Trending November 2023 # What Is A Stock? A Beginner’S Guide # Suggested December 2023 # Top 14 Popular

You are reading the article What Is A Stock? A Beginner’S Guide updated in November 2023 on the website Moimoishop.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested December 2023 What Is A Stock? A Beginner’S Guide

Stock

A claim over a company’s assets and its ownership

Written by

Andrew Loo

Published March 3, 2023

Updated May 13, 2023

What is a Stock?

When a person owns stock in a company, the individual is called a shareholder and is eligible to claim part of the company’s residual assets and earnings (should the company ever have to dissolve). A shareholder may also be referred to as a stockholder. The terms “stock,” “shares,” and “equity” are used interchangeably in modern financial language. The stock market consists of exchanges where investors can buy and sell individual shares of a company.

Benefits of Owning Stocks

There are many potential benefits to owning stocks or shares in a company.

1. Claim on assets

A shareholder has a claim on assets of a company it has stock in. However, the claims on assets are relevant only when the company faces liquidation. In that event, all of the company’s assets and liabilities are counted, and after all creditors are paid, the shareholders can claim what is left. This is the reason that equity (stocks) investments are considered higher risk than debt (credit, loans, and bonds) because creditors are paid before equity holders, and if there are no assets left after the debt is paid, the equity holders may receive nothing.

2. Dividends and capital gains

A stockholder may also receive earnings, which are paid in the form of dividends. The company can decide the amount of dividends to be paid in one period (such as one quarter or one year), or it can decide to retain all of the earnings to expand the business further. Aside from dividends, the stockholder can also enjoy capital gains from stock price appreciation.

3. Power to vote

Another powerful feature of stock ownership is that shareholders are entitled to vote for management changes if the company is mismanaged. The executive board of a company will hold annual meetings to report overall company performance. They disclose plans for future period operations and management decisions. Should investors and stockholders disagree with the company’s current operation or future plans, they have the power to negotiate changes in management or business strategy.

4. Limited liability

Lastly, when a person owns shares of a company, the nature of ownership is limited. Should the company go bankrupt, shareholders are not personally liable for any loss.

Risks of Owning Stock

Along with the benefits of stock ownership, there are also risks that investors have to consider.

1. Loss of capital

There is no guarantee that a stock’s price will move up. An investor may buy shares at $50 during an IPO, but find that the shares move down to $20 as the company begins to perform badly, for example.

2. No liquidation preference

When a company liquidates, creditors are paid before equity holders. In most cases, a company will only liquidate when it has very little assets left to operate. In most cases, that means that there will be no assets left for equity holders once creditors are paid off.

3. Irrelevant power to vote

While retail investors technically have voting rights in executive board meetings, in practice they usually have very limited influence or power. The majority shareholder typically determines the outcome of all votes at shareholder meetings.

Modern Stock Trading

What Affects Share Prices on the Stock Market?

There are many factors that affect share prices. These may include the global economy, sector performance, government policies, natural disasters, and other factors. Investor sentiment — how investors feel about the company’s future prospects — often plays a large part in dictating the price. If investors are confident about a company’s ability to rapidly grow and eventually produce large returns on investment, then the company’s stock price may be well above its current intrinsic, or actual, value.

Two of the most examined financial ratios used to evaluate stocks are the following:

Revenue growth

Earnings growth

Revenue growth tells analysts about the sales performance of the company’s products or services and generally indicates whether or not its customers love what it does. Earnings reveal how efficiently the company manages its operations and resources to produce profits. Both are very high-level indicators that can be used as references on whether or not to purchase shares. However, stock analysts also use many other financial ratios and tools to help investors profit from equity trading.

No matter what your job in the financial industry, you will be involved with stocks in one way or another.

Additional Resources

Stock Market Guide

Investing for Beginners

Exchange-Traded Funds (ETFs)

See all equities resources

You're reading What Is A Stock? A Beginner’S Guide

What Is A Research Design

A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:

Your overall research objectives and approach

Whether you’ll rely on primary research or secondary research

Your sampling methods or criteria for selecting subjects

Your data collection methods

The procedures you’ll follow to collect data

Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Step 1: Consider your aims and approach

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

Research question exampleHow can teachers adapt their lessons for effective remote learning?

