MASTERAL IN BUSINESS ADMINISTRATION (MBADEGREE)

Authored by: RODEL SY NAVARRO ISBN:xxxxxxx

Copyright No.:xxxxxxx

TABLE OF CONTENTS1. Pre-MBA Courses

Business Communication

Applied Mathematics Managerial Statistics Financial Accounting Methods of Research

2. Core Courses

Business Ethics Leadership Effectiveness Applied ManagementScience Operations Management

Managerial Accounting Financial Management Principles & Dynamics ofManagement Human ResourceManagement Management Conceptsfor InformationTechnology Marketing Management Economics for Managers

3. Electives

Brand Management Business Intelligence Business and

Professional Discourse Controllership Economic Development Electronic Commerce E-Marketing Entrepreneurship Financial Analysis forDecision-making Financial Engineering Global Marketing Information SecurityManagement Investment Analysis andPortfolio Management Law in BusinessEnvironment Lean Six Sigma Management ofFinancial Institutions MarketingCommunication Personal Finance Project Management Supply Chain Management

4. Integrating Course

Strategic Management

Business communication

Business communication is the sharing of information between people within and outside theorganization that is performed for the commercial benefit of the organization. It can also bedefined as relaying of information within a business by its people.

Overview

Business communication (or simply "communication", in a business context) encompassestopics such as marketing, brand management, customer relations, consumer behavior,advertising, public relations, corporate communication, community engagement, reputationmanagement, interpersonal communication, employee engagement, and event management. Itis closely related to the fields of professional communication and technical communication.

Media channels for business communication include the Internet, print media, radio, television,ambient media, and word of mouth.

Business communication can also refer to internal communication that takes place within anorganization.

Business communication is a common topic included in the curricula of Undergraduate andMaster programs of many colleges and universities.

There are several methods of business communication, including:

Web-based communication - for better and improved communication, anytime anywhere ...

video conferencing which allow people in different locations to hold interactive meetings;

Reports - important in documenting the activities of any department;

Presentations - very popular method of communication in all types of organizations, usuallyinvolving audiovisual material, like copies of reports, or material prepared in MicrosoftPowerPoint or Adobe Flash;

telephone meetings, which allow for long distance speech;

forum boards, which allow people to instantly post information at a centralized location; and

face-to-face meetings, which are personal and should be succeeded by a written followup.

suggestion box: It is primarily used for upward communication, because some people mayhesitate to communicate with management directly, so they opt to give suggestions by draftingone and putting it in the suggestion box.

Effective business communication

A two way information sharing process which involves one party sending a message that iseasily understood by the receiving party. Effective communication by business managersfacilitates information sharing between company employees and can substantially contribute toits commercial success.

For business communication to be effective these qualities are essential :

Establish clear hierarchy

Use visual communication

Conflict Management

Consider Cultural Issues

Good Written communication

Face-to-face

Face-to-face communication helps to establish a personal connection and will help sell theproduct or service to the customer. These interactions can portray a whole different messagethan written communication as tone, pitch, and body language is observed. Information iseasier to access and delivered immediately with interactions rather than waiting for an email orphone call. Conflicts are also easily resolved this way, as verbal and non-verbal cues areobserved and acted upon. Communicating professionally is very important as one isrepresenting the company. Speak clearly and ask questions to understand the needs and wants,let the recipient respond as one resolves the issue. Decisions are made more confidently duringa face-to-face interaction as the recipient asks questions to understand and move forward withtheir decision.

Email

When using email to communicate in the business world, it is important to be careful with thechoice of words. Miscommunication is very frequent as the reader doesn’t know whatnon-verbal cues one is giving off, such as the pitch, tone, or expressions. Before beginning anemail, make sure the email address one is using is appropriate and professional as well as themessage one is going to send. Again, make sure the information is clear and to the point so therecipient isn’t confused. Make sure one includes their signature, title, and other contactinformation at the end

Telephone

When making a business call, make it clear who is on the line and where one is from as well asone's message when on the phone. Smile and have a positive attitude as the recipient will beable to read the caller and that will affect how they react. When leaving a message, make sureone is clear and brief. One should state their name and who they are and the purpose forcontacting them. If replying to a voicemail, try to respond as soon as possible and take intoconsideration the time of day. Don't call too early or too late, as it is important to respectother's time. Also be mindful of where one is and the noise level as well as the people one isaround when trying to reach someone by phone.[4]

When making a sales call, hope for the person one are trying to connect to does not answer thephone. Leave up to five enticing messages and one's target audience will be ready to speakwhen one either gets a call back or one calls and reaches the person. The enticing messageprepares the person to speak to the representative. It may be that the person is not interestedbased on what one had said in each voice message. Always be polite and accept that one mayhave many more to call. If the individual is reached, one might ask if there might be someonebetter suited for the advertised program.

If one is calling and leaving voice messages, include time of availability for callbacks. There isnothing worse than a callback coming to one when one is not available. Use the telephone as agreat communication tool. Be polite and always put oneself in the other person's position.

