1995a). The philosophy of social ecology . Black Rose.

Bookchin, M. (1995b). Re-enhancing humanity: A defence of the human spirit against anti-humanism, misanthropy, mysticism and primitivism . Casell.

Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. .

Braun, V., & Clarke, V. (2022). Conceptual and design thinking for thematic analysis. Qualitative Psychology, 9 (1), 3–26. https://doi.org/10.1037/qup0000196

Brooker, L., Blaise, M., & Edwards, S. (2014). The Sage handbook of play in early childhood . Sage.

Book   Google Scholar  

Edwards, S. (2021). Digital play and technical code: What new knowledge formations are possible? Learning, Media and Technology, 46 , 306–319. https://doi.org/10.1080/17439884.2021.1890612

Fiske, J. (1991). Understanding popular culture . MA Unwin.

Glassman, M. (1994). All things being equal: The two roads of Piaget and Vygotsky. Developmental Review, 14 (2), 186–214. https://doi.org/10.1006/drev.1994.1008

Kewalramani, S., Palaiologou, I., & Dardanou, M. (2020a). Children’s engineering design thinking processes: The magic of the ROBOTS and the power of BLOCKS (electronics). Eurasia Journal of Mathematics, Science and Technology Education, 16 (3), em 1830. https://doi.org/10.29333/ejmste/113247

Kewalramani, S., Palaiologou, I., Arnott, L., & Dardanou, M. (2020b). The integration of the internet of toys in early childhood education: A platform for multi-layered interactions. European Early Childhood Research Association Journal, 28 (2), 197–213. https://doi.org/10.1080/1350293X.2020.1735738

Kewalramani, S., Kidman, G., & Palaiologou, I. (2021a). Using Artificial Intelligence (AI)-interfaced robotic toys in early childhood settings: A case for children’s inquiry literacy. European Early Childhood Education Research Journal, 29 (5), 652–668. https://doi.org/10.1080/1350293X.2021.1968458

Kewalramani, S., Palaiologou, I., Dardanou, M., Allen, K., & Phillipson, S. (2021b). Using robotic toys in early childhood education to support children’s social and emotional competencies. Australian Journal of Early Childhood, 46 (4), 355–369. https://doi.org/10.1177/1836939121105666

Kewalramani, S., Palaiologou, I., Siraj-Blatchford, J., & Dardanou, M. (2021c). Can robots support social and emotional development? In ED Magazine Issue 3 by STEMEDmagazine, https://issuu.com/stemedmagazine/docs/final_stem_ed_magazine_issue_3/s/12514157 (Available at http://thespoke.earlychildhoodaustralia.org.au/21650-2/

Kewalramani, S., Palaiologou, I., & Dardanou, M. (2023). The integration of internet of toys in early childhood education . Routledge and Taylor and Francis.

Male, T., Palaiologou, I., & Ince, A. (2024). Leadership behaviours during crises: Cases from early childhood education and care in England. European Early Childhood Education Research Journal , 1–13. https://doi.org/10.1080/1350293X.2024.2329617

Mitchell, C. (2011). Doing visual research . SAGE.

Neale, D., Clarkson, K., Georgieva, S., Debatas, H., Wass, S., & Leong V. (2017). Towards a neuroscientific understanding of play: A neuropsychological coding framework for analysing infant-adult play patterns. https://doi.org/10.1101/202648 . https://www.biorxiv.org/content/early/2017/10/13/202648

Nikolopoulou, A. (1993). Play, cognitive development, and the social world: Piaget, Vygotsky and beyond. Human Development, 36 , 1–23.

Palaiologou, I. (2017). Assessing children’s play: Reality or illusion? The case of early years foundation stage in England. Early Child Development and Care, 187 (8), 1259–1272. https://doi.org/10.1080/03004430.2017.1295233

Palaiologou, I. (2019). Child observation: A guide for students of early childhood. SAGE.

Palaiologou, I., Kewalramani, S., & Dardanou, M. (2021). Make-believe play with internet of toys: A case for multimodal playscapes. British Journal of Educational Research . (Special issue: Digitalisation in early childhood: approaches, effects and critical views) , 52 (6), 2100–2117. https://doi.org/10.1111/bjet.13110

Piaget, J. (1952). Child’s conception of the world . Routledge and Kegan Paul.

Piaget, J. (1955). Commentary on Vygotsky’s criticism. New Ideas in Psychology, 13 , 325–340.