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative approach Quantitative approach

Understand subjective experiences, beliefs, and concepts

Gain in-depth knowledge of a specific context or culture

Explore under-researched problems and generate new ideas

Measure different types of variables and describe frequencies, averages, and correlations

Test hypotheses about relationships between variables

Test the effectiveness of a new treatment, program or product

Qualitative research designs tend to be more flexible and inductive, allowing you to adjust your approach based on what you find throughout the research process.

Qualitative research exampleIf you want to generate new ideas for online teaching strategies, a qualitative approach would make the most sense. You can use this type of research to explore exactly what teachers and students struggle with in remote classes. Quantitative research exampleIf you want to test the effectiveness of an online teaching method, a quantitative approach is most suitable. You can use this type of research to measure learning outcomes like grades and test scores.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics.

How much time do you have to collect data and write up the research?

Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?

Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?

Will you need ethical approval?

At each stage of the research design process, make sure that your choices are practically feasible.

Step 2: Choose a type of research design

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

Experimental and

quasi-experimental

designs allow you to test cause-and-effect relationships

Descriptive

and

correlational

designs allow you to measure variables and describe relationships between them.

Type of design Purpose and characteristics

Experimental

Used to test causal relationships

Involves manipulating an independent variable and measuring its effect on a dependent variable

Subjects are randomly assigned to groups

Usually conducted in a controlled environment (e.g., a lab)

Quasi-experimental

Used to test causal relationships

Similar to experimental design, but without random assignment

Often involves comparing the outcomes of pre-existing groups

Often conducted in a natural environment (higher ecological validity)

Correlational

Used to test whether (and how strongly) variables are related

Variables are measured without influencing them

Descriptive

Used to describe characteristics, averages, trends, etc

Variables are measured without influencing them

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation).

Correlational design exampleYou could use a correlational design to find out if the rise in online teaching in the past year correlates with any change in test scores.

But this design can’t confirm a causal relationship between the two variables. Any change in test scores could have been influenced by many other variables, such as increased stress and health issues among students and teachers.

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Experimental design exampleIn an experimental design, you could gather a sample of students and then randomly assign half of them to be taught online and the other half to be taught in person, while controlling all other relevant variables.

By comparing their outcomes in test scores, you can be more confident that it was the method of teaching (and not other variables) that caused any change in scores.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Type of design Purpose and characteristics

Case study

Detailed study of a specific subject (e.g., a place, event, organization, etc).

Data can be collected using a variety of sources and methods.

Focuses on gaining a holistic understanding of the case.

Ethnography

Detailed study of the culture of a specific community or group.

Data is collected by extended immersion and close observation.

Focuses on describing and interpreting beliefs, conventions, social dynamics, etc.

Grounded theory

Aims to develop a theory inductively by systematically analyzing qualitative data.

Phenomenology

Aims to understand a phenomenon or event by describing participants’ lived experiences.

What can proofreading do for your paper?

Scribbr editors not only correct grammar and spelling mistakes, but also strengthen your writing by making sure your paper is free of vague language, redundant words, and awkward phrasing.

See editing example

Step 3: Identify your population and sampling method

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Population exampleIf you’re studying the effectiveness of online teaching in the US, it would be very difficult to get a sample that’s representative of all high school students in the country.

To make the research more manageable, and to draw more precise conclusions, you could focus on a narrower population—for example, 9th-grade students in low-income areas of New York.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling. The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling Non-probability sampling

Sample is selected using random methods

Mainly used in quantitative research

Allows you to make strong statistical inferences about the population

Sample selected in a non-random way

Used in both qualitative and quantitative research

Easier to achieve, but more risk of research bias

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Step 4: Choose your data collection methods

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Questionnaires Interviews

More common in quantitative research

May be distributed online, by phone, by mail or in person

Usually offer closed questions with limited options

Consistent data can be collected from many people

More common in qualitative research

Conducted by researcher in person, by phone or online

Usually allow participants to answer in their own words

Ideas can be explored in-depth with a smaller group (e.g., focus group)

