Are you a PhD or Master-level researcher who has already collected data (either quantitative or qualitative or both) and is likely to analyse it with a view to addressing research gap by answering research questions or approving/disproving research hypotheses? Do you have curiosity to detect unseen patterns or to establish meaningful connections or to figure out relationships between different variables with the help of collected data? If you answered ‘yes’ to the above-given questions, you must glance through this article as it attempts to offer deep insights into methods that could be used to analyse both quantitative and qualitative data.

HOW TO ANALYSE QUANTITATIVE DATA

Let us start by figuring out how best to analyse quantitative data in the space given below.

Before we move on to the various methods that are used to analyse quantitative data, it is imperative that we take a look at how to prepare the data for the purpose of conducting analysis appropriately.

DATA PREPARATION

Data preparation happens to be the first and foremost thing that any researcher should undertake so as to be able to successfully derive meaningful insights from the given data. Below given is a 4 step-process that you could use to convert raw data into something that holds some value and meaning:

Data Validation:

In this stage, the researcher is supposed to validate the data using the following 4-step model, in order to make sure that it was collected in line with pre-set standards and without any bias.

• Fraud: The first step of data validation requires the researcher to determine whether each of the target respondents was interviewed in reality or not.
• Screening: After determining the authenticity of raw data in the first step, the researcher has to get on with screening the respondents to ensure that they were targeted in line with the research criteria.
• Procedure: Moving on, the researcher is to determine whether data was collected in accordance with the data collection procedures duly laid down in the methodology chapter or not.
• Completeness: Last but not the least, completeness of the collected data has to be established, meaning the researcher needs to look at whether each target respondent was asked all the questions or not.

In order to validate the data, the interviewer or researcher has to randomly select a bunch of surveys and validate the collected data by means of placing calls to target respondents or emailing them and asking a particular set of questions (from the survey) again to find out if the collected responses are authentic. Let us say that there were 100 respondents from two different cities. In an attempt to validate the data, the researcher will call up or email 10 respondents from each of the two cities and ask them re-answer couple of questions from the survey. It is to be noted that data validation is a time-consuming process and could be very lengthy to deal with in case of large number of survey responses.

Data Editing:

Close on the heel of data validation comes data editing which simply refers to the process of dealing with empty fields or incorrect information (present in raw data set) with a view to maintaining accuracy of the results. Basically, one has to check on basic raw data (ranging from ‘matching total number of questions to total number of columns’ to ‘matching total number of respondents to total number of rows’ to ‘ensuring consistency and validity of time stamps’), deal with outliers (i.e. data points that are significantly different from other data points in a data set) and edit the raw data to identify and remove any data point that could question the authenticity of the results as part of data editing process.

For instance, some fields may have been left empty by target respondents. Such empty fields are to be either removed or duly filled when editing raw data.

Data Coding:

When it comes to preparing data for the purpose of analysis, data coding plays a crucial role. The task of a researcher during this stage is to group survey responses and assign values to the same.

Let us say, for instance, that 2000 respondents have been surveyed by a researcher. In order to find out the average age of the surveyed target respondents, the researcher will go about creating age groups first and assigning code to the age of each respondent afterwards. Any target respondent aged between 14-16 would, for example, be assigned a code of 1. Anyone falling into age category of 16-18 would be assigned a code of 2 and the process goes on until each respondent in the data set has been assigned a value or a code.

Engaging in such kind of data coding will help the researcher simplify the process of data analysis later on as the researcher will be dealing with easy-to-manage brackets rather than a massive range of individual ages.

METHODS OF QUANTITATIVE DATA ANALYSIS

After the raw data has been validated, edited and coded, it is now ready to be analysed. There are two popular methods of analysing quantitative data, namely, descriptive statistics and inferential statistics.