Piaget, J. (1964). The early growth of logic in the child (Routledge and Kegan Paul, 1964) [ La genèse des structures logiques elementaires (1959)]. (1971). Biology and knowledge . Edinburgh University Press.

Piaget, J. (1977). Intellectual evolution from adolescence to adulthood (Cambridge University Press, 1977) [ L’evolution intellectuelle entre l’adolescence et l’age adulte (1970)].

Piaget, J. (1985). The equilibration of cognitive structures: The central problem of intellectual development (University of Chicago Press, 1985) [ L’equilibration des structures cognitives (1975), previously translated as the development of thought: Equilibration of cognitive structures (1977)].

Piaget, J. (1986). Essay on necessity. Human Development, 29 , 301–314.

Piaget, J. (1995). Sociological studies . Routledge.

Preissler, M. A. (2006). Play and autism: Facilitating symbolic understanding. In D. Singer, K. Golninkoff, & K. Hirsh-Pasek (Eds.), Play=learning: How play motivates and enhances children’s cognitive and social–emotional growth (pp. 231–250). Oxford University Press.

Chapter   Google Scholar  

Prout, J. (2010). The future of childhood . Routledge.

Prout, A., & James, A. (1997). A new paradigm for the sociology of childhood? Provenance, promise and problems. In A. James & A. Prout (Eds.), Constructing and reconstructing childhood: Contemporary issues in the sociological study of childhood (2nd ed., pp. 7–33). Routledge Falmer.

Rogers, S. (2010). Powerful pedagogies and playful resistance: Role play in the early childhood classroom. In L. Brooker & S. Edwards (Eds.), Engaging play (pp. 152–165). Open University.

Vygotsky, L. (1930/2004). Imagination and creativity in early childhood. Journal of Russian and East European Psychology, 42 (1), 7–97.

Vygotsky, L. S. (1966/translation 2016). Play and its role in the mental development of the child. Soviet Psychology, 5 , 6–18. / International Research in Early Childhood Education , 7 (2), 2016.

Vygotsky, L. (1976). Play: Its role in the development and evolution. In J. Bruner, A. Jolly, & K. Sylva (Eds.), Play: Its role in development and evolution (pp. 537–554). Basic Books.

Vygotsky, L. (1978). Mind in society: The development of higher process . Harvard University Press.

Vygotsky, L. (1994). The Vygotsky reader . Blackwell.

Wyly, E. (2010). Things pictures don’t tell us: In search of Baltimore. City, 14 (5), 497–528.

Download references

Author information

Authors and affiliations.

Department of Teacher Education and Pedagogy, UiT The Arctic University of Norway, Tromsø, Norway

Maria Dardanou

School of Education (Psychology in Education), University of Bristol (UK), Bristol, UK

Ioanna Palaiologou

Swinburne University of Technology, Department of Education, School of Social Sciences, Media, Film and Education, Hawthorn, VIC, Australia

Sarika Kewalramani

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Maria Dardanou .

Editor information

Editors and affiliations.

Faculty of Education, Peninsula Campus, Monash University, Frankston, VIC, Australia

Marilyn Fleer

Faculty of Education, Aristotle University of Thessaloniki, Thessaloniki, Greece

Glykeria Fragkiadaki

KINDKNOW Research Centre, Western Norway University of Applied Sciences, Bergen, Norway

Elin Eriksen Ødegaard

Faculty of Education, Monash University, Frankston, VIC, Australia

Prabhat Rai

KINDKNOW Research Centre, Western Norway University of Applied Sci, Bergen, Norway

Alicja R. Sadownik

Rights and permissions

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions

Copyright information

© 2024 The Author(s)

About this chapter

Dardanou, M., Palaiologou, I., Kewalramani, S. (2024). Not Just Data : Analysing Visual Narratives of Children in Research and the Quest for “Micro-ethical” Moments. In: Fleer, M., Fragkiadaki, G., Ødegaard, E.E., Rai, P., Sadownik, A.R. (eds) Cultural-historical Digital Methodology in Early Childhood Settings. Perspectives in Cultural-Historical Research, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-031-59785-5_10

Download citation

DOI : https://doi.org/10.1007/978-3-031-59785-5_10

Published : 11 July 2024

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-59784-8

Online ISBN : 978-3-031-59785-5

eBook Packages : Education Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

CHAPTER THREE 3.0 RESEARCH METHODOLOGY 3.1 Introduction

• University of Arusha

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

PRESCRIBING PRACTICES AND CLINICAL IMPACT OF NEXT GENERATION SEQUENCING IN ROUTINE PRACTICE IN SOLID TUMORS – REAL WORLD EXPERIENCE IN LMIC