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Quantitative observation Qualitative observation

Systematically counting or measuring

Taking detailed notes and writing rich descriptions

All relevant observations can be recorded

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

Field Examples of data collection methods

Media & communication Collecting a sample of texts (e.g., speeches, articles, or social media posts) for data on cultural norms and narratives

Psychology Using technologies like neuroimaging, eye-tracking, or computer-based tasks to collect data on things like attention, emotional response, or reaction time

Education Using tests or assignments to collect data on knowledge and skills

Physical sciences Using scientific instruments to collect data on things like weight, blood pressure, or chemical composition

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

Step 5: Plan your data collection procedures

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations, which events or actions will you count?

ExampleTo measure student participation in an online course, you could record the number of times students ask and answer questions.

If you’re using surveys, which questions will you ask and what range of responses will be offered?

ExampleTo measure teachers’ satisfaction with online learning tools, you could create a questionnaire with a 5-point rating scale.

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

Reliability Validity

Does your measure capture the same concept consistently over time?

Does it produce the same results in different contexts?

Do all questions measure the exact same concept?

Do your measurement materials test all aspects of the concept? (content validity)

Does it correlate with different measures of the same concept? (criterion validity)

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

How many participants do you need for an adequate sample size?

What inclusion and exclusion criteria will you use to identify eligible participants?

How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability).

Step 6: Decide on your data analysis strategies

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis. With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics, you can summarize your sample data in terms of:

The distribution of the data (e.g., the frequency of each score on a test)

The central tendency of the data (e.g., the mean to describe the average score)

The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics, you can:

Make estimates about the population based on your sample data.

Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis.

Approach Characteristics

Thematic analysis

Focuses on the content of the data

Involves coding and organizing the data to identify key themes

Discourse analysis

Focuses on putting the data in context

Involves analyzing different levels of communication (language, structure, tone, etc)

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

What Is A Ceiling Effect?

A ceiling effect occurs when too large a percentage of participants achieve the highest score on a test. In other words, when the scores of the test participants are all clustered near the best possible score, or the “ceiling”, the measurement loses value. This phenomenon is problematic because it defeats the purpose of the test, which is to accurately measure something.

Example: Ceiling effectOn a midterm math exam, in which the highest possible score is 100 points, 90% of the students score 98 out of 100. This means that the majority of the students obtained a top score, and the clustering of the scores near the top is evidence of a ceiling effect. This suggests the exam was too easy.

A ceiling effect can be observed in surveys, standardized tests, or other measurements used in quantitative research.

What is a ceiling effect?

A ceiling effect is a measurement problem that places a limitation to the maximum level an individual can achieve on a test. As a result, there is a discrepancy between a person’s test score and their “true” score, or reality.

Depending on the scientific area, the term signifies one of the following:

A ceiling effect in medicine and pharmacology refers to the phenomenon in which a drug reaches a maximum effect, so that increasing the dosage does not increase its effectiveness. For example, researchers sometimes observe that there is a threshold above which a painkiller has no additional effect. Even if they increase the dosage, there is no added benefit regarding pain relief. In this context, the ceiling effect occurs due to human biology.

A ceiling effect associated with statistics in social sciences refers to the phenomenon in which the majority of the data are close to the upper limit or highest possible score of a test. This means that (almost) all of the test participants achieved the highest (or very near to the highest) score.

What causes a ceiling effect?

In the context of statistics, a ceiling effect can occur in survey data because of the limited ability of survey instruments to accurately measure participants’ true responses, as well as distinguish them from others’ responses. This can be due to:

Efforts to limit response bias. In an attempt to prevent biases like social desirability bias, researchers might create ceiling effects due to the way they phrase the possible responses. For example, when asking respondents about their alcohol consumption, the highest possible option might be “2 drinks per day or more”. This makes it easier for heavy drinkers to fill in the question without feeling too exposed. However, researchers then lose the ability to differentiate between those who consume 3, 4, 6, or more drinks per day.