Listening

When listening to another employee or customer speak it is very important to be an avidlistener. Here are some obstacles that you might have to overcome:

Filters and Assumptions

Biases and Prejudices

Inattention and Impatience

Surrounding Environment

A good way to overcome these factors is by using LOTS Better Communication method. Thismethod includes four steps in order to produce good listening skills and the ability to respondwith an educated statement. The four steps to this method are:

Listen

Observe

Think

Speak

Doing all of these things while showing good eye contact and body posture will assure thespeaker that he/she is getting full attention from the listeners.

Choice of Means and Mode of Communication - Choosing the right means and mode ofcommunication plays a vital role in the effectiveness of the message being communicated andsuch choice depends on various factors such as:

Organization Size and Policy - If the organisation is small, probably more communication will beoral, than in larger organizations where it may organizations where it may be in writing. Thepolicy for communication also would play a major role in influencing one's choice of mode ofcommunication.

Cost Factor - The main point to be considered here would be to evaluate wheather the costinvolved in sending the message would be commensurate with the results expected.

Nature of Message - Whether the message is confidential in nature, urgent or important etc.and whether a matter would require hand delivery or be set by registered post etc. also

influences the choice of mode and means of communication.

Distance Involved - Whether the message to be sent is also another vital factor which couldinfluence the choice of means and modes of communication. For example, if a letter is to besent to a partner in a joint venture in Japan and it is urgent, you would not think of sendingsomeone to personally deliver it.

Resources - The resources available to both the sender and receiver would also influence yourchoice. You can only send a fax if the other person/organization has a fax machine. Thereforewe can see that the choice of a particular mode and means of communication will depend on acase to case basis and is influenced by various factors.

Choosing Communication Media

When choosing a media of communication, it is important to consider who are the respectiveaudience and the objective of the message itself. Rich media are more interactive than leanmedia and provide the opportunity for two-way communication: the receiver can ask questionsand express opinions easily in person.[5] To help such decision, one may roughly refer to thecontinuum shown below.

From Richer to Leaner

1.Face-to-Face Meeting

2.In-Person Oral Presentation

3.Online Meeting

4.Videoconferencing

5.Teleconferencing

6.Phone Call

7.Voice Message

8.Video

9.Blog

10.Report

11.Brochure

12.Newsletter

13.Flier

14.Email

15. Memo

Subliminal method of communication

Subliminal perception refers to the individual ability to perceive and respond to stimuli that arebelow the threshold or level of consciousness, which proved to influence thoughts, feelings oractions altogether or separately. There are four distinct methods of communicatingsubliminally. These are visual stimuli in movies, accelerated speech, embedded images in a printadvertisement, and suggestiveness which is not normally seen at first glance.Focussing onSubliminal Communication through visual stimuli, Marketing people have adopted this methodeven incorporating it films and television shows.Subliminal method of communication firstmade its debut in a 1957 advertisement, during which a brief message flashed, telling viewersto eat popcorn and drink Coca-Cola. Since that time, subliminal communication has occupied acontroversial role in the advertising landscape, with some people claiming it's omnipresent,while others emphasize it's not real. As of publication, there is still an ongoing scientific debateabout whether subliminal advertising works. Subliminal messaging is a form of advertising inwhich a subtle message is inserted into a standard ad. This subtle message affects theconsumer's behavior, but the consumer does not know she's seen the message. For example, amarketer might incorporate a single frame telling consumers to drink tea in a movie. In printmedia, advertisers might put hidden images or coded messages into ad text.

Business Writing Process

The challenge of the communication process is for the sender and receiver to gain a mutualunderstanding about the meaning of the message. A writer can put his or her words on paper,but the reader may not react to the words as the writer intended. Most writers are much moreeffective, successful, and productive if they spend time thinking about the communicationsituation before beginning to write. Successful writers approach writing as a three- step processthat involves planning before starting to write, drafting with the audience (the reader) in mind,and revising the document to determine if it meets the audience’s needs and if it represents theorganization well.

STEP1: Planning

You should spend more time planning and revising your document than you spend writing.

STEP2: Drafting

Once you have planned the purpose of your message, considered how your audience mightreact to the message, gathered your information, decided on an order for your information, andselected your medium for delivery, you are ready to compose your document. About 20percent of your writing time should be spent drafting the document.

Do not be concerned with perfection as you draft your message. Write in a conversational tone,without using slang; write as you would speak in a workplace environment. One guideline thathelps in the drafting stage is to write as though you are presenting the information to a friend.Rather than thinking of the audience as just “someone out there,” think of the audience as aspecific person with whom you are building or maintaining a relationship. Thinking of a friendhelps you choose effective words and tone, helps you be clear, and helps you includeinformation helpful to the reader.

STEP3: Revising

Revising is more than checking your spelling and punctuation. Revising requires you to checkevery part of your message to see if it is clear, concise, and correct and will take approximately40 percent of your writing time. You want to look at every word to see if you selected the mostappropriate one, at every sentence to see whether the structure is the best it can be, and atevery paragraph to see whether it includes a well-developed argument. Finally review thedocument design to look for an attractive, professional appearance that meets your employer’sand your reader’s expectations.[1]

Corporate communication

Corporate communication is a set of activities involved in managing and orchestrating allinternal and external communications aimed at creating favourable point of view amongstakeholders on which the company depends. It is the messages issued by a corporateorganization, body, or institute to its audiences, such as employees, media, channel partnersand the general public. Organizations aim to communicate the same message to all itsstakeholders, to transmit coherence, credibility and ethic. Corporate Communications help

organizations explain their mission, combine its many visions and values into a cohesivemessage to stakeholders. The concept of corporate communication could be seen as anintegrative communication structure linking stakeholders to the organization.

