Research Planning & Designing: A lens view

Although some people dismiss descriptive #research as `mere description', good description is fundamental to the #research enterprise and it has added immeasurably to our knowledge of the shape and nature of our society. Descriptive #research encompasses much government sponsored #research including the population census, the collection of a wide range of social indicators and economic information such as household expenditure patterns, time use studies, employment and crime statistics and the like. Descriptions can be concrete or abstract. A relatively concrete description might describe the ethnic mix of a community, the changing age of a population or the gender mix of a workplace. Alternatively, the description might ask more abstract questions such as `Is the level of social inequality increasing or declining?', `How secular is society?' or `How much poverty is there in this community?' Accurate descriptions of the level of unemployment or poverty have historically played a key role in social policy reforms . By demonstrating the existence of social problems, competent description can challenge accepted assumptions about the way things are and can provoke action.

we will be discussing about Planning & Sampling process which will help us further to develop a effective #research study.

The theme divided into three parts in

Part one we will discuss about What is #research Planning

Part Two what is Sampling process

Final part is how to plan #design the #research

Part One

In this part we will discuss more about #research Planning & how effectively we can do it for effective #research #design.

Nearly every applicant for a tenure-track faculty job is expected to include a #research plan. Exceptions are rare. Just as rare are programs #designed to help doctoral students and postdocs learn how to create a #research plan. Which is too bad: Writing an effective #research plan is tricky. And until now, there was little advice to be found.

Okay, so that isn't exactly true: It isn't hard to find advice. Opinions, after all, are not in short supply in the academy. What is hard is finding advice you can rely on. We can help.

Why? Because we talked to a lot of people. We interviewed and corresponded with faculty and #research scientists who have served on hiring committees. All of our sources have experience; some of our sources have a lot of experience.

We considered everything, filtered out the muck, and distilled it all down to a general strategy and a few simple principles, with a few variations on the theme thrown in for good measure. Our aim is to do some of your homework for you, to make sure that you'll never have to read more than you have time for.

Furthermore, we'll keep talking to people about this topic, and we'll incorporate new responses into this document as we receive them. As a consequence, this piece, like the other tools in the tool kit, will remain fresh and useful when other resources have become dated and useless.

So, onward and upward ...

What's the purpose of a #research plan?

It depends on who's asking the question, and who’s answering it. From your immediate point of view, the purpose of a #research plan is to help get you hired.

The #research plan, however, serves another, very important function: It contributes to your development as a scientist. Your #research plan is a map for your career as a #research science professional.

 As will become apparent later in this document, one of the functions of a #research plan is to demonstrate your intellectual vision and aspirations. It's also an opportunity to begin to demonstrate the creative and independent thinking required of a successful scientist.

Not yet on the job market? Just starting out as a postdoc? A #research plan isn't just for demonstrating; it's also for honing and refining. It's possible to function quite well as a postdoc or grad student while giving little thought to your future.

Writing a #research plan casts your gaze forward and prompts you to begin planning for when you have your own laboratory. And if you've already started to think about your own lab, it will help you to refine your plans.

So, take a stab at writing a #research plan, even if you don't expect to be on the job market for a while. Think of it as a rough draft, a fantasy trip for your career.

But never mind about that. Most of you are trying to get hired. In that case what matters is, what is the committee looking for?

The answer: relief from anxiety.

Hiring committees desperately want to avoid making a serious mistake by investing institutional and intellectual capital in the wrong person. The aim of your #research plan, then, as of the rest of your application, is to assure the hiring committee that life with you will be pain-free.

How do you do this? Provide the committee a compelling, reassuring, believable image of what their life will be like when you are working down the hall.

Tell them a story--a believable, credible story--about what your lab will be like 5 years from now: well-funded, vibrant, productive, pursuing a valuable, ambitious but realistic #research agenda that meshes well with the department's mission and with the other #research going on in the department.

Please don't misunderstand: You shouldn't tell them this ("in 5 years my lab will be vibrant, productive, and well-funded ..."); rather, you need to lead them to believe it by describing a #research agenda that persuades them that you will succeed.

 There are two parts to this: You have to tell a good story, and you have to make them believe it. If the story isn't compelling you won't get hired, and if they can't quite imagine it becoming reality, you won't get hired.

How do I tell a good story?

First, choose an important subject.