Instrument design constraints. Due to poor design, a questionnaire might not be able to measure a variable above a certain limit. For example, when a college exam is too easy, everyone will get more or less the same high score. The ceiling effect creates an artificially low threshold, since anyone is able to pass the exam. As a result, the exam fails to measure what it’s supposed to measure (aptitude) beyond a certain (low) level.

Why is the ceiling effect a problem?

Because of the ceiling effect, tests, surveys and other measures fail to capture the true range of values or responses, resulting in little variance in the data.

Ceiling effects cause a number of problems in data analysis including the inability to:

Determine the central tendency of the data, or the true average in a dataset.

Compare the means between two groups, e.g., between a treatment and a control group.

Get an accurate measure of variability, such as standard deviation.

Form conclusions about the effect of the independent variable on any dependent variables.

Rank individuals according to their score.

Overall, a ceiling effect hinders the accurate interpretation of data and can render results meaningless.

Ceiling effect examples

Ceiling effects can be observed in surveys that include response categories that do not fully capture the range of possible answers above a certain point.

Example: Ceiling effect and response biasSuppose that you are researching what residents in an area think about the new section of urban motorway constructed nearby. Among your survey questions, there is one concerning income (“What was your total household income last year?”) You present respondents with different income categories to choose from:

less than $50,000

$50,000-$100,000

Over $100,000

Although this is a discreet way to ask a sensitive question and avoid response bias, there is also a downside to it. Setting the top range like this creates an artificial cutoff point, or ceiling, beyond which it is not possible to measure income. In other words, you can’t differentiate between someone that makes $100,000, $400,000 or $1 million per year.

Because the income range is not inclusive of the true values above that point, this results in inaccurate measurement and a ceiling effect.

A ceiling effect can create a low threshold, making it easy for participants to reach the highest possible score on a test.

Example: Ceiling effect and poor designYou have created a short memory test that assesses participants’ ability to recall information. The test consists of showing five words on a screen. Because most participants can remember all five words, the test exhibits a ceiling effect: you can’t use it to rank participants according to their recall ability. The best approach would be to use an already validated memory test.

How to avoid ceiling effects?

Ceiling effects can impact the quality of your data collection. It’s really important to take the necessary steps to prevent this phenomenon. There are a few strategies you can use to avoid ceiling effects in your research:

Use previously-validated instruments, such as pre-existing questionnaires measuring the concept you are interested in. In this way, you can ensure that the questionnaire will allow you to capture a wide range of responses.

If no such instrument exists, run a pilot survey or experiment to check for ceiling effects. Running a small-scale trial of your survey will give you the opportunity to adjust your questions in case you do notice a ceiling effect.

When your survey includes sensitive or personal topics, like questions about income or drug use, provide anonymity, and don’t set artificial limits on responses. Instead, you could let participants fill in the higher value themselves.

Other types of research bias Frequently asked questions

What is the difference between ceiling and floor effect?

The terms ceiling effect and floor effect are opposites but they refer to the same phenomenon: the clustering of individual survey responses around a certain value. More specifically, ceiling effects occur when a considerable percentage of participants score the best or maximum possible score, while floor effects occur when the opposite happens, i.e., a considerable percentage of participants obtain the worst or minimum available score. This can be observed, for example, when a test is too easy (ceiling effect) or too difficult (floor effect). As a result, researchers can’t use the test to rank participants at either end of the scale.

What is a ceiling effect in pharmacology

In pharmacology a ceiling effect is the point at which an independent variable (the variable being manipulated) is no longer affecting the dependent variable (the variable being measured). This can be seen with analgesic or pain-relieving medication. Even if researchers increase the dosage, there is a certain point beyond which the effectiveness of the medication will no longer increase.

Why is the ceiling effect a problem?

The ceiling effect is a problem in statistical analysis and data interpretation because it restricts the range of values that a variable can take. Due to this, there is a difference between the reported values and the ‘real’ values which means that the survey, test, or other measure used fails to collect accurate data.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

This Scribbr article

Nikolopoulou, K. Retrieved July 10, 2023,

Cite this article

Sources

Show all sources (5)

What Is A Super Integron?

