Representative sample

A representative sample is a tiny amount or a subset of something larger, according to the definition. It exhibits the same characteristics and ratios as a bigger population.

Consider a company that is getting ready to introduce a new product in a US city, for instance. It will be nearly hard to send a survey to everyone in the city in order to get their opinions on the features of the product. To handle their comments on the product, researchers gather a small sample of people who will adequately represent the population of the city. We refer to this sample as a representative sample.

Researchers can generalize the knowledge they have gathered to a larger population when they use a representative sample. In terms of time, money, and resources, the majority of psychological and market research studies are inappropriate for gathering data on everyone. In particular, with a big population like a whole country, it is almost impossible to collect data from every single person.

Importance of a representative sample for studies in applied research

• A representative sample will be helpful to you if you want to conduct market research that is effective.
• A representative sample is a small group of individuals who as closely as possible represent a larger group. Then, using a sample of the population that we believe to be the most representative of our target group, we can conduct an online survey, for instance.

How to build a representative sample

1. Probability sampling: This technique involves selecting a sample from a broader population using a procedure based on probability theory. Participant selection must be done at random for them to be regarded as a probability sample.
2. Non-probability sampling is a sampling method in which the researcher chooses samples based on his or her personal assessment rather than through random selection. In contrast to probability sampling, which gives every member of the population a known chance of being chosen, non-probability sampling gives only some population members a chance of taking part in the study.

When a sample is not representative, we have a sampling error known as the margin of error. If we want a representative sample of 100 employees, we must select an equal number of men and women. For example, if we have a sample that is biased toward a specific genre, we will have a sample error.

The sample size is important, but it does not ensure that it accurately represents the population that we require. More than size, representativeness is linked to the sampling frame, or the list from which people are drawn, as in a survey. As a result, we must take care to include people from our target audience in that list in order to claim that it is a representative sample.

 Random sampling and ways of obtaining a Random sampling

Random sampling is a technique for selecting a sample of observations from a population in order to draw conclusions about the population. It is also known as probability sampling. Non-probability sampling or Non-random sampling is the opposite of this sample. Simple random sample, stratified sampling, cluster sampling, and multistage sampling are the main varieties of this sampling. Convenience samples are often known as non-arbitrary samples in sampling methodologies.

Probability sampling's key characteristic is that the selection of data must be 'random' in a way that prevents them from significantly diverging from unsampled observations. Here, we presume that the data used in statistical investigations was drawn using random sampling.

Type of Random Sampling

The random sampling technique makes use of a random selection in some way. In this strategy, everyone who qualifies can select a sample from the entire sample space. It is a costly and time-consuming approach. Utilizing probability sampling has the benefit of guaranteeing that the sample will accurately reflect the population. There are four main subtypes of this sampling technique:

1. Simple Random Sampling
2. Systematic Sampling
3. Stratified Sampling
4. Clustered Sampling

Simple random sampling

With this sampling technique, every component of the population has an equal and likely chance of being included in the sample (for example, each member in a group is marked with a specific number). This method is referred to as "Method of Chance Selection" since the choice of an item is entirely dependent on possibilities. Additionally, a large sample size was used, and a random object was chosen. The term "Representative Sampling" is a result.

Systematic Random Sampling

In this method, after a certain sampling period, the other methods are picked along with the random selection point to select the items from the target population. The ratio of the entire population to the necessary population size is what it is equal to.

Stratified Random Sampling

In this sampling technique, a population is broken down into smaller groups in order to collect a simple random sample from each group and complete the sampling procedure (for example, number of girls in a class of 50 strength). Strata are the collective nouns for these little groups. Several characteristics of the population are used to generate the tiny group. The researcher selects a sample at random after segmenting the population into smaller groups.

Clustered Sampling

The population is divided into a high number of subgroups in cluster sampling, unlike stratified sampling (for example, hundreds of thousands of strata or subgroups). After that, simple random samples are taken from some of these subgroups that were randomly selected. The term "clusters" refers to these subdivisions. Essentially, it is used to reduce the cost of data compilation.

Stratified random sampling differs from Cluster - random sampling

 Stratified random sampling differs from Cluster - random sampling

 Stratified random sampling

Stratified sampling is a type of probability sampling that divides a population into strata and draws members of the sample at random from these strata. In stratified sampling, the strata must be homogeneous, collectively exhaustive, and mutually exclusive. The strata must define a subset of the population.

Furthermore the sample members must be distinct, that is, each element must belong to one and only one stratum in the population. This implies that the samples must include the entire population. In each stratum, simple

Examples of Stratified Sampling

Stratified sampling is more representative of the population. When you anticipate that the subgroup you're studying will have different mean values for variables, stratified sampling is a better sampling method to use.

Eg: You want to know how an MBA degree can affect the wage disparities between different gender identities in your company. Based on the employee list, you estimate that only a small percentage of the company's employees are MBA graduates. So you use stratified sampling to compare men, women, and other gender identities who have an MBA to those who do not.