Descriptive Statistics:

When a researcher goes about digging into the collected data to simply describe the population through numerical calculations, graphs or tables, he or she could be said to be involved in the process of conducting data analysis using descriptive statistics. It is often the first phase of data analysis that could be performed using the following methods:

• Mean – Also knowns as average, it refers to the central value of a discrete set of numbers. It is often calculated by dividing sum of values by the number of values.
• Median – For a data set, it is often the middle value, separating the higher half from the lower half.
• Mode – It refers to the most repeated value of a data set.
• Percentage – It is often used to express a small group of respondents within a large data set.
• Frequency – It demonstrates the number of times a specific event occurred in an experiment or study.
• Range – It represent the difference between the highest and the lowest value of a data set.

Before you make use of descriptive statistics, you need to determine which of the above-mentioned methods can answer your research question in the best possible manner and what it is that you would like to show to the target audience. For example, gender distribution of respondents in a given data set could be best represented using percentage.

Also known as univariate analysis for its ability to analyse single variable, descriptive statistics is a method of data analysis that is often useful when research is based on small sample that does not require generalization to a larger population. For instance, comparison of percentages of children that have been vaccinated in two separate cities could very well be dealt with using descriptive statistics.

Inferential Statistics:

Also referred to as bivariate or multivariate analysis for its ability to deal with two or more variables, Inferential statistics is a method of data analysis that is used to generalize results and make predictions about larger population.

This is often the second phase of quantitative data analysis used by the researchers to depict the relationship between two or more variables rather than describe single variable. A researcher could be said to be involved in inferential statistics when he or she is using the following:

• Correlation – This statistical technique demonstrates the degree (association or absence) of relationship between two variables. For instance, taller people tend to have more weight. Hence, height and weight are correlated. With that said, there might not be causal relationship between the two, meaning gaining weight does not cause one to become taller.
• Regression – This form of statistical analysis has to do with predicting value of the dependent variable on the basis of already-known value/s of one or more independent variable/s. For example, we can figure out one’s weight on the basis of one’s height.
• Analysis of Variance (ANOVA) – This technique of statistics is often used to analyse the differences among group means in a given sample. To put it simpler, ANOVA can help a researcher test the degree to which two or more groups vary or differ in an experiment. For instance, if you are trying to gauge relationship between the total number of children in a family and socio-economic status, you may begin with recruitment of a sample of families from each socio-economic status and then ask them about their ideal number of children. ANOVA, in this case, would be used to check if difference between groups’ responses is statistically significant or due to random chance.

While the above-given statistical techniques are what researchers often resort to for conducting inferential analysis, there are other methods available too, such as standard deviation and variance.

This brings us to an end for the discussion related to quantitative data analysis. Hence, we should move on to discuss how best to analyse qualitative data in the space below:

HOW TO ANALYSE QUALITATIVE DATA

Mostly used for exploratory research, qualitative data analysis refers to the process of analysing data collected in the form of words, observations, images and symbols to answer research questions. Unlike quantitative data analysis where the researcher is required to validate, edit and code the data before carrying out actual analysis, qualitative data analysis begins as soon as the data is collected by the researcher.

DATA PREPARATION AND BASIC QUALITATIVE DATA ANALYSIS

Often do we see data preparation and data analysis being done by the researcher simultaneously. The following steps of qualitative data analysis would throw more light on the same:

• Familiarity with data – The very first step for any researcher conducting qualitative data analysis is to get familiar with the collected word-rich data by means of transcribing and reading through it over and over with an intention to seek out basic patterns or observations.
• Review of research questions – Following the first step, the researcher begins to review the research questions that could be best answered by the collected data.
• Development of framework – After reviewing the research questions, the researcher goes about developing a framework by means of coding or indexing the collected data. All the broad ideas, phrases, concepts or behaviours are first identified and then assigned a value. This particular step helps with structuring and labelling of data for the purpose of drawing meaningful insights.
• Identification of patterns and connections – The last step is to do with discovering themes that could best address the research questions, identifying patterns and finding areas for future research.

Let us now expand on what it takes to identify patterns when you have qualitative data at hand. Also known as themes, qualitative data patterns could be discovered in several ways.