Super-integron term was first applied in 1998 (but without definition) to the integron with a long cassette array on the small chromosome of Vibrio cholerae. The term has since been used for integrons of various cassette array lengths or for integrons on bacterial chromosomes (plasmids).

The use of “super-integron” is now discouraged since its meaning is unclear. In more modern usage, an integron located on a bacterial chromosome is termed a sedentary chromosomal integron, and one associated with transposons or plasmids is called a mobile integron. Two groups of integrons are known: resistance integrons and super-integrons. Gene cassettes in super-integrons encode a variety of different functions.

Super-integrons are located on the bacterial chromosome. The gene cassettes in resistance integrons probably originated from super-integrons. The recent finding of super-integron (SI) structures in the genomes of several bacterial species have expanded their role in genome evolution.

The Vibrio cholerae super integron is gathered in a single chromosomal super-structure harboring hundreds of gene cassettes. A comparison of the cassette contents of super-integrons from remote Vibrio species suggests that most of their cassettes are species-specific.

Many bacterial species belonging to several distinct genera of the γ- and β-proteobacteria undoubtedly carry or show strong evidence for the presence of chromosomal SIs. If each bacterial species harboring a SI has its own cassette pool, the resource in terms of gene cassette availability may be immense.

Super Integron

Our five-decade-long battle against bacteria is a testament to the genetic flexibility of these organisms. Not long after their introduction, we were witnessing the emergence of bacterial resistance to new antimicrobial agents. It is now clearly established that the prevailing strategy adopted by bacteria to evade antimicrobial activity is via acquisition of a gene from an exogenous source that confers resistance by any means. Integrons can now be divided into two major groups: the resistance integrons (RI) and the super-integrons (SI).

RI carry mostly gene cassettes that encode resistance against antibiotics and disinfectants and can be located either on the chromosome or on plasmids. The large chromosomally located integrons, which contain gene cassettes with a variety of functions, belong to the SI group.

SI are not given a specific name. The integron originally designated as class 4 is now named Vibrio cholerae SI. SI have been described for Geobacter sulfurreducens, Listonella pelagia, Nitrosomonas europaea, Pseudomonas alcaligenes, Pseudomonas mendocina, Pseudomonas spp., Pseudomonas stutzeri, Shewanella oneidensis, Shewanella putrefaciens, Treponema denticola, Vibrio anguillarum, Vibrio cholerae, Vibrio fischerii, Vibrio metschnikovii, Vibrio mimicus, Vibrio parahaemolyticus and Xanthomonas campestris.

Three classes of multi-resistant (MR) integrons have been defined based on the homology of the integrase genes and each class appears to be able to acquire the same gene cassettes. The integron platforms are defective for self-transposition but they are often found associated with insertion sequences (ISs), transposons, and/or conjugative plasmids which can serve as vehicles for the intra- and interspecies transmission of genetic material.

The potency of a highly efficient gene capture and expression system in combination with broad host range mobility is self-evident. The proficiency of this partnership is confirmed by the marked differences in codon usage among cassettes within the same integron, indicating that the antibiotic resistance determinants are of diverse origins

Such a system permits bacteria to stockpile exogenous genetic loci and MR integrons harboring up to five different cassettes have been characterized (In30).

Several observations suggest that integron structures impact genome evolution to a much greater extent than initially believed. First, the degree of homology between the three integrase classes (45-58%) suggests that their evolutionary divergence has extended over a longer period than the 50 years of the antibiotic era.

Second, the bias towards the propagation of resistance gene cassettes is likely due to the selective pressure of antibiotic therapy regimes driving the specific capture of resistance cassettes, implying that cassette genesis is not restricted to resistance determinants. It is conceivable that any ORF can be structured as a gene cassette.

Recently a new type of integron, a super-integron (SI) harboring hundreds of cassettes and differing in several ways from the MR integrons, has been identified in the Vibrio cholerae genome. This review focuses on this type of integron and gives the current state of knowledge on their characteristics and distribution.