Methods and tactics

Three principal clusters of task-planning and communication form the backbone of business andthe activity of business organizations. These include management communication, marketingcommunication, and organizational communication.

Management communication takes place between management and its internal and externalaudiences. To support management communication, organizations rely heavily on specialists inmarketing communication and organizational communication.[citation needed]

Marketing communication gets the bulk of the budgets in most organizations, and consists ofproduct advertising, direct mail, personal selling, and sponsorship activities.

Organizational communication consist of specialists in public relations, public affairs, investorrelations, environmental communications, corporate advertising, and employeecommunication.

The responsibilities of corporate communication are:

to promote the profile of the "company behind the brand" (corporate branding)

to minimize discrepancies between the company's desired identity and brand features

to delegate tasks in communication

to formulate and execute effective procedures to make decisions on communication matters

to mobilize internal and external support for corporate objectives

to coordinate with international business firms

Corporate branding

A corporate brand is the perception of a company that unites a group of products or services for

the public under a single name, a shared visual identity, and a common set of symbols. Theprocess of corporate branding consists creating favourable associations and positive reputationwith both internal and external stakeholders. The purpose of a corporate branding initiative isto generate a positive halo over the products and businesses of the company, imparting morefavourable impressions of those products and businesses.

In more general terms, research suggests that corporate branding is an appropriate strategy forcompanies to implement when:

there is significant "information asymmetry" between a company and its clients; That is to saycustomers are much less informed about a company's products than the company itself is;

customers perceive a high degree of risk in purchasing the products or services of thecompany;

features of the company behind the brand would be relevant to the product or service acustomer is considering purchasing.

Corporate and organizational identity

There are two approaches for identity:

Corporate identity is the reality and uniqueness of an organization, which is integrally relatedto its external and internal image and reputation through corporate communication[6]

Organizational identity comprises those characteristics of an organization that its membersbelieve are central, distinctive and enduring. That is, organizational identity consists of thoseattributes that members feel are fundamental to (central) and uniquely descriptive of(distinctive) the organization and that persist within the organization over time (enduring)".

Corporate responsibility

Corporate responsibility (often referred to as corporate social responsibility), corporatecitizenship, sustainability, and even conscious capitalism are some of the terms bandied aboutthe news media and corporate marketing efforts as companies jockey to win the trust and

loyalty of constituents. Corporate responsibility (CR) constitutes an organization’s respect forsociety’s interests, demonstrated by taking ownership of the effects its activities have on keyconstituencies including customers, employees, shareholders, communities, and theenvironment, in all parts of their operations. In short, CR prompts a corporation to look beyondits traditional bottom line, to the social implications of its business.

Corporate reputation

Reputations are overall assessments of organizations by their stakeholders. They are aggregateperceptions by stakeholders of an organization's ability to fulfill their expectations, whetherthese stakeholders are interested in buying the company's products, working for the company,or investing in the company's shares.

Crisis communications

Crisis communication is sometimes considered a sub-specialty of the public relations professionthat is designed to protect and defend an individual, company, or organization facing a publicchallenge to its reputation. These challenges may come in the form of an investigation from agovernment agency, a criminal allegation, a media inquiry, a shareholders lawsuit, a violation ofenvironmental regulations, or any of a number of other scenarios involving the legal, ethical, orfinancial standing of the entity. The crisis for organizations can be defined as follows:

A crisis is a major catastrophe that may occur either naturally or as a result of human error,intervention, or even malicious intent. It can include tangible devastation, such as thedestruction of lives or assets, or intangible devastation, such as the loss of an organization'scredibility or other reputational damage. The latter outcomes may be the result ofmanagement's response to tangible devastation or the result of human error. A crisis usuallyhas significant actual or potential financial impact on a company, and it usually affects multipleconstituencies in more than one market.

Internal/employee communications

As the extent of communication grows, many companies create an employee relations (ER)function with dedicated staff to manage the numerous media through which senior managerscan communicate among themselves and with the rest of the organization. Internalcommunication in the 21st century is more than the memos, publications, and broadcasts thatcomprise it; it’s about building a corporate culture on values that drive organizationalexcellence. ER specialists are generally expected to fulfill one or more of the following four

roles:

Efficiency: Internal communication is used primarily to disseminate information aboutcorporate activities.

Shared meaning: Internal communication is used to build a shared understanding amongemployees about corporate goals.

Connectivity: Internal communication is used mainly to clarify the connectedness of thecompany's people and activities.

Satisfaction: Internal communication is used to improve job satisfaction throughout thecompany.

Investor relations

The investor relations (IR) function is used by companies which publicly trade shares on a stockexchange. In such companies, the purpose of the IR specialist is to interface with current andpotential financial stakeholders-namely retail investors, institutional investors, and financialanalysts.