 If the #research you plan is not compelling, no rhetorical skill will make it compelling to a committee of smart scientists. If the #research you propose is not manifestly, obviously important, if you don't know why it's important, or if you can't convey its importance effectively, convincing the committee to hire you won't be easy.

Note that there are two issues here: believing in the importance of your own work, and persuading others that your work is important.

If you don't think the work you'll be doing is important, your best bet is to change fields. The goal of science may be to uncover truth, but uncovering objective truth is a very difficult thing to do, and doing it requires passion. If you aren't passionate about your work, your best bet is to find work about which you can be passionate.

It isn't easy to change gears midcourse, but getting yourself into an important area of #research will be well worth the effort in the long term--to your hirability, to your fundability, to your tenurability, and also to your career satisfaction. Do another postdoc if you must.

Passion for your work is a necessary, but insufficient, condition for capturing the attention of hiring committees. After all, some people are passionate about, um, peculiar things.

 To convince the committee to hire you, you must convince them that your passion is justified and that they will benefit from investing in your passion--that is, that your work is important.

Be specific.

 Curing cancer is not a suitable goal for one individual's #research plan--exciting, yes, but much too big to be believable. Inhibiting tumor growth? That's better, says one of our respondents--especially when that general goal is supported by more specific strategies.

 "[That kind of #research] can travel down several different mechanistic routes," this respondent says, "i.e., angiogenesis, breakdown of extracellular matrix, gene activation, induction of molecules involved--it can use different models--implanting tumors, using different tumor models, in vivo, in vitro, etc." The combination of a manifestly important goal with manifestly interesting, feasible approaches is the foundation of the #research plan.

Being specific is not the same thing as including loads of detail. Being specific means including only as much detail as the job requires--not more. "Vague generalities are the sign of a vague mind," says one source. "This means that the proposal must walk the fine line of enough detail to show the reader that the candidate knows what they are talking about, but not too much detail that it confuses or bores the search committee."

Keep it short and focus on the major themes. 

"Brevity and clarity are the most important elements," wrote another respondent, expressing a sentiment shared by everyone. "Clear, concise writing ... is a plus," said another. "Superfluous details are not just unnecessary, they are often the hallmark of a poor plan. The specific aims must be clear and succinct." Identify your goals,

state why those goals are important, define your approach to achieving those goals, and indicate the kinds of evidence that will validate your approach. Oh, and do it clearly and succinctly.

"If you were sitting for 4 hours reading such proposals, what would you look for? Clear and to the point wins every time in this arena."

Effective communication requires anticipating readers' needs, giving them exactly the information they need just when they need it. Constructing a #research plan along these lines strengthens your application in three ways:

You avoid alienating the committee by boring them; you tell the committee precisely what you intend to do; and you show that you have a subtle mind and a deep knowledge of your field.

Can't do this yet? No hurry--consider spending another year as a postdoc, and study hard.

Be serious about writing. 

Writes one respondent: "If the proposal confuses the reader in almost any way, it is simply tossed out. I strongly recommend that the candidate have colleagues pre-review the proposal and make sure the English is clear and ideas explained so that a variety of people in the general area can understand what is being proposed and the importance of the work."

If your writing skills are weak, it might be time to strengthen them. Or hire an editor. And by all means have several people--preferably senior colleagues who have served on hiring committees--critique your #research plan.

But there were two parts to this, remember? You not only have to tell a good story--you also have to make it seem real, to make them expect it to come true.

How do I make my #research plan seem real?

Have a solid, well-considered, realistic plan.

 If you want to get a job at an institution that takes its #research seriously, you'll have to convince your future colleagues that you've gotten past the young, impressionable phase, where every idea glitters with promise despite the fact that it isn't feasible and isn't likely to work.

Show the committee that, although your high ideals remain intact, your years of graduate and postdoctoral study have helped you to know the difference between good ideas and good intentions. In the words of one scholar, "You can tell a 'building castles in the sky' #research plan. They are not built on solid data and go to the very bottom of the pool." Indeed.

Include preliminary data

Preliminary data offer the most convincing argument for the viability of your #research plan. If you have them, use them--positive results will be of interest and persuasive to hiring committee members.

The nature of your preliminary data and findings will vary--some will have much to share, others might be forced to share very preliminary data.

Nothing grounds your hopes and dreams in the real world like good, solid data. Your plan might sound exciting, but will it work? It's one thing to make it sound good;

if you can show that you've already taken the first, tentative but successful steps of that long journey, reaching your destination will seem a lot less like a pipe dream.