Conclusion

The gene cassettes found in SI encode a wide variety of different functions, in contrast to the functions of gene cassettes found in RI. The number of resistance genes carried by the same plasmid, and even in the same integron, appears to rise. The integration of virulence factors and resistance determinants on the same plasmid may have even greater implications for public health. These bearers of multi-resistance are likely to remain because the physical association of integrons with other resistance determinants will lead to their continuous selection.

The role of SI in the evolution of bacterial species has been barely touched upon, but their apparent ubiquity suggests that they play an important role in bacterial evolution. The variety of structures found among class 1 integrons and their genetic surroundings after slightly more than half a century of antibiotic usage bears testament to the genetic flexibility and adaptability of the bacterial genome under environmental stress, making these microorganisms ultimate survivors.

What Is A Nexus Device?

HTC Nexus One

What Nexus devices are there?

Motorola Xoom

Nexus naming nightmare

As you can see from the product names mentioned above, Nexus devices don’t have a cohesive naming scheme and that could turn out to be a marketing annoyance in the coming years and I’ll show you why.

The first Nexus handset was the HTCNexus One, which had a rather logical name. We were expecting a Nexus Two to follow, but legend has it that Samsung did not want to be second to anybody. Consequently the second and third Nexus smartphones were called the Nexus S and the Galaxy Nexus, respectively. Finally, the fourth model was unexpectedly baptized the Nexus 4 – not Nexus Four mind you – and we expect this year’s Nexus smartphone to be called the Nexus 5, as long the company making it will not have a different opinion.

Samsung Nexus S

That’s all fine and dandy, but while Nexus smartphones numbers represent their generation, moving along to Nexus tablets will complicate things. The Nexus 7 and Nexus 10 are not the seventh and tenth, respectively, Nexus tablets. They’re the first two, only one is a 7 incher while the other sports a 10-inch display.

In case you don’t see the problem yet, then in couple of years we may have a strange mix of Nexus products including the Nexus 7 smartphone and the third-generation Nexus 7 tablet or Nexus 7 3.0.

What matters though is that you remember that Nexus devices are made only by/for Google.

Nexus vs Android devices

So if Nexus devices are running Android, what are the differences between Nexus smartphones and tablets and all the other Android-running gadgets out there?

Hardware

In the Android universe, it’s not exactly possible to have the latest hardware on a device for more than a few months. Because a variety of worldwide retailers launch new Android gadgets every few months, Nexus devices will not always be the hottest devices in town. That said, you should know that at the time of their original launch, Nexus smartphones sport the latest hardware features available to OEMs, and with few exceptions they’re ready to offer you the same set of specs and features found on top-shelf Android handsets

So Nexus smartphones are usually high-end devices, but they may lack certain features, including microSD support, which happens to be a deal breaker for some, and even LTE support (see the Nexus 4) which could be a problem in the future if the trend continues.

With Nexus tablets, or at least with some of them, things are a bit different. Because it wasn’t able to really compete against the iPad since Apple launched the iOS tablet, and because the Search company received an unexpected hit from Amazon, which released the Kindle Fire tablet in late 2011, a device running a forked Android version stripped off all Google elements and apps, and sold at cost, Google was forced to come out with a budget tablet of its own, the Nexus 7, in mid-2012.

Therefore the Nexus 7 tablet isn’t a high-end device specs-wise. It’s not targeting the iPad directly, not that you’re likely to feel any performance troubles during daily tablet operations because it still sports some great internal components. The Nexus 10, on the other hand, is Google’s first try at directly fighting the iPad, and therefore it’s offering some higher-end features.

Samsung Galaxy Nexus

Software

Buying Nexus vs buying other Android devices

So now that you have an idea what Nexus devices have to offer compared to their Android equivalents, and especially if you’re just moving away from cellphones / featurephones to smartphones and/or tablets, you may be wondering whether it’s better to buy a Nexus or an Android device. You know, since they’re essentially running the same software.