The role of investor relations is to fulfill three principal functions:

comply with regulations;

Create a favorable relationship with key financial audiences;

contribute to building and maintaining the company's image and reputation.

Public relations: issues management and media relations

The role of the public relations specialist, in many ways, is to communicate with the generalpublic in ways that serve the interests of the company. PR therefore consists of numerousspecialty areas that convey information about the company to the public, includingsponsorships, events, issues management and media relations. When executing these types ofactivities, the PR Specialist must incorporate broader corporate messages to convey thecompany’s strategic positioning. This ensures the PR activities ultimately convey messages thatdistinguish the company vis-à-vis its competitors and the overall marketplace, while also

communicating the company’s value to target audiences.

Issues management

A key role of the PR specialist is to make the company better known for traits and attributesthat build the company’s perceived distinctiveness and competitiveness with the public. Inrecent years, PR specialists have become increasingly involved in helping companies managestrategic issues – public concerns about their activities that are frequently magnified by specialinterest groups and NGOs. The role of the PR specialist therefore also consists of issuesmanagement, namely the “set of organizational procedures, routines, personnel, and issues”. Astrategic issue is one that compels a company to deal with it because there is “ a conflictbetween two or more identifiable groups over procedural or substantive matters relating to thedistribution of positions or resources”.

Media relations

To build better relationships with the media, organizations must cultivate positive relations withinfluential members of the media. This task might be handled by employees within thecompany’s media relations department or handled by a public relations firm.

Company/spokesperson profiling

These "public faces" are considered authorities in their respective sector/field and ensure thecompany/organization is in the limelight.

- Managing content of corporate websites and/or other external touch points

-Managing corporate publications - for the external world

Managing print media. [2]

Ethics in business communication

Communication is the process by which individuals exchange information between otherindividuals or groups of people. Throughout the process, effective communicators try as clearly

and accurately to convey their thoughts, intentions and objectives to theirreceiver.Communication is successful only when both the sender and the receiver understandthe same information.In today's business environments, effective communication skills arenecessary due to the highly informational and technological era.

Regardless of context, communication involves choice, reflects values, and has consequences.For better communication, understanding the obvious and the subtle issues relating tocommunication is necessary. Any company that aims to be socially and ethically responsiblemust make a priority of ethical communication both inside the company and in its interactionswith the public. In theory, many consumers prefer to do business with companies they believeare ethical which gives those ethical businesses an advantage in the market. Ethical issues ofbusiness communication are one such issue. Some of the vital characteristics of ethicalcommunication are discussed below.

Conveying the point without offending the audience:

While communicating to the audience, conveying the desired message to them in a significantmanner is of primary importance. For instance, the employees in a company can be asked toincrease their efficiency in a demanding manner whereas managers and executives will feeloffended if the same tone is used on them. There are different ways to explain the exact thingsto them in a much smoother manner.

Maintain a relationship with the audience:

Maintaining the same wavelength with the audience is very important for a communicator toensure the audiences feel at home. Experienced communicators immediately build arelationship based on trust with the audience as soon as they start speaking. Great orators suchas Winston Churchill and Mahatma Gandhi always were able to maintain a relationship withtheir audience because they were masters at striking the same wavelength of the audience.

Avoid withholding crucial information:

In the modern era, information is vital for all decisions. Hence, it is vital for any organization tobe cautious when communicating with the public. The communicated information should be

absolute and all vital information must be conveyed appropriately. Purposely withholdingcrucial information might result in the public conceiving a bad image.

Well organized value system:

In order to ensure that this concept is successfully practiced and understood in an organization,a well-organized value system must be established throughout the organization by the topmanagement. If an organization functions on the base of value systems common to both thetop management and the employees, mutual respect between them will be present. A soundand healthy value system can make way for ethical communication.

Accuracy of information is necessary:

Any information that is to be passed on must be true and accurate. Communicating withoutchecking the truth of the information can be highly dangerous for the organization.Identification of the source and testing the information is necessary before communicating it.

Ways to overcome ethical dilemma

Message ahead of the person - Common good approach:

Most people in organization face ethical dilemma when they want to withhold crucialinformation because of conflict with an individual or a group. In such situations, importanceshould be given to the message to be communicated and not on the person or the group towhich the message is to be communicated. Hence people should give priority to the commongood of the organization rather than interpersonal or inter-group conflicts.

Decisions that produce more good and less harm – Utilitarian approach:

When in ethical dilemma consider the effects of various alternatives after a certain period oftime. Ethical decision is to choose the alternative which provides more good and less harm tothe organization.

Code of Ethics: The International Association of Business Communicators has developed a codeof ethics for business communication.The IABC code of ethics requires business communicatorsto be truthful and accurate and to personally correct any inaccuracies they have theopportunity to correct. They are also expected to support human rights, such as freedom ofspeech and to respect and understand the values of different cultures and belief systems. Theymust refuse to participate in any unethical business communication practices, follow all lawsand regulations affecting their industry, avoid plagiarism in communication, maintainconfidentiality except when it would be legally or ethically inappropriate to do so, avoid theappearance of bribery or conflict of interest, avoid promising unrealistic results or benefits toclients or customers and practice honesty with both self and others.[3]

Organizational communication

Organizational communication is a subfield of the larger discipline of communication studies.Organizational communication, as a field, is the consideration, analysis, and criticism of the roleof communication in organizational contexts. Its main function is to inform, persuade andpromote goodwill. The flow of communication could be either formal or informal.Communication flowing through formal channels are downward, horizontal and upwardwhereas communication through informal channels are generally termed as grapevine.