One of my sources was unequivocal on this point: "Does the #research question build on the preliminary data the person has generated? No preliminary data equals no #research question." Which also equals no job offer at that institution.

It is important to remember that just as institutions vary widely in their practices, so too do the expectations of hiring committees. Do your homework: Learn about the culture of the department and the experiences of previous faculty hires.

Include redundant approaches

 If you want to succeed as a scientist you have to be resourceful. You can't be a one-trick pony. And the focus must be on the science--on the problem you aim to solve--not on the scientist or a particular approach.

No matter how knowledgeable you are, no matter how well considered your #research plan, you can't predict the future. And if you haven't done the work yet, you don't know how it will turn out. That means that any one approach you specify might not work, even

if it seems compelling. So if you want to convince the committee that you will succeed, give them not one, but two, or even three, compelling approaches, all of which have a good chance of success.

How do I demonstrate my independence?

Different institutions expect you to be at different stages of your career. Think of it as a continuum: At one end sit well-established #researchers with strong #research records, many first-author (or last-author) publications, and their own #research funding. At the other end sit rosy-cheeked, freshly minted Ph.D.s full of enthusiasm, promise, and ideas, but with little yet to show for it.

Most candidates for entry-level tenure-track faculty jobs at institutions that require #research (that is, most of the people who write #research plans for job applications) are somewhere in the middle. You probably won't get hired anywhere if you aren't well prepared to start a productive #research program at a scale appropriate for the institution.

But these days some institutions and departments are looking for more than that. Increasingly, especially in the biomedical field, universities are hiring established #researchers, even at the "entry" (assistant professor) level. How is this possible?

These days some pretenure-track scientists are setting up their own #research programs. Increasingly, senior postdocs are being promoted to #research associate or #research faculty positions during what  calls the "postpostdoc" phase of their #research career.

 In that position, they write #research grants in their own names and their host institutions sponsor them.

The key objective if you’re applying to one of these institutions is securing #research grants: If you have a grant in your own name, you'll be a strong candidate; if you don't have your own grant, you are less competitive.

It's a cynical cop out on the institution’s part, really, taking a pass on the difficult job of evaluating talent and capitulating to the reality of big-time biomedical #research: It's all about the cash. Still, increasingly it's a fact of life. But how do you know if the institution to which you hope to apply is one of these? Ask.

Those scientists and institutions--the ones sitting at the experienced far end of the continuum--are exceptional. Indeed, second-tier #research institutions tend to expect the most experience. Most hiring committees aren't looking for completely independent work; they're looking for original, creative ideas, together with a record of accomplishment.

Few people applying for tenure-track jobs have had the opportunity to start their own #research programs. After all, traditionally that's what assistant professorships are all about, and most institutions still think that way. It helps to be somewhere in the middle of that continuum, but most committees are still looking more for promise than for guarantees.

Demonstrate your promise by displaying your potential and actual independence. Show the committee that you have the deep thinking and talent to operate independent of your adviser.

How do you demonstrate your independence when you have never been given the chance to work independently?

Likely as not, all your data were collected in someone else's lab, as a part of someone else's #research agenda. How, then, do you distinguish your #research from your adviser's #research?

On paper. 

It's an apparent : You need to show that your ideas are fresh, new, and yours, and you have to show they're grounded in work you've already done, usually in someone else's lab. It's a tough sell, but most of your competitors are in the same boat.

So how do you do it? One respondent said it beautifully: "The best plans usually build on the prior experience of the applicant but are not direct extensions of their postdoctoral work."

I'm going to type that phrase again, it's so important: The best plans usually build on the prior experience of the applicant but are not direct extensions of their postdoctoral work.

Unless you're one of the select few applicants with lots of experience leading your own lab, that's the key to your rhetorical strategy. That's the outline of the story you must tell: "I did this work as a grad student/postdoc and it was important and it was great.

Now, as a faculty member, I want to do something a little bit different, but the work I'm proposing takes full advantage of the knowledge and skills I gained during the training phase of my career." It's different enough to be original, but similar enough that your years of training aren't wasted.

Another respondent wrote, "Most candidates (95%) stick to extensions of what they are most familiar with, but the key is, have they figured out some rather creative new directions for the #research and have they done a good job convincing us that they can do it based on what is already known?"