• Find this author on Google Scholar
• Find this author on PubMed
• Search for this author on this site
• ORCID record for nidaezehra zehra
• For correspondence: [email protected]
• Info/History
• Preview PDF

Molecular characterization of disease is essential for precision medicine due to novel predictive biomarkers. Multiple next-generation sequencing (NGS) platforms are available, but their expense and clinical utility vary. Even if a targetable mutation is detected, corresponding drugs may not be available or affordable. No prior studies in Pakistan have focused on integrating NGS results into patient care to assist with therapeutic decision-making and survival outcomes. This retrospective study aimed to evaluate the molecular profiling and therapeutic implications of NGS testing across solid tumors. It included all patients with histologically proven malignancy (metastatic or non-metastatic) who had NGS analysis at Aga Khan University Hospital (AKUH) from June 1, 2020, to June 1, 2023. Foundation One was the NGS platform used. From 2020 to 2023, 192 patients underwent NGS. The majority were male (55.2%) and aged over 50 years (71.9%). The most common indications for NGS were carcinoma of unknown primary (CUP) and lung cancers, representing 26% and 25% respectively, followed by colon (9%) and breast cancers (8%). Most patients had metastatic disease (98.4%). Common mutations in lung cancer were EGFR (16.3%) and KRAS G12C (14.3%). In unknown primary, breast, and colon cancers, the most common mutations were BRAF (8%), PIK3CA (18%), and KRAS (42.1%), respectively. Microsatellite instability (MSI) testing was performed in 95% of patients, with 6% being MSI high. Actionable alterations were detected in 31.8% of patients, but only 17.2% received genotype-matched treatment, mostly as a first-line treatment for lung cancer. The primary barriers were drug availability and affordability. Our results show that the implementation of NGS analysis supports clinical decision making. However, these results were applicable to a small percentage of patients. For better compliance and applicability, drug availability and cost of treatment needs to be addressed

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

The author(s) received no specific funding for this work.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Aga Khan University AKU ERC ERC #2023-8745-25330. Adeeba Zaki: IMPACT AND ROUTINE APPLICATION OF NEXT GENERATION SEQUENCING ON CLINICAL PRACTICE IN ONCOLOGY Thank you for your application for exemption from ethical approval regarding the above mentioned study. Your study was reviewed and approved as exemption. Please ensure that the study is performed as per protocol following all AKU standards.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

View the discussion thread.

Thank you for your interest in spreading the word about medRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Citation Manager Formats

• EndNote (tagged)
• EndNote 8 (xml)
• RefWorks Tagged
• Ref Manager
• Tweet Widget
• Facebook Like
• Google Plus One

Subject Area

• Addiction Medicine (336)
• Allergy and Immunology (658)
• Anesthesia (177)
• Cardiovascular Medicine (2570)
• Dentistry and Oral Medicine (310)
• Dermatology (218)
• Emergency Medicine (390)
• Endocrinology (including Diabetes Mellitus and Metabolic Disease) (913)
• Epidemiology (12083)
• Forensic Medicine (10)
• Gastroenterology (743)
• Genetic and Genomic Medicine (3990)
• Geriatric Medicine (375)
• Health Economics (666)
• Health Informatics (2577)
• Health Policy (992)
• Health Systems and Quality Improvement (959)
• Hematology (357)
• HIV/AIDS (825)
• Infectious Diseases (except HIV/AIDS) (13569)
• Intensive Care and Critical Care Medicine (783)
• Medical Education (396)
• Medical Ethics (107)
• Nephrology (423)
• Neurology (3756)
• Nursing (206)
• Nutrition (559)
• Obstetrics and Gynecology (718)
• Occupational and Environmental Health (687)
• Oncology (1957)
• Ophthalmology (567)
• Orthopedics (233)
• Otolaryngology (301)
• Pain Medicine (247)
• Palliative Medicine (72)
• Pathology (469)
• Pediatrics (1088)
• Pharmacology and Therapeutics (453)
• Primary Care Research (442)
• Psychiatry and Clinical Psychology (3352)
• Public and Global Health (6425)
• Radiology and Imaging (1360)
• Rehabilitation Medicine and Physical Therapy (793)
• Respiratory Medicine (858)
• Rheumatology (394)
• Sexual and Reproductive Health (399)
• Sports Medicine (336)
• Surgery (431)
• Toxicology (51)
• Transplantation (184)
• Urology (163)