Early underlying assumptions

Some of the main assumptions underlying much of the early organizational communicationresearch were:

Humans act rationally. Some people do not behave in rational ways, they generally have noaccess to all of the information needed to make rational decisions they could articulate, andtherefore will make unrational decisions, unless there is some breakdown in the communicationprocess—which is common. Irrational people rationalize how they will rationalize theircommunication measures whether or not it is rational.

Formal logic and empirically verifiable data ought to be the foundation upon which any theoryshould rest. All we really need to understand communication in organizations is (a) observableand replicable behaviors that can be transformed into variables by some form of measurement,and (b) formally replicable syllogisms that can extend theory from observed data to othergroups and settings.

Communication is primarily a mechanical process, in which a message is constructed andencoded by a sender, transmitted through some channel, then received and decoded by areceiver. Distortion, represented as any differences between the original and the receivedmessages, can and ought to be identified and reduced or eliminated.

Organizations are mechanical things, in which the parts (including employees functioning indefined roles) are interchangeable. What works in one organization will work in another similarorganization. Individual differences can be minimized or even eliminated with carefulmanagement techniques.

Organizations function as a container within which communication takes place. Any differencesin form or function of communication between that occurring in an organization and in anothersetting can be identified and studied as factors affecting the communicative activity.

Interorganization communication

Flow nomenclature

There is an emerging informal use of abbreviations to indicate the flow of information inaddition to other transactions. These share a common pattern of source and destinationseparated by the numeral "2" in place of the word "to." This doesn't assume that thecommunication only flows in one direction with these terms. duplex point-to-pointcommunication systems, computer networks, non-electronic telecommunications, and

meetings in person are all possible with the use of these terms. Example of terms:

In Business

B2B (business-to-business)

B2C (business-to-consumers)

B2E (business-to-employees)

B2G (business-to-government)

In Governance

G2G (government-to-government)

G2C (government-to-citizens)

G2E (government-to-employees)

G2B (government-to-business)

In Society

C2B (consumer-to-business)

C2C (consumer-to-consumer)

or (customer-to-customer)

or (citizen-to-citizen)

Interpersonal communication

Another fact of communication in the organization is the process of one-to-one or interpersonalcommunication, between individuals. Such communication may take several forms. Messagesmay be verbal (that is, expressed in words), or they may not involve words at all but consist ofgestures, facial expressions, and certain postures ("body language"). Nonverbal messages mayeven stem from silence.

Managers do not need answers to operate a successful business; they need questions. Answerscan come from anyone, anytime, anywhere in the world thanks to the benefits of all theelectronic communication tools at our disposal. This has turned the real job of managementinto determining what it is the business needs to know, along with the who/what/where/whenand how of learning it. To effectively solve problems, seize opportunities, and achieveobjectives, questions need to be asked by managers—these are the people responsible for theoperation of the enterprise as a whole.

Ideally, the meanings sent are the meanings received. This is most often the case when themessages concern something that can be verified objectively. For example, "This piece of pipefits the threads on the coupling." In this case, the receiver of the message can check thesender's words by actual trial, if necessary. However, when the sender's words describe afeeling or an opinion about something that cannot be checked objectively, meanings can bevery unclear. "This work is too hard" or "Watergate was politically justified" are examples of

opinions or feelings that cannot be verified. Thus they are subject to interpretation and henceto distorted meanings. The receiver's background of experience and learning may differ enoughfrom that of the sender to cause significantly different perceptions and evaluations of the topicunder discussion. As we shall see later, such differences form a basic barrier to communication.

A number of variables influence the effectiveness of communication. Some are found in theenvironment in which communication takes place, some in the personalities of the sender andthe receiver, and some in the relationship that exists between sender and receiver. Thesedifferent variables suggest some of the difficulties of communicating with understandingbetween two people. The sender wants to formulate an idea and communicate it to thereceiver. This desire to communicate may arise from his thoughts or feelings or it may havebeen triggered by something in the environment. The communication may also be influencedby the relationship between the sender and the receiver, such as status differences, a staff-linerelationship, or a learner-teacher relationship.

Physical and cognitive, including semantic filters (which decide the meaning of words) combineto form a part of our memory system that helps us respond to reality. In this sense, March andSimon compare a person to a data processing system. Behavior results from an interactionbetween a person's internal state and environmental stimuli. What we have learned throughpast experience becomes an inventory, or data bank, consisting of values or goals, sets ofexpectations and preconceptions about the consequences of acting one way or another, and avariety of possible ways of responding to the situation. This memory system determines whatthings we will notice and respond to in the environment. At the same time, stimuli in theenvironment help to determine what parts of the memory system will be activated. Hence, thememory and the environment form an interactive system that causes our behavior. As thisinteractive system responds to new experiences, new learnings occur which feed back intomemory and gradually change its content. This process is how people adapt to a changingworld.