"Once we have a short list of candidates," writes yet another source, "the #research proposals are looked at more carefully for imaginative ideas that differ from the candidates’ Ph.D. or postdoctoral #research." Get the message?

With your adviser's cooperation. 

One key to doing this successfully is to make sure your boss tells the same story. It is hoped that you have a good, open relationship with your adviser; if you do, go in and chat and coordinate your strategies.

 Decide what turf is his or hers, what turf is yours, and what story you intend to tell in your #research plan and his or her letter of recommendation. But make sure they don't match too precisely.

Is this sort of coordination unethical? Hardly. There's no deception here, no attempt to pull the wool over the committee's eyes. On the contrary, it's clarity you're seeking: in your relationship with your adviser and with the hiring committee.

Be careful, however: This is tricky ethical territory. The ideas you're claiming must be yours. Don't just take your adviser's ideas and package them as your own, even if your adviser signs off on the plan.

If your relationship with your adviser isn't so chummy, you still want to do these same things; you just want to do it more carefully.

If you still have time, set up your own lab in the corner of your adviser's

 If you aren't applying for jobs right now, there's still time. Talk to your adviser about carving out your own #research niche within the larger #research effort, where you do work motivated by your own original ideas, something related but oblique to what your adviser is doing in the rest of the lab.

Is the #research plan more important in the screening phase or late in the game?

In general, #research plans are weighed more heavily later in the game, with more readily comprehensible evidence (especially pedigree, letters of recommendation, impact factor of journals, etc.) being weighed more heavily in the early rounds.

However, your #research plan must be #designed to serve more than one purpose. It must withstand intense scrutiny in the later rounds of the job search, and it must make a good first impression.

How long should it be?

Opinions vary. One person I spoke to said that a #research plan should be "about three pages of 1.5-spaced text, and NEVER more than five." Another source prefers "three semi-independent (but related) sub-proposals not more than about three to four pages (single-spaced) each with a half page of important and relevant references." That's nine to 12 pages.

There is some variation from one discipline to the next (the first of these recommendations came from a medical school, the second from a department of chemistry), but there are few if any standards even within a field. This shows how much of a crapshoot getting hired can be: Because you usually don't know in advance how long a document the hiring committee is looking for, there's little chance of the same candidate, no matter how qualified, getting offers from both of these institutions.

My recommendation? Call the chair of the hiring committee (or send e-mail) and ask for advice. If no advice is forthcoming, aim for five pages, 12-point Times New Roman, 1.5 spaced. Some will think it's a bit too long, others a bit too short, but no one will throw it out because of its length.

Remember that we said that a #research plan needs to help you through initial screening and withstand careful scrutiny in the later stages.

How do you make a good first impression?

Keep it short.

 No more than five 1.5-spaced pages, unless you've gotten different advice from the hiring committee chair.

Write it carefully. 

Make sure that it swings. If you're a lousy writer, get help.

Include an executive summary. 

Call it an abstract if you wish. The idea is to present, up front, in half a page or so, the information that the committee is most likely to be looking for in the early, screening phase of the search: clearly stated #research goals, the most compelling motivation, and the general approach you intend to take.

Pay attention to the layout. 

Keep the number of fonts to a minimum, but make sure the various sections and ideas are set off by plenty of white space, well-chosen section headings, etc. Bulleted lists are good; page-long paragraphs, bad. And for gosh’s sake, use your spell checker.

Use good graphics. 

A good figure, displayed prominently and captioned carefully, is worth, say, a couple hundred words. "Clear figures and illustrations," writes a respondent, "that can give the reader (skimmer!) a quick (and clear) idea of the proposed #research is a must."

If committee members can get the gist of what you’re saying from a figure without wading through your impenetrable prose, your odds of getting interviewed shoot up.

Focus on the work, not yourself. 

A #research plan should tell how great the science is, not how great you are. Selling yourself is the job of your curriculum vitae and letters of recommendation.

 "Focus on contributions to scientific knowledge, not #research experience and expertise," writes one respondent.

Avoid obvious mistakes. 

Surprisingly, a lot of people mess this up. In her list of fatal errors, one respondent wrote: "Poorly covering or misstating the literature, grammatical or spelling errors, and, near the top of the list, writing #research plans that ask for too much effort on the part of the reader--they should be clear and concise."

Avoid obvious hype. 

You want the value of your #research to speak for itself--avoid exaggerated claims of its importance. "Over hyping," writes a source, "is very dangerous."