The first one to be mentioned here is a word-based method wherein the researcher could resort to techniques, such as word repetitions or key words in context. In case of word-repetition, the researcher looks at various words that have been repeatedly used in the text to be analysed. For example, a researcher studying about what people think in relation to Brexit could come across words, such as change, negotiations, politics, deal etc., on numerous occasions. Hence, such words could be further used for the purpose of conducting qualitative data analysis by the researcher.

Where a researcher chooses to go for the second technique, i.e. key words in context, he or she basically tries to dig into a particular concept by putting context around it. For example, a researcher attempting to study about anxiety among the target respondents is likely to study the context of when anxiety was reported by each respondent. This could be reported when working under a lot of pressure at work or suffering from poor financial condition or fighting a family member etc.

The second method for identifying themes is a set of scrutiny-based techniques. ‘Compare and contrast’ happens to be one of such techniques wherein the researcher reviews the collected data and comes up with a theme on the basis of various pieces of text that are either in favour of it or against it. Let us suppose that the researcher has discovered a theme, i.e. ‘significance of social media in today’s world’, on the basis of the arguments for or against it found in the text. While a school of thought may be supporting the presence of social media, another school of thought may be talking against it. The data could be further analysed by the researcher to unearth the real causes behind the thought process of each group involved.

Finding out metaphors and analogies, and discovering connectors (depicting relationship between different ideas) in the text are some of the other ways a researcher could engage in scrutiny of qualitative data for the purpose of finding patterns.

METHODS OF QUALITATIVE DATA ANALYSIS

Let us now review some of the popular methods that are often resorted to by researchers for conducting qualitative data analysis in the space provided below:

• Content analysis – Often used to analyse documented information such as text, media or even physical items, this method of qualitative data analysis is quite a common one among researchers. Often would you see interview transcripts being analysed using this particular method.
• Narrative analysis – When a researcher goes about analysing data collected from a number of sources (including interviews, observations or surveys) to answer the research questions, he or she could be said to be involved in narrative analysis that aims at drawing insights from stories and experiences shared by target respondents.
• Discourse analysis – Similar to narrative analysis, this method attempts to analyse data obtained from various interactions with target respondents to answer the research questions. In addition, discourse analysis takes into consideration the social context in which conversations between the researcher and respondents take place, meaning this method would also take into account respondent’s day-to-day environment when analysing the collected data.
• Grounded theory – Using this method, a researcher analyses qualitative data in the form of various case-studies in an attempt to find out why certain phenomenon took place. The researcher has to go through the process of altering and creating new explanations as he/she develops knowledge about a particular phenomenon by means of reading new cases regularly. The process comes to an end as the researcher gets hold of an explanation that fits all case-studies.

While other qualitative data analysis methods, such as conversational analysis, exist, the above-given methods are what researchers often make use of in order to analyse qualitative data.

When done appropriately, data analysis could provide solid grounds for ‘Results’ and ‘Discussion’ chapters of your PhD thesis or dissertation. This, in turn, would give rise to a credible piece of research for sure. Hence, you must take utmost care when conducting data analysis to be able to derive meaningful and insightful findings from the data.

Hopefully, the article has served its purpose of clarifying your doubts related to quantitative and qualitative data analysis. Please feel free to share the article with your connections who might benefit from it.

Last but not the least, if you are still struggling with any aspect of data analysis while working on your PhD thesis or dissertation, please feel free to write to us at info@ukdissertationwriters.com or fill out our Contact Us form and our team of native English-speaking UK-University Professors and Sr. Lecturers would be more than happy to assist you further.

Are you someone who is presently working on PhD Thesis or Master-level Dissertation? Do you find yourself stuck when writing up ‘Methodology’ chapter? Does it bother you to such an extent that you feel clueless about the future course of action? If you answered ‘yes’ to all the previously asked questions, you must read on as this article would attempt to clear your doubts related to ‘Methodology’ chapter by using Saunders research onion as the base.