Cluster - random sampling

Cluster sampling is a technique used in market research tools when homogeneity is external but heterogeneity is internal within clusters/groupings. It is the process of dividing a population into multiple groups/clusters.

Cluster sampling is commonly used to reduce the number of interviews and costs while maintaining the desired accuracy. In a fixed sample size, the chance of random error is reduced when the majority of heterogeneity is internal within the group. The total population is divided into clusters, and a simple random sample of the cluster is chosen, followed by a sample of the elements in each of these clusters.

Cluster sampling methods include single-stage, two-stage, and multi-stage sampling. The number of steps required to create the desired sample determines the method used.

Examples of Cluster Sampling 

Because it is less expensive, cluster sampling is advantageous when a large population requires a survey, as previously stated. As a result, one type of cluster sampling is area sampling. It is also used for estimation in high-mortality cases such as wars, famines, and natural diseases.

Respondents are divided into clusters within a local area during the cluster sampling process. However, precision in the estimate is also required, for which the sample size must be increased.

1. One-stage cluster sampling example: Assume the retail store where you work wants to know how many people in town buy their product.

The shop divides the town into neighborhoods and randomly selects people to form cluster samples. Everyone in the neighborhood can take part in the study.

2. Two-stage cluster sampling example: Assume a restaurant owner wants to know how all of its restaurant branches are performing. The owner groups the branches based on their location and then randomly selects samples from the clusters to study the performance.

All the above information highlights the difference between the two categories of Sampling.

• Cluster sampling is done on a population of clusters, so each cluster/group is considered a sampling unit.
• Elements within each stratum are sampled in Stratified Sampling.
• Only selected clusters are sampled in Cluster Sampling.
• A random sample is drawn from each stratum in Stratified Sampling.
• The goal of Cluster Sampling is to reduce sampling costs while increasing sampling efficiency.
• The goal of Stratified Sampling is to increase precision in order to reduce error.

Distinguish between Sample and Population

 Distinguish between a Sample and Population

When we hear the word "population," the first image that comes to mind is one of a sizable crowd of people. Similar to this, the term "population" in statistics designates a sizable group made up of elements sharing at least one characteristic. The phrase is frequently used in opposition to the sample, which is just a subset of the population chosen at random to represent the complete group.

Population is the whole of people, entities, things, and anything else that can be conceived and has particular attributes. Instead, the sample is a limited subset of the population that is chosen through a methodical process in order to determine the features of the parent set. The differences between a sample and a population are discussed in the article that is provided below.

The difference between population and sample can be drawn clearly on the following grounds:

The term "population" refers to the totality of all elements in the cosmos that share a common trait. The term "sample" refers to a subset of the population members selected to take part in the study.

Every component of the entire group is included in the population. However, a sample only includes a small portion of the population.

While the measure of a sample observation is referred to as a statistic, a population feature based on all units is referred to as a parameter.

Censuses or comprehensive enumerations are processes that involve gathering data from every unit of the population. The sample survey, on the other hand, is carried out to collect data from the sample using the sampling method.

In a population, the emphasis is on identifying the elements' characteristics, whereas in a sample, the emphasis is on drawing generalizations about the elements of the population that the sample was drawn from.

Despite the above differences, it is also true that sample and population are related, i.e. sample is drawn from the population, so sample may not exist in the absence of the population. Furthermore, the sample's primary goal is to make statistical inferences about the population, which should be as accurate as possible. The greater the sample size, the higher the level of generalization accuracy.

COMPARISON	POPULATION	SAMPLE
Meaning	Population refers to the collection of all elements possessing common characteristics, that comprises universe.	Sample means a subgroup of the members of population chosen for participation in the study.
Includes	Each and every unit of the group.	Only a handful of units of population.
Characteristic	Parameter	Statistic
Data collection	Complete enumeration or census	Sample survey or sampling
Focus on	Identifying the characteristics.	Making inferences about population.

WHY A LITERATURE REVIEW IS VALUE OF?

WHY A LITERATURE REVIEW IS VALUE OF?

Any literature review should have the objective of synthesizing and summarizing the theories and arguments that already exist in the topic without making any new discoveries. They aid the researcher in even turning the wheels of the research issue because they are based on existing knowledge. Only thorough understanding of the specific flaws in the current findings makes it possible to overcome them. The literature review outlines the path that other studies should take to be successful.