How do I make my plan withstand careful scrutiny?

Most of this has already been said:

Avoid mistakes.

Avoid misrepresentations. "A perceived misrepresentation of any kind can doom an application."

Motivate your work (why must this work be done?).

Think it through and present a workable strategy.

Use appropriate detail.

Include preliminary data.

Demonstrate your awareness of other work being done in the field. 

One respondent said, "I have seen applications rejected because they appear to have been produced in a vacuum without reference to other scientists."

Should I include a #research hypothesis?

There is some disagreement here among respondents. One respondent listed a hypothesis among the essential features of a #research plan. Others preferred a broad-brushed approach: "Is the #research question a good question?

 Is it big enough, but with answerable individual questions so that the question generates a #research path that could be followed for some time?" Including a hypothesis is unlikely to hurt you (assuming it's done effectively), and it'll keep you in the running at institutions where a hypothesis is required.

Other advice

Present more than one good idea. 

Even the best idea might fail to pan out, so you need to have a backup. Furthermore, presenting more than one idea will help convince the committee that you aren't a one-trick pony. Your #research plan should be coherent, with a theme common to all your work, but not so close that they seem to be shades of the same idea.

Customize your #research plan to the institution you're applying for. 

Let’s go now to Part Two here we will discuss about #sample Planning

In order to answer the #research questions, it is doubtful that #researcher should be able to collect data from all cases. Thus, there is a need to select a #sample.

A sampling plan is a term widely used in #research studies that provide an outline on the basis of which #research is conducted. It tells which category is to be surveyed, what should be the #sample size and how the respondents should be chosen out of the population.

 For example, if a drug manufacturer would like to #research the adverse side effects of a drug on the population of the country, it is close to impossible to be able to conduct a #research study that involves everyone. In this case, the #researcher decides a #sample of people from each demographic and then conducts the #research on them which gives them an indicative feedback on the behavior of the drug on the population.

Sampling plan is a base from which the #research starts and includes the following three major decisions:

1.    What should be the Sampling unit i.e. choosing the category of the population to be surveyed is the first and the foremost decision in a sampling plan that initiates the #research.

Example  In the case of Banking industry, should the sampling unit consist of current account holders, saving account holders, or both? Should it include male or female account holders? These decisions once made the then sampling frame is #designed to give everyone in the target population equal chance of being #sampled.

2.    The second decision in sampling plan is determining the size of the #sample i.e. how many objects in the #sample is to be surveyed. Generally, “the larger the #sample size, the more is the reliability” and therefore, #researchers try to cover as many #samples as possible.

3.    The final decision that completes the sampling plan is selecting the sampling procedure i.e. which #method can be used such that every object in the population has an equal chance of being selected. Generally, the #researchers use the probability sampling to determine the objects to be chosen as these represents the #sample more accurately.

Following are the probability #samples:

§  Simple Random #sample– where every item of the #sample has an equal chance of getting selected.

§  Stratified #sample– In this, the population is divided into mutually exclusive groups viz. age group and then the choice is made randomly from each group.

§  Cluster #sample– It is also called area sampling, here the population is divided on the basis of location viz. city and then selected randomly.

In case the #researcher finds probability #samples to be costly and time-consuming then the #researcher can make use of non-probability #samples which means by choice.

Following are the non-probability #samples:

§  Convenience #sample– Here, the #researcher selects the easiest and accessible population member.

§  Judgment #sample-Here, the #researcher selects those members of the population whom he thinks to contribute the accurate information.

§  Quota #sample-Here, the #researcher interviews the fixed number of members of each category.

Now we are going dipper discuss about sampling.

Now we will discuss different Types of Sampling #methods 

Any #research study requires two essential types of sampling. They are:

1.    Probability Sampling: 

Probability sampling s a sampling #method that selects random members of a population by setting a few selection criteria. These selection parameters allow every member to have the equal opportunities to be a part of various #samples.

2.    Non-probability Sampling: 

Non probability sampling #method is reliant on a #researcher’s ability to select members at random. This sampling #method is not a fixed or pre-defined selection process which makes it difficult for all elements of a population to have equal opportunities to be included in a #sample.

 

 

We are now going in-depth about sampling discussion

Probability Sampling is a sampling technique in which #sample from a larger population are chosen using a #method based on the theory of probability. This sampling #method considers every member of the population and forms #samples on the basis of a fixed process.