Approaches

Informal and formal communication are used in an organization.

Informal communication, generally associated with interpersonal, horizontal communication,was primarily seen as a potential hindrance to effective organizational performance. This is nolonger the case. Informal communication has become more important to ensuring the effectiveconduct of work in modern organizations.

Top-down approach: This is also known as downward communication. This approach is used bythe Top Level Management to communicate to the lower levels. This is used to implement

policies, guidelines, etc. In this type of organizational communication, distortion of the actualinformation occurs. This could be made effective by feedbacks.

Currently, some topics of research and theory in the field are:

Constitution, e.g.,

how communicative behaviors construct or modify organizing processes or products

how communication itself plays a constitutive role in organizations

how the organizations within which we interact affect our communicative behaviors, andthrough these, our own identities

structures other than organizations which might be constituted through our communicativeactivity (e.g., markets, cooperatives, tribes, political parties, social movements)

when does something "become" an organization? When does an organization become(an)other thing(s)? Can one organization "house" another? Is the organization still a usefulentity/thing/concept, or has the social/political environment changed so much that what wenow call "organization" is so different from the organization of even a few decades ago that itcannot be usefully tagged with the same word – "organization"?

Narrative, e.g.,

how do group members employ narrative to acculturate/initiate/indoctrinate new members?

do organizational stories act on different levels? Are different narratives purposively invokedto achieve specific outcomes, or are there specific roles of "organizational storyteller"? If so, arestories told by the storyteller received differently from those told by others in the organization?

in what ways does the organization attempt to influence storytelling about the organization?under what conditions does the organization appear to be more or less effective in obtaining adesired outcome?

when these stories conflict with one another or with official rules/policies, how are theconflicts worked out? in situations in which alternative accounts are available, who or how orwhy are some accepted and others rejected?

Identity, e.g.,

who do we see ourselves to be, in terms of our organizational affiliations?

do communicative behaviors or occurrences in one or more of the organizations in which weparticipate effect changes in us? To what extent do we consist of the organizations to which webelong?

is it possible for individuals to successfully resist organizational identity? what would that looklike?

do people who define themselves by their work-organizational membership communicatedifferently within the organizational setting than people who define themselves more by anavocational (non-vocational) set of relationships?

Interrelatedness of organizational experiences, e.g.,

how do our communicative interactions in one organizational setting affect ourcommunicative actions in other organizational settings?

how do the phenomenological experiences of participants in a particular organizationalsetting effect changes in other areas of their lives?

when the organizational status of a member is significantly changed (e.g., by promotion orexpulsion) how are their other organizational memberships affected?

what kind of future relationship between business and society does organizationalcommunication seem to predict?

Power e.g.,

How does the use of particular communicative practices within an organizational settingreinforce or alter the various interrelated power relationships within the setting? Are thepotential responses of those within or around these organizational settings constrained byfactors or processes either within or outside of the organization – (assuming there is an"outside")?

Do taken-for-granted organizational practices work to fortify the dominant hegemonicnarrative? Do individuals resist/confront these practices, through what actions/agencies, and towhat effects?

Do status changes in an organization (e.g., promotions, demotions, restructuring,financial/social strata changes) change communicative behavior? Are there criteria employedby organizational members to differentiate between "legitimate" (i.e., endorsed by the formalorganizational structure) and "illegitimate" (i.e., opposed by or unknown to the formal powerstructure) behaviors? When are they successful, and what do we mean by "successful" whenthere are "pretenders" or "usurpers" who employ these communicative means? [4]

Professional communication

Professional communication encompasses written, oral, visual and digital communicationwithin a workplace context. This discipline blends together pedagogical principles of rhetoric,technology, software, and learning theory to improve and deliver communication in a variety ofsettings ranging from technical writing to usability and digital media design. It is a new disciplinethat focuses on the study of information and the ways it is created, managed, distributed, andconsumed. Since communication in modern society is a rapidly changing area, the progress oftechnologies seems to often outpace the number of available expert practitioners. This createsa demand for skilled communicators which continues to exceed the supply of trainedprofessionals.

The field of professional communication is closely related to that of technical communication,though professional communication encompasses a wider variety of skills. Professionalcommunicators use strategies, learning theory, and technologies to more effectivelycommunicate in the business world.

Successful communication skills are critical to a business because all businesses, though tovarying degrees, involve the following: writing, reading, editing, speaking, listening, softwareapplications, computer graphics, and Internet research. Job candidates with professionalcommunication backgrounds are more likely to bring to the organization sophisticatedperspectives on society, culture, science, and technology.[5]

Applied mathematics

Applied mathematics is a branch of mathematics that deals with mathematical methods thatfind use in science, engineering, business, computer science, and industry. Thus, applied

mathematics is a combination of mathematical science and specialized knowledge. The term"applied mathematics" also describes the professional specialty in which mathematicians workon practical problems by formulating and studying mathematical models. In the past, practicalapplications have motivated the development of mathematical theories, which then becamethe subject of study in pure mathematics where abstract concepts are studied for their ownsake. The activity of applied mathematics is thus intimately connected with research in puremathematics.