Contrary to popular assumption, literature reviews don't actually do anything more than summarize the sources that were used in the research. In addition, a lot of authors of scientific publications think that their works are only summaries of the study that has been done on the subject at hand. Instead, it makes use of publicly available data from important and relevant sources, including

• scholarly works
• scholarly publications
• current research in the topic reputable schools of thinking
• reputable scientific journals' pertinent articles

and many more for a subject of research, a hypothesis, or a specific issue to do the following:

• summarize everything into a concise account.
• Restructure and reorganize the information to create a synopsis.
• An analysis of a theory, school of thought, or set of concepts
• Make the authors aware of their level of expertise in the relevant topic.
• Appraise
• Identify
• Evaluate
• Encapsulate
• Correlate
• Contrast and compare

It is possible to distill the value of literature reviews in scientific articles into an analytical characteristic to permit their multifaceted significance. It benefits the validity of the study in a number of ways:

• establishes the consistency in knowledge and the applicability of existing materials by providing the interpretation of existing literature in light of current advancements in the field.
• By charting their intellectual advancement, it assists in determining the influence of the most recent material in the field.
• To establish facts, it draws attention to the dialectics of inconsistencies between diverse theories within the area.

• The research gaps that were initially examined are subsequently investigated to determine the most recent facts and theories to advance the subject.

POTENTIAL RESEARCH PROBLEMS IN EDUCATION

 POTENTIAL RESEARCH PROBLEMS IN EDUCATION

A research problem is a claim about an area of interest, an issue that needs to be resolved, a challenge that needs to be overcome, or a perplexing topic that appears in academic literature, in theory, or in practice and necessitates thoughtful analysis and inquiry.

As an alternative, current literature, reports, or databases from a particular field might be reviewed to spot research issues. Potential research issues are frequently raised in the section of "recommendations for the future studies" found at the end of journal articles or doctoral dissertations.

HERE ARE SOME POTENTIAL RESEARCH PROBLEMS IN EDUCATION

1. The future of didactics
2. Teaching digital literacy
3. What is “learning loss”?
4. Augmented reality in the classroom
5. Real-time performance data in education
6. Cognitive science and learning environments
7. Ways of monitoring students’ mental health
8. Girls’ education and empowerment
9. Mental effects of distance learning
10. Online teacher-parent communication
11. Distant education in the era of COVID-19 pandemic
12. The role of technology in distant learning
13. Student-student communication in distance education

How to Choose an Education Research Topic?

The following list of suggestions on how to choose a topic might be quite helpful if you have decided to begin working on an educational research project. It is the initial writing step on your path to a successful paper.

Study your earlier works. Look at the papers and projects you completed previously. It's possible that you brought up a subject there that merits a more thorough investigation.

Continue to be up to date with education's latest developments. Several government reports may be found that discuss their strategy. The most pertinent subjects that require development in the near future can be found there very easily.

"Enter the field," was ordered. Visiting some schools and universities can be helpful even if you don't work in the field of education. Observing how each strategy is used in practice provides some thought-provoking material.

Study the literature. Reading the works of some well-known authors could serve as inspiration for you.

Investigate global standards. Don't be scared to venture beyond what the system of public education allows. There are countless new methods being used in various branches of education.

 

ETHICAL RESEARCH

 ETHICAL RESEARCH

The responsible conduct of research is governed by research ethics. Additionally, it trains and supervises researchers to achieve a high ethical standard. The general outline of several ethical precepts is as follows:

Honesty: Provide accurate data, results, techniques, and publishing status information. Don't make up, falsify, or represent data incorrectly.

Aim for objectivity in all areas of research, including grant writing, grant review, peer review, personnel decisions, data analysis, data interpretation, and expert testimony.

Integrity means keeping your word, acting really, and making an effort to be consistent in your thoughts and deeds.

Be attentive; avoid being negligent and making thoughtless mistakes; thoroughly review your own work as well as that of your peers. Record your research activities thoroughly.

Respect for Intellectual Property: Be mindful of copyrights, patents, and other types of IP. Without authorization, never use unpublished data, techniques, or outcomes. Where credit is due, give it. Never use plagiarism.

Protect private information, including patient records, commercial or military secrets, papers or grants that have been submitted for publication, as well as employee records.

Responsible Publication: Don't just publish to further your own career; publish to advance research and scholarship. Stay away from redundant and useless publications.

Mentoring that is responsible: Assist in guiding, mentoring, and teaching students. Encourage their welfare and give them the freedom to decide for themselves.

Respect for Colleagues: Show respect for and fair treatment of your colleagues.

Social responsibility: Make an effort to advance social welfare and stop or lessen social evils through activism, research, and public outreach.

Non-Discrimination: Refrain from treating colleagues or students unfairly on the grounds of their sexual orientation, race, ethnicity, or any other characteristics unrelated to their scientific integrity and competence.

Competence: Take initiatives to advance scientific competency broadly; maintain and advance your own professional competence and expertise through lifetime learning.

Legality: Comprehend and abide by all applicable laws, rules, and regulations.

Animal Care: When employing animals in research, treat them with the appropriate respect and care. Avoid using animals in pointless or poorly thought-out research.

Human Subjects Protection: Minimize risks and damages while maximizing benefits when using human subjects in research; respect for individual liberty, privacy, and dignity.