For example, in a population of 1000 members, each of these members will have 1/1000 chances of being selected to be a part of a #sample. It gets rid of bias in the population and gives a fair chance to all members to be included in the #sample.

There are 4 types of probability sampling technique:

·         Simple Random Sampling: 

One of the best probability sampling techniques that helps in saving time and resources, is the Simple Random Sampling #method.

It is a trustworthy #method of obtaining information where every single member of a population is chosen randomly, merely by chance and each individual has the exact same probability of being chosen to be a part of a #sample.

For example, in an organization of 500 employees, if the HR team decides on conducting team building activities, it is highly likely that they would prefer picking chits out of a bowl. In this case, each of the 500 employees has an equal opportunity of being selected.

·         Cluster Sampling:

 Cluster sampling is a #method where the #researchers divide the entire population into sections or clusters that represent a population. Clusters are identified and included in a #sample on the basis of defining demographic parameters such as age, location, sex etc. 

which makes it extremely easy for a survey creator to derive effective inference from the feedback.

·         Systematic Sampling:

 Using systematic sampling #method, members of a #sample are chosen at regular intervals of a population. It requires selection of a starting point for the #sample and #sample size that can be repeated at regular intervals.

 This type of sampling #method has a predefined interval and hence this sampling technique is the least time-consuming.

For example, a #researcher intends to collect a systematic #sample of 500 people in a population of 5000.

Each element of the population will be numbered from 1-5000 and every 10th individual will be chosen to be a part of the #sample (Total population/ #sample Size = 5000/500 = 10).

·         Stratified Random Sampling:

 Stratified Random sampling is a #method where the population can be divided into smaller groups, that don’t overlap but represent the entire population together.

While sampling, these groups can be organized and then draw a #sample from each group separately.

For example, a #researcher looking to analyze the characteristics of people belonging to different annual income divisions, will create strata (groups) according to annual family income such as – Less than $20,000, $21,000 – $30,000, $31,000 to $40,000, $41,000 to $50,000 etc.

and people belonging to different income groups can be observed to draw conclusions of which income strata have which characteristics. Marketers can analyze which income groups to target and which ones to eliminate in order to create a roadmap that would definitely bear fruitful results.

How to Use of the Probability Sampling #method

There are multiple uses of the probability sampling #method. They are:

·         Reduce #sample Bias: 

Using the probability sampling #method, the bias in the #sample derived from a population is negligible to non-existent. The selection of the #sample largely depicts the understanding and the inference of the #researcher. Probability sampling leads to higher quality data collection as the population is appropriately represented by the #sample.

·         Diverse Population:

 When the population is large and diverse, it is important to have adequate representation so that the data is not skewed towards one demographic. For example, if Square would like to understand the people that could their point-of-sale devices, a survey conducted from a #sample of people across different industries and socio-economic backgrounds, helps.

·         Create an Accurate #sample: 

Probability sampling helps the #researchers plan and create an accurate #sample. This helps to obtain well-defined data.

There are different Types of Sampling:  one of it is Non-probability Sampling #methods

The non-probability #method is a sampling #method that involves a collection of feedback on the basis of a #researcher or statistician’s #sample selection capabilities and not on a fixed selection process.

 In most situations, output of a survey conducted with a non-probable #sample leads to skewed results, which may not totally represent the desired target population.

But, there are situations such as the preliminary stages of #research or where there are cost constraints for conducting #research, where non-probability sampling will be much more effective than the other type.

There are 4 types of non-probability sampling which will explain the purpose of this sampling #method in a better manner:

·         Convenience sampling: 

This #method is dependent on the ease of access to subjects such as surveying customers at a mall or passers-by on a busy street. It is usually termed as convenience sampling, as it’s carried out on the basis of how easy is it for a #researcher to get in touch with the subjects. #researchers have nearly no authority over selecting elements of the #sample and it’s purely done on the basis of proximity and not representativeness. 

This non-probability sampling #method is used when there are time and cost limitations in collecting feedback. In situations where there are resource limitations such as the initial stages of #research, convenience sampling is used.

For example, startups and NGOs usually conduct convenience sampling at a mall to distribute leaflets of upcoming events or promotion of a cause – they do that by standing at the entrance of the mall and giving out pamphlets randomly.

·         Judgmental or Purposive Sampling:

 In judgemental or purposive sampling, the #sample is formed by the discretion of the judge purely considering the purpose of study along with the understanding of target audience.