As a PhD or Postgraduate researcher, you may already have settled on a suitable research topic by now. Additionally, you might have critically examined or reviewed literature relevant to the chosen topic and penned down clear & concise research questions with a view to addressing the research gap and making a significant contribution to the existing body of knowledge in your chosen field of study. So far, you have not had any issues with regard to your research paper whatsoever. However, you start feeling ill at ease as you move on to draft one of the crucial chapters of your PhD thesis or dissertation, i.e. Methodology. This particular chapter apparently underpins your entire piece of research and adds a great deal of credibility to it. Hence, it must be drafted carefully after weighing all the possible methodological decisions.

In academia, the research onion model is quite a famous one and is often resorted to by researchers across the world for drafting the much-feared methodology chapter. This model was developed by Saunders et al in 2007 and the different layers of the model represent the various stages through which a researcher must pass when putting together an effective methodology. At each stage, the researcher must weigh all the given possibilities and make the most logical methodological decision which, in turn, will give rise to a credible piece of research.

As you can see in the picture above, there are 6 layers to the research onion. Now, you must be wondering as to what is the analogy of an onion to research methodology. Well, it simply has to do with the structured process of peeling, i.e. you have to peel away each layer of the onion starting from the outer one in order to make your way to the core eventually. Similarly, you have to start from the topmost layer of the research onion, i.e. research philosophy, and move through the middle layers comprising of research approaches, research strategies, research choices and time horizons before finally getting to the core, i.e. research techniques and procedures. The important thing to note here is that each layer has something important to offer to make your PhD thesis or dissertation look credible and hence, none of the layers should be overlooked or ignored at all. Let us now find out what each of the layers means in detail in the space provided below:

Research Philosophy – Saunders Research Onion

The first and topmost layer of the research onion has to do with a set of beliefs related to the nature of reality being investigated, and is often studied in the context of ontology and epistemology. You should explore it carefully as selection of a philosophical stance would influence data collection and data analysis going forward. Let us briefly understand what epistemology and ontology mean and consist of.

Epistemology – This branch of philosophy is concerned with the question of what is (or should be) regarded as acceptable knowledge in a discipline and how we can obtain it. It basically answers the questions beginning with ‘how’ and ‘what’. Perception, sensation, intuition, reason and even faith are considered as means to knowledge as per Epistemology.

In order to understand it better, let us look at positivism, critical realism and interpretivism (the three philosophical stances that are often linked to Epistemology).

• Positivism – This position is based on the idea that scientific knowledge is the true or acceptable knowledge of the world and is characterized by the testing of the hypotheses (or research questions) derived from the existing theory of knowledge. The body of research generated by this philosophical stance is something that can be replicated by other researchers with same or very similar quantifiable results arising from statistical analysis.
• Realism – The philosophy of realism questions reliability of the scientific knowledge and maintains that all theories can be revised and more reliable results can be obtained through continual research and application of new methods of research. Hence, new research methods contribute to the acceptable knowledge in this case.
• Interpretivism – Unlike positivism and realism, an interpretive philosophy emphasizes the use of qualitative analysis over quantitative or statistical analysis to obtain the results. The interpretivist researcher is often seen playing an important role in making sense of and interpreting the collected data. Therefore, it would not be wrong to say that interpretivism incorporates human interest into a research study and recognises differences between people.

Ontology – This branch of metaphysics (philosophy related to the overall nature of what things are) is concerned with the study of reality or things that exhibit reality. It answers the question ‘what is’. Physical objects, minds, events, properties, values and abstract entities such as numbers and sets could all be said to be representing the ontology (or inventory) of the world.

In order to get further insight into Ontology, let us take a look at objectivism, constructivism and pragmatism that constitute it:

• Objectivism – This philosophy is derived from the idea that human knowledge and values are objective and are determined by the nature of reality. They are not created by the thoughts which one (i.e. social actor) has. For instance, if it is raining, it is for real and would, therefore, be acknowledged by every living creature. Such reality is not dependent on the thoughts of any specific social actor. Rather, it influences them. In your research, you may, for example, talk about how a specific law (a real phenomenon) passed by the government is impacting a group of people (social actors).
• Constructivism – This philosophical stance focuses on how bodies of knowledge come to be and how ideas are constructed by human interactions and decisions. Contrary to what we saw in case of objectivism, constructivism maintains that reality is dependent on or is constructed by social actors. For example, a new law (reality) passed by the government may be the outcome of the behavior of a group of people (social actors) which the law has now impact on.
• Pragmatism – This philosophical standpoint centers on the linking of theory and practice. It asserts that both objectivism and constructivism are practical and valid ways to approach any research, and they both could be comfortably used to find solutions to problems.