Historically, applied mathematics consisted principally of applied analysis, most notablydifferential equations; approximation theory (broadly construed, to include representations,asymptotic methods, variational methods, and numerical analysis); and applied probability.These areas of mathematics related directly to the development of Newtonian physics, and infact, the distinction between mathematicians and physicists was not sharply drawn before themid-19th century. This history left a pedagogical legacy in the United States: until the early 20thcentury, subjects such as classical mechanics were often taught in applied mathematicsdepartments at American universities rather than in physics departments, and fluid mechanicsmay still be taught in applied mathematics departments. Quantitative finance is now taught inmathematics departments across universities and mathematical finance is considered a fullbranch of applied mathematics. Engineering and computer science departments havetraditionally made use of applied mathematics.

Today, the term "applied mathematics" is used in a broader sense. It includes the classical areasnoted above as well as other areas that have become increasingly important in applications.Even fields such as number theory that are part of pure mathematics are now important inapplications (such as cryptography), though they are not generally considered to be part of thefield of applied mathematics per se. Sometimes, the term "applicable mathematics" is used todistinguish between the traditional applied mathematics that developed alongside physics andthe many areas of mathematics that are applicable to real-world problems today.

There is no consensus as to what the various branches of applied mathematics are. Suchcategorizations are made difficult by the way mathematics and science change over time, andalso by the way universities organize departments, courses, and degrees.

The success of modern numerical mathematical methods and software has led to theemergence of computational mathematics, computational science, and computationalengineering, which use high-performance computing for the simulation of phenomena and thesolution of problems in the sciences and engineering. These are often consideredinterdisciplinary.

Historically, mathematics was most important in the natural sciences and engineering.However, since World War II, fields outside of the physical sciences have spawned the creationof new areas of mathematics, such as game theory and social choice theory, which grew out ofeconomic considerations.

The advent of the computer has enabled new applications: studying and using the newcomputer technology itself (computer science) to study problems arising in other areas ofscience (computational science) as well as the mathematics of computation (for example,theoretical computer science, computer algebra, numerical analysis). Statistics is probably themost widespread mathematical science used in the social sciences, but other areas ofmathematics, most notably economics, are proving increasingly useful in these disciplines.

Academic institutions are not consistent in the way they group and label courses, programs, anddegrees in applied mathematics. At some schools, there is a single mathematics department,whereas others have separate departments for Applied Mathematics and (Pure) Mathematics.It is very common for Statistics departments to be separated at schools with graduateprograms, but many undergraduate-only institutions include statistics under the mathematicsdepartment.

Many applied mathematics programs (as opposed to departments) consist of primarilycross-listed courses and jointly appointed faculty in departments representing applications.Some Ph.D. programs in applied mathematics require little or no coursework outside ofmathematics, while others require substantial coursework in a specific area of application. Insome respects this difference reflects the distinction between "application of mathematics" and"applied mathematics".

Applied mathematics has substantial overlap with the discipline of statistics. Statistical theoristsstudy and improve statistical procedures with mathematics, and statistical research often raisesmathematical questions. Statistical theory relies on probability and decision theory, and makesextensive use of scientific computing, analysis, and optimization; for the design of experiments,statisticians use algebra and combinatorial design. Applied mathematicians and statisticiansoften work in a department of mathematical sciences (particularly at colleges and smalluniversities).

Mathematical economics is the application mathematical methods to represent theories andanalyze problems in economics. The applied methods usually refer to nontrivial mathematicaltechniques or approaches. Mathematical economics is based on statistics, probability,mathematical programming (as well as other computational methods), operations research,game theory, and some methods from mathematical analysis. In this regard, it resembles (but isdistinct from) financial mathematics, another part of applied mathematics.[6]

Applied mathematics is a branch of mathematics that concerns itself with the application ofmathematical knowledge to other domains. Such applications include numerical analysis,mathematics of engineering, linear programming, optimization and operations research,continuous modelling, mathematical biology and bioinformatics, information theory, gametheory, probability and statistics, financial mathematics, actuarial science, cryptography andhence combinatorics and even finite geometry to some extent, graph theory as applied tonetwork analysis, and a great deal of what is called computer science.[7]

Business statistics

Business statistics is the science of good decision making in the face of uncertainty and is usedin many disciplines such as financial analysis, econometrics, auditing, production and operationsincluding services improvement, and marketing research.

These sources feature regular repetitive publication of series of data. This makes the topic oftime series especially important for business statistics. It is also a branch of applied statisticsworking mostly on data collected as a by-product of doing business or by government agencies.It provides knowledge and skills to interpret and use statistical techniques in a variety ofbusiness applications.

A typical business statistics course is intended for business majors, and covers statistical study,descriptive statistics (collection, description, analysis, and summary of data), probability, andthe binomial and normal distributions, test of hypotheses and confidence intervals, linearregression, and correlation. [8]

Statistics

Statistics is the study of the collection, analysis, interpretation, presentation, and organizationof data.[1] In applying statistics to, e.g., a scientific, industrial, or social problem, it isconventional to begin with a statistical population or a statistical model process to be studied.Populations can be diverse topics such as "all people living in a country" or "every atomcomposing a crystal". Statistics deals with all aspects of data including the planning of datacollection in terms of the design of surveys and experiments.