ASUS Nexus 7

But there is no right answer. Going the Nexus way is encouraged if you want to have a pure Google Android experience and have access to the latest software updates without installing custom ROMs based on those software releases. In most cases you’ll run Google’s latest OS version after a few days since it becomes official, although some carriers may still get in the way of timely Nexus updates (see Verizon and Sprint in the USA). You won’t have to deal with custom user interfaces built on top of Android and you won’t have to deal with all the pre-loaded apps from carriers and their partners that you’ll find on other Android handsets and tablets.

Furthermore, if you are a software developer looking to create Android apps and/or bringing your existing mobile apps to the Google Play Store, then owning a Nexus device may be a must, in order for you to stay up to date with the latest Android releases and what not. At the same time, owning a bunch of other Android hardware may be required in order for you to optimize app experience on different smartphones and tablets.

If the timeliness of software updates isn’t such an important factor in your Android device buying decision, then going for any other device isn’t a bad thing either. No matter what your budget is, you’ll be able to afford a new Android handset and/or tablet, and you’ll have a rich mobile experience. You don’t need to buy a Nexus to get that. Not to mention that you may be interested in having a device that comes with a microSD slot or LTE connectivity, which would mean they wouldn’t be available by purchasing the latest Nexus devices out there.

The better acquainted you get with Android on these non-Nexus devices, the more likely you’ll be to try out custom ROMs, and there are a lot of them out there – although we’ll never encourage you to install any unofficial software on any device you own – and you’ll get access to Android updates faster than your carrier wants you to.

Aren’t Nexus devices cheaper though?

LG Nexus 4

How is Google able to pull it off? First off, in the tablet sector it has to fight the Amazon threat, not to mention that Apple has its own cheaper tablet as well, so that’s an area where we can’t expect any price hikes for the foreseeable future.

But what surprised everyone in late 2012 is that Google managed to strike an interesting deal with LG to sell the Nexus 4 for a lot less than anticipated, as long as the device is purchased through its Google Play storefront. The device sells starting at $299 and that’s the off-contract price. But carriers and retailers around the world do not get the same preferential treatment.

Will the trend continue in 2013? That’s certainly something we’re interested to see, because while Google is definitely making waves with its Nexus phones and tablets (with price being an important marketing factor,) it’s also indirectly hurting its partners. All the other Android OEMs don’t have a second revenue stream like the Google Play Store to fall back to and they want to make money from handset and tablet sales. And that’s harder to do when Google is selling hot devices with lower starting prices.

And then there are the carriers, who can’t really afford to offer buyers cheap high-end handsets as the Nexus 4 because subscribers would quickly move from postpaid to prepaid plans, which is not what any mobile operator wants.

On the other hand, the tougher the economy, the more attention one pays to the budget for mobile purchases. With that in mind, getting a brand-new high-end off-contract Nexus 4 smartphone is probably one of the best deals one can look forward to – in fact it’s the only such offer available out there, as no other high-end smartphone will sell for as low under the same conditions (new device, without a subsidy and contract). If only Google had enough Nexus 4 units to go around, right?

Google and Motorola

As you have noticed so far, Google worked with a variety of companies including HTC, Samsung, ASUS and LG to release Nexus devices. And that’s certainly the best business practice for the company. Google can’t just partner up with one Android device maker for Nexus handsets and tablets because at the end of the day Google wants to keep its partners happy in order to have as them making as many Android devices as possible year after year.

However, Google failed to use Motorola to build Nexus devices so far. Starting with May 2012, Motorola is officially a Google subsidiary which means that in theory the two companies could create a plethora of Nexus smartphones and tablets and sell them at cost to hook as many new mobile users into Google’s mobile environment.

Samsung Nexus 10

But Google said it wouldn’t treat Motorola preferentially now that it owns it, and it probably had to show everyone it means that by not creating any Nexus device in partnership with Motorola in 2012. In fact, it looks like it intentionally stayed away from doing so. Moreover, Moto launched several new Android devices in the RAZR family in fall 2012 without equipping them with the then-freshly launched Android 4.1 Jelly Bean OS version.

Google execs did say that the Motorola purchase was mainly for its patent chest needed to fight Apple, Microsoft and anyone else that’s attacking Android, although that kind of didn’t work for the company, attractive negative reactions from U.S. and European regulatory commission that went as far as to initiate inquiries into Google’s patent-related business practices.