Research Approach – Saunders Research Onion

This happens to be the second layer of the research onion and demonstrates the approaches that a researcher could resort to when conducting research of any kind.

A research approach could be either deductive or inductive, and its selection would often depend on the previously-made choices, research aims, limitations and personal opinions.

• Deductive – This research approach tends to flow from generic to specific. Any researcher using deductive reasoning would start with theory and move on to research question or hypothesis which is tested through data collection afterwards. In the end, findings derived from the collected data would either confirm or reject the research question or hypothesis.
• Inductive – This type of reasoning or approach is often used or helpful when there is little research available on a topic. Unlike deductive approach, a researcher has to move in the opposite direction from research question to observation and description to analysis before finally getting on to theory.

Generally speaking, a deductive approach is often associated with quantitative research whereas an inductive approach is often linked to qualitative research.

Research Strategies – Saunders Research Onion

The third layer of the research onion is concerned with how a researcher plans to collect data for the PhD thesis or dissertation in question. Such data collection methods could include experiment, survey, case study, action research, grounded theory, ethnography and archival research. An academic researcher can choose more than one of the above-mentioned options to collect data as long as such decision is explained well in the PhD thesis or dissertation. Let us glance through some of the research strategies in section below:

• Experiment – Scientific and rigid in structure, experimental designs are meant to test causal effects of phenomena on a group. The data collected using this method could be statistically analysed.
• Survey – A survey design often results from a deductive approach and is capable of collecting large volume of data that is apt for statistical analysis. The collected data helps answer the research question or hypothesis in a cost-effective manner.
• Case study – This type of research design provides unique examples of real people or cases in real situations. The number of such case studies is often restricted for drawing clear conclusions from the data.
• Action research – This is primarily designed to deal with a specific problem in a specific situation. The process of action research begins with setting up of a clear objective. Thereafter, the problem is fully diagnosed and subsequently, a list of solutions is prepared and presented as recommendations to solve the issue.
• Grounded Theory – This strategy takes the approach of collecting data to build theory rather than to test or refine one. The researcher using grounded theory strategy often begins with a research question or qualitative data collection. Thereafter, the data collected through observation is reviewed by the researcher and the repeated ideas are then coded/grouped into categories that, in turn, create basis for a new theory.
• Ethnography – This strategy studies people in natural surroundings in order to develop theory around behaviour and culture. This is rather a daunting and time-consuming research design wherein the researcher becomes part of the community being investigated with a view to producing details about long-term changes in opinions and actions.
• Archival Research – This strategy derives information from existing data and archive documents. A study using this research strategy could face a roadblock going forward as the accuracy and the amount of available information could present issues. Hence, you are encouraged not to rely solely on this kind of secondary data for your PhD thesis or dissertation.

Research Choices – Saunders Research Onion

The fourth layer of the research onion is concerned with qualitative and quantitative methodologies. You would need to make a decision as to whether you would choose the former or the latter or both for your PhD thesis or dissertation. If you are using both methods, you would further need to decide whether both methods would weigh equally or one would dominate the other in your research.

Quantitative research relates to numbers, measurements and quantity whereas Qualitative research is concerned with rich data including opinions, description and personal accounts.

You would need to follow a framework of methods and tools depending on the choice you make at this stage.

Basically, we have three methods to choose from when working with this specific layer of the research onion. These are listed below:

• Mono-method – This type of method takes place when the researcher goes about collecting either qualitative data or quantitative data based on the decisions made in the previous stages of research onion.
• Mixed-methods – When a researcher collects both quantitative data and qualitative data with an intention to use those equally in the research study, data collection and analysis, we term it as mixed-methods research. It is rather a popular framework that attempts to complement quantitative data with qualitative data in order to overcome the limitations of each individual method.
• Multi-method – This means that both qualitative and quantitative data collection techniques are used. However, analysis of the collected data is done using one perspective only.