 Also known as deliberate sampling, the participants are selected solely on the basis of #research requirements and elements who do not suffice the purpose are kept out of the #sample. 

For instance, when #researchers want to understand the thought process of people who are interested in studying for their master’s degree. The selection criteria will be: “Are you interested in studying for Masters in …?” and those who respond with a “No” will be excluded from the #sample.

·         Snowball sampling: 

Snowball sampling is a sampling #method that is used in studies which need to be carried out to understand subjects which are difficult to trace. For example, it will be extremely challenging to survey shelterless people or illegal immigrants.

In such cases, using the snowball theory, #researchers can track a few of that particular category to interview and results will be derived on that basis.

·          This sampling #method is implemented in situations where the topic is highly sensitive and not openly discussed such as conducting surveys to gather information about HIV Aids.

Not many victims will readily respond to the questions but #researchers can contact people they might know or volunteers associated with the cause to get in touch with the victims and collect information.

·         Quota sampling:

  In Quota sampling, selection of members in this sampling technique happens on basis of a pre-set standard. In this case, as a #sample is formed on basis of specific attributes,

 the created #sample will have the same attributes that are found in the total population. It is an extremely quick #method of collecting #samples.

Use of the Non-Probability Sampling #method

There are multiple uses of the non-probability sampling #method. They are:

·         Create a hypothesis: 

The non-probability sampling #method is used to create a hypothesis when limited to no prior information is  available. This #method helps with immediate return of data and helps to build a base for any further #research.

·         Exploratory #research: 

This sampling technique is widely used when #researchers aim at conducting qualitative #research, pilot studies or exploratory #research.

·         Budget and time constraints:

 The non-probability #method when there are budget and time constraints and some preliminary data has to be collected. Since the survey #design is not rigid, it is easier to pick respondents at random and have them take the survey or questionnaire.

Thus, a #researcher can select any type of #sample according to his convenience provided it fulfills the purpose for which the #research is conducted.

Now we reach  to Final part here we will discuss about how effectively we write & #design #research whole process which will help you for further final move on #research Development area.

In your thesis or dissertation, you will have to discuss the #methods you used to do your #research. The #methodology or #methods section explains what you did and how you did it, allowing readers to evaluate the reliability and validity of the #research. It should include:

• The type of #research you did

• How you collected your data

• How you analyzed your data

• Any tools or materials you used in the #research

• Your rationale for choosing these #methods

Step 1: Explain your #methodological approach

Begin by introducing your overall approach to the #research. What #research problem or question did you investigate, and what kind of data did you need to answer it?

• Quantitative #methods (e.g. surveys) are best for measuring, ranking, categorizing, identifying patterns and making generalizations

• Qualitative #methods (e.g. interviews) are best for describing, interpreting, contextualizing, and gaining in-depth insight into specific concepts or phenomena

• Mixed #methods allow for a combination of numerical measurement and in-depth exploration

Depending on your discipline and approach, you might also begin with a discussion of the rationale and assumptions underpinning your #methodology.

• Was your aim to address a practical or a theoretical #research problem?

• Why is this the most suitable approach to answering your #research questions?

• Is this a standard #methodology in your field or does it require justification?

• Were there any ethical or philosophical considerations?

• What are the criteria for validity and reliability in this type of #research?

In a quantitative experimental study, you might aim to produce generalizable knowledge about the causes of a phenomenon. Valid #research requires a carefully #designed study with a representative #sample and controlled variables that can be replicated by other #researchers. In a qualitative ethnography,

 you might aim to produce contextual real-world knowledge about the behaviors, social structures and shared beliefs of a specific group of people.

As this #methodology is less controlled and more interpretive, you will need to reflect on your position as #researcher, taking into account how your participation and perception might have influenced the results.

Step 2: Describe your #methods of data collection

Once you have introduced your overall #methodological approach, you should give full details of the #research #methods you used. Outline the tools, procedures and materials you used to gather data, and the criteria you used to select participants or sources.

Quantitative #methods

 Surveys

Describe where, when and how the survey was conducted.

• How did you #design the questions and what form did they take (e.g. multiple choice, rating scale)?

• What sampling #method did you use to select participants?

• Did you conduct surveys by phone, mail, online or in person, and how long did participants have to respond?

• What was the #sample size and response rate?