Will 2013 be the year of the first Motorola Nexus device? We’ll just have to wait and see.

Nexus devices are here to stay

Google Nexus Q

So do expect to see more Nexus devices in the coming years. And whether you pick one up or not next time you’re buying a smartphone or tablet, it won’t necessarily matter for Google, as long as you choose an Android device.

Will you buy a Nexus devices as soon as possible?

What Is A Fishbone Diagram?

A fishbone diagram is a problem-solving approach that uses a fish-shaped diagram to model possible root causes of problems and troubleshoot possible solutions. It is also called an Ishikawa diagram, after its creator, Kaoru Ishikawa, as well as a herringbone diagram or cause-and-effect diagram.

Fishbone diagrams are often used in root cause analysis, to troubleshoot issues in quality management or product development. They are also used in the fields of nursing and healthcare, or as a brainstorming and mind-mapping technique many students find helpful.

How to make a fishbone diagram

A fishbone diagram is easy to draw, or you can use a template for an online version.

Your fishbone diagram starts out with an issue or problem. This is the “head” of the fish, summarized in a few words or a small phrase.

Next, draw a long arrow, which serves as the fish’s backbone.

From here, you’ll draw the first “bones” directly from the backbone, in the shape of small diagonal lines going right-to-left. These represent the most likely or overarching causes of your problem.

Branching off from each of these first bones, create smaller bones containing contributing information and necessary detail.

When finished, your fishbone diagram should give you a wide-view idea of what the root causes of the issue you’re facing could be, allowing you to rank them or choose which could be most plausible.

NoteBe careful not to make your fish too “bony”! Too many smaller bones or long explanations can lead to confusion and distractions, and defeat the purpose of the exercise in the first place.

Fishbone diagram templates

There are no built-in fishbone diagram templates in Microsoft programs, but we’ve made a few free ones for you to use that you can download below. Alternatively, you can make one yourself using the following steps:

Draw a long arrow from left to right, and add a text box on the right-hand side. These serve as the backbone and the head of the fish.

Next, add lines jutting diagonally from the backbone. These serve as the ribs, or the contributing factors to the main problem.

Next, add horizontal lines jutting from each central line. These serve as the potential causes of the problem.

Lastly, add text boxes to label each function.

You can try your hand at filling one in yourself using the various blank fishbone diagram templates below, in the following formats:

Fishbone diagram template Excel

Download our free Excel template below!

Download Excel template

Fishbone diagram template Word

Download our free Word template below!

Download Word template

Fishbone diagram template PowerPoint

Download our free PowerPoint template below!

Download PowerPoint template

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

Academic style

Vague sentences

Grammar

Style consistency

See an example

Fishbone diagram examples

Fishbone diagrams are used in a variety of settings, both academic and professional. They are particularly popular in healthcare settings, particularly nursing, or in group brainstorm study sessions. In the business world, they are an often-used tool for quality assurance or human resources professionals.

Fishbone diagram example #1: Climate change

Let’s start with an everyday example: what are the main causes of climate change?

Fishbone diagram example #2: Healthcare and nursing

Fishbone diagrams are often used in nursing and healthcare to diagnose patients with unclear symptoms, or to streamline processes or fix ongoing problems. For example: why have surveys shown a decrease in patient satisfaction?

Fishbone diagram example #3: Quality assurance

QA professionals also use fishbone diagrams to troubleshoot usability issues, such as: why is the website down?

Fishbone diagram example #4: HR

Lastly, an HR example: why are employees leaving the company?

Advantages

Great tool for brainstorming and mind-mapping, either individually or in a group project.

Can help identify causal relationships and clarify relationships between variables.

Constant iteration of “why” questions really drills down to root problems and elegantly simplifies even complex issues.

Can lead to incorrect or inconsistent conclusions if the wrong assumptions are made about root causes or the wrong variables are prioritized.

Fishbone diagrams are best suited to short phrases or simple ideas—they can get cluttered and confusing easily.

Best used in the exploratory research phase, since they cannot provide true answers, only suggestions.