Time Horizons – Saunders Research Onion

Layer 5 of the research onion is to do with the timeframe for the research. There are two options for a researcher to choose from, i.e. cross-sectional and longitudinal.

• Cross-sectional – This option presents a snapshot view of a particular situation at a single point in time and confines the duration of data collection and research to short period of time.
• Longitudinal – This type of research studies events and behaviours using concentrated samples over a long period of time.

Techniques and Procedures – Saunders Research Onion

The final layer of the research onion looks at data collection and analysis. As a researcher, you need to pen down decisions related to sample groups, questionnaire content, interview questions, ethics and what not in this particular section. Please note that all the decisions incorporated into the 5^th layer should be in accordance with the discussions that took place in the previous layers of the research onion if you are to produce valid results.

Hopefully, the article has served its purpose of clarifying your doubts related to research onion and methodology chapter well. Please feel free to share the article with your connections who might benefit from it.

Last but not the least, if you are still struggling with your methodology chapter or any other aspect of your PhD thesis or dissertation, please feel free to write to us at info@ukdissertationwriters.com or fill out our Contact Us form and our team of native English-speaking UK-University Professors and Sr. Lecturers would be more than happy to assist you further. 

When you are pursuing your PhD, you are often flooded with large chunks of information that result from the meetings you hold with your PhD supervisor and other academics, the journal articles and books you read through, the conferences you attend and what not.

If you leave such high volume of incoming data unattended and unorganised, you will have to suffer from its dire consequences, such as making multiple revisions of your PhD Thesis draft unnecessarily or wasting a great deal of time re-locating any useful reference which you had thought of including into your PhD research to make its arguments stronger and more critical, in the foreseeable future.

In order to avoid such unwanted troubles during your PhD journey, you will have to stay extremely organised throughout, and you can do so by picking out one of the software tools that help with the organisation of data.

While there are many software tools out there in the market, Evernote has been touted by a number of PhD students for its wonderful attributes that aid in collecting, storing and accessing the important pieces of information, relevant to your PhD research, across all of your devices.

With the help of Evernote, you can manage wide variety of data, including text, images, audio, web pages and snapshots, in a smooth way. Not only does Evernote help store and sync all the relevant details related to your PhD research across all your devices, but also it helps you share the stored information with the targeted recipients via various means, including URL, e-mail and social networks, in an easy manner. Additionally, you could use its ‘TypeAhead’ search functionality to look up any note you have saved in the past.

With plethora of benefits that Evernote has on offer, it becomes an ideal tool for PhD students to streamline their research work with a view to improving their academic productivity and completing their PhD journey in a hassle-free manner.

Let us now discuss how you can use Evernote to boost your PhD productivity in detail:

1. MINUTES OF THE MEETINGS

As a PhD student, you often have to meet your PhD supervisor as well as other academics with regard to your research work in person or virtually.

In each and every meeting, important points, including key issues, central ideas and to-do tasks, related to your PhD research are discussed and hence, it becomes vital to you, as a researcher, to record all the relevant pieces of discussed information as minutes so as to be able to remain on the right track all the time.

With feature-rich Evernote, you can keep track of all the important details related to your meetings and stay on the top of your PhD research throughout. If you do not feel like penning down the minutes of any meeting, you could even resort to the audio note (instead of text) to record everything that has been discussed and transcribe the relevant details later on.

Every note created using Evernote has a URL that makes sharing of information very simple and easy. As a PhD researcher, you can take advantage of this feature and share the minutes of meeting with all the participants, be it your PhD supervisor or other doctoral researchers, using the relevant URL so as to keep them all on the same page.

If you have a follow-up meeting coming up, you might want to search for minutes of the previous meeting stored on Evernote to quickly refresh your memory of the previously-held discussion. This would ensure that discussion in the following meeting proceeds in the right direction smoothly.