You might want to include the full questionnaire as an appendix so that your reader can see exactly what data was collected.

 Experiments

Give full details of the tools, techniques and procedures you used to conduct the experiment.

• How did you #design the experiment?

• How did you recruit participants?

• How did you manipulate and measure the variables?

• What tools or technologies did you use in the experiment?

In experimental #research, it is especially important to give enough detail for another #researcher to reproduce your results.

 Existing data

Explain how you gathered and selected material (such as publications or archival data) for inclusion in your analysis.

• Where did you source the material?

• How was the data originally produced?

• What criteria did you use to select material (e.g. date range)?

Quantitative #methods example The survey consisted of 5 multiple-choice questions and 10 questions that the respondents had to answer with a 7-point Likert scale. The aim was to conduct the survey with 350 customers of Company X on the company premises in The Hague from 4-8 July 2017 between 11:00 and 15:00.

A customer was defined as a person who had purchased a product from Company X on the day of questioning. Participants were given 5 minutes to fill in the survey anonymously, and 408 customers responded. Because not all surveys were fully completed, 371 survey results were included in the analysis.

Qualitative #methods

 Interviews or focus groups

Describe where, when and how the interviews were conducted.

• How did you find and select participants?

• How many people took part?

• What form did the interviews take (structured, semi-structured, unstructured)?

• How long were the interviews and how were they recorded?

 Participant observation

Describe where, when and how you conducted the observation.

• What group or community did you observe and how did you gain access to them?

• How long did you spend conducting the #research and where was it located?

• How did you record your data (e.g. audiovisual recordings, note-taking)?

 Existing data

Explain how you selected case study materials (such as texts or images) for the focus of your analysis.

• What type of materials did you analyze?

• How did you collect and select them?

Qualitative #methods example In order to gain a better insight into the possibilities for improvement of the product range, semi-structured interviews were conducted with 8 returning customers from the main target group of Company X.

A returning customer was defined as someone who usually bought products at least twice a week from Company X. The surveys were used to select participants who belonged to the target group (20-45 years old). Interviews were conducted in a small office next to the cash register, and lasted approximately 20 minutes each.

Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

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Step 3: Describe your #methods of analysis

Next, you should indicate how you processed and analyzed the data. Avoid going into too much detail—you should not start presenting or discussing any of your results at this stage.

Quantitative #methods

In quantitative #research, your analysis will be based on numbers. In the #methods section you might include:

• How you prepared the data before analyzing it (e.g. checking for missing data, removing outliers, transforming variables)

• Which software you used to analyze the data (e.g. SPSS or Stata)

• Which statistical #methods you used (e.g. two-tailed t-test, simple linear regression)

Quantitative #methods example before analysis the gathered data was prepared. The dataset was checked for missing data and outliers. For this the “outlier labeling rule” was used.

All values outside the calculated range were considered outliers. The data was then analyzed using statistical software SPSS.

Qualitative #methods

In qualitative #research, your analysis will be based on language, images and observations (often involving some form of textual analysis). Specific #methods might include:

• Content analysis: categorizing and discussing the meaning of words, phrases and sentences

• Thematic analysis: coding and closely examining the data to identify broad themes and patterns

• Discourse analysis: studying communication and meaning in relation to their social context

Qualitative #methods example the interviews were transcribed and thematic analysis was conducted. This involved coding all the data before identifying and reviewing six key themes.

Each theme was examined to gain an understanding of participants’ perceptions and motivations.

Step 4: Evaluate and justify your #methodological choices

Your #methodology should make the case for why you chose these particular #methods, especially if you did not take the most standard approach to your topic.

Discuss why other #methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

You can acknowledge limitations or weaknesses in the approach you chose, but justify why these were outweighed by the strengths.

Lab-based experiments can’t always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables.

Unstructured interviews usually produce results that cannot be generalized beyond the #sample group, but they provide a more in-depth understanding of participants’ perceptions, motivations and emotions.

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Reference:

https://www.scribbr.com/#research-process/#research-#design/

https://www.nyu.edu/classes/bkg/#methods/005847ch1.pdf

https://scholarshipfellow.com/#research-#design-types-#research-#design/

https://www.douban.com/note/191741822/

https://advance.uncc.edu/sites/advance.uncc.edu/files/media/Persuasive%20argument%20to%20get%20resources_needed-1.pdf

https://#research-#methodology.net/#research-#methodology/#research-#design/