2. CONFERENCES

While you are pursuing your PhD, you will get many opportunities to attend conferences that are relevant to your research area. When you make it to such conferences, you will get to meet other doctoral students who, like you, would be there to discuss their research work using fascinating posters.

Some of the showcased research posters might seem interesting and useful to you in the context of your own research work. Hence, you might end up requesting other researchers to forward you a copy of their research poster via email.

While some researchers may send you their research posters, others may not. In order to avoid the possibility of missing out on any relevant research poster, you might want to click pictures of all those that appear relevant and useful to you from the viewpoint of your own research and save them all using Evernote. This would ensure that you have access to all the interesting research posters at your will across all of your devices.

With Evernote, you may also like to make note of all the questions you have been asked in relation to your research work at any conference. This might be a useful input when you start preparing for your viva after you are done with writing your PhD Thesis.

You could also use Evernote to save all of your travel documents, tickets, bills and expenses (related to any conference) so that you could make a declaration of the spent amount appropriately when you are back to your university.

3. LITERATURE REVIEW

As a doctoral student, you have to read through a number of journal articles, books, industry reports, relevant publications and what not, so as to be able to write a critical and cohesive literature review on your research topic.

There are times when you realise that you cannot read any interesting academic article instantly and you may want to go through it later on when you have sufficient time handy.

Many a time, you forget to bookmark or save the article at an appropriate or easy-to-access place for future references. As a result of the same, you lose track of that article completely and are left with no option but to repent not reading it at all.

In order to avoid losing sight of important academic articles and web pages, you can use Evernote Clipper that enables one to clip or store any piece of information that is found out on the web, be it a PDF or an article or any paragraph or a photo. With Evernote, the process of reviewing literature becomes far easier.

4. MANAGEMENT OF DAY-TO-DAY TASKS

Evernote is often used by PhD students to manage a wide of variety of tasks which they have to deal with off and on. The tasks could range from ‘finishing any particular section of PhD Thesis’ to ‘reviewing a set of journal articles’ to ‘responding to important emails’ to ‘attending someone’s birthday party’ to many more.

With the help of Evernote, you could create a notebook titled ‘to-do tasks’ that would contain all the notes that need be acted upon. Thereafter, another notebook titled ‘today’ could be generated where you could drag all the notes (from ‘to-do tasks’ notebook) that need be dealt with today.

The creation of the above-mentioned Evernote notebooks would help you stay organised throughout and complete all of your PhD-related tasks on or before time.

5. AUTOMATE EVERNOTE WITH IFTTT

Have you ever thought of syncing your Evernote account with IFTTT, which refers to ‘If This Then That’? If not, you must consider doing it now in order to make your PhD life more organised and less complicated.

The IFTTT website has a total of 305 channels, including Evernote, Facebook, Twitter and Foursquare, and offers you a great opportunity to connect two of its channels to make a unique and helpful recipe.

To understand this concept better, let us see how connecting Evernote to Twitter (the two channels on IFTTT website) might help. Suppose you visit your Twitter account very often and retweet all those pieces of content which you find useful in the context of your PhD. Deep inside your heart, you wish to save those re-tweets at a place where those could be easily accessed. What you will do, in this case, is connect your Twitter account to your Evernote account using IFTTT and instruct the website that if you retweet anything on Twitter, then that retweet gets saved on Evernote for future reference.

The above-given example demonstrates one of the recipes you could make using Evernote and another channel of IFTTT website (i.e. Twitter). You could try making other useful recipes to stay organised and updated by combining Evernote with other relevant channels appearing on IFTTT website.

In conclusion, Evernote is a tool you cannot live without as a PhD student. Whether you are struggling to absorb information during any meeting or you want to capture some fascinating research posters at any conference so as to review those later or you wish to save link to any academic article for future reference, Evernote is what you should resort to. This tool will help you remain organised throughout your PhD journey and add simplicity to the complicated and intense life that you may be leading at present.