Some popular definitions are:

Merriam-Webster dictionary defines statistics as "classified facts representing the conditionsof a people in a state – especially the facts that can be stated in numbers or any other tabular orclassified arrangement

When census data cannot be collected, statisticians collect data by developing specificexperiment designs and survey samples. Representative sampling assures that inferences andconclusions can safely extend from the sample to the population as a whole. An experimentalstudy involves taking measurements of the system under study, manipulating the system, andthen taking additional measurements using the same procedure to determine if themanipulation has modified the values of the measurements. In contrast, an observational studydoes not involve experimental manipulation.

Two main statistical methodologies are used in data analysis: descriptive statistics, whichsummarizes data from a sample using indexes such as the mean or standard deviation, andinferential statistics, which draws conclusions from data that are subject to random variation(e.g., observational errors, sampling variation). Descriptive statistics are most often concernedwith two sets of properties of a distribution (sample or population): central tendency (orlocation) seeks to characterize the distribution's central or typical value, while dispersion (orvariability) characterizes the extent to which members of the distribution depart from its centerand each other. Inferences on mathematical statistics are made under the framework ofprobability theory, which deals with the analysis of random phenomena.

A standard statistical procedure involves the test of the relationship between two statisticaldata sets, or a data set and a synthetic data drawn from idealized model. An hypothesis isproposed for the statistical relationship between the two data sets, and this is compared as analternative to an idealized null hypothesis of no relationship between two data sets. Rejectingor disproving the null hypothesis is done using statistical tests that quantify the sense in whichthe null can be proven false, given the data that are used in the test. Working from a nullhypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falselyrejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and anactual difference between populations is missed giving a "false negative"). Multiple problemshave come to be associated with this framework: ranging from obtaining a sufficient samplesize to specifying an adequate null hypothesis.

Measurement processes that generate statistical data are also subject to error. Many of theseerrors are classified as random (noise) or systematic (bias), but other types of errors (e.g.,blunder, such as when an analyst reports incorrect units) can also be important. The presenceof missing data and/or censoring may result in biased estimates and specific techniques havebeen developed to address these problems.

Statistics can be said to have begun in ancient civilization, going back at least to the 5th centuryBC, but it was not until the 18th century that it started to draw more heavily from calculus andprobability theory. Statistics continues to be an area of active research, for example on theproblem of how to analyze Big data.

Statistics is a mathematical body of science that pertains to the collection, analysis,interpretation or explanation, and presentation of data, or as a branch of mathematics. Someconsider statistics to be a distinct mathematical science rather than a branch of mathematics.While many scientific investigations make use of data, statistics is concerned with the use ofdata in the context of uncertainty and decision making in the face of uncertainty.

Mathematical statistics is the application of mathematics to statistics, which was originallyconceived as the science of the state — the collection and analysis of facts about a country: itseconomy, land, military, population, and so forth. Mathematical techniques used for thisinclude mathematical analysis, linear algebra, stochastic analysis, differential equations, and

measure-theoretic probability theory.

In applying statistics to a problem, it is common practice to start with a population or process tobe studied. Populations can be diverse topics such as "all persons living in a country" or "everyatom composing a crystal".

Ideally, statisticians compile data about the entire population (an operation called census). Thismay be organized by governmental statistical institutes. Descriptive statistics can be used tosummarize the population data. Numerical descriptors include mean and standard deviation forcontinuous data types (like income), while frequency and percentage are more useful in termsof describing categorical data (like race).

When a census is not feasible, a chosen subset of the population called a sample is studied.Once a sample that is representative of the population is determined, data is collected for thesample members in an observational or experimental setting. Again, descriptive statistics can beused to summarize the sample data. However, the drawing of the sample has been subject toan element of randomness, hence the established numerical descriptors from the sample arealso due to uncertainty. To still draw meaningful conclusions about the entire population,inferential statistics is needed. It uses patterns in the sample data to draw inferences about thepopulation represented, accounting for randomness. These inferences may take the form of:answering yes/no questions about the data (hypothesis testing), estimating numericalcharacteristics of the data (estimation), describing associations within the data (correlation) andmodeling relationships within the data (for example, using regression analysis). Inference canextend to forecasting, prediction and estimation of unobserved values either in or associatedwith the population being studied; it can include extrapolation and interpolation of time seriesor spatial data, and can also include data mining.

Sampling

When full census data cannot be collected, statisticians collect sample data by developingspecific experiment designs and survey samples. Statistics itself also provides tools forprediction and forecasting the use of data through statistical models. To use a sample as a guideto an entire population, it is important that it truly represents the overall population.Representative sampling assures that inferences and conclusions can safely extend from thesample to the population as a whole. A major problem lies in determining the extent that thesample chosen is actually representative. Statistics offers methods to estimate and correct forany bias within the sample and data collection procedures. There are also methods ofexperimental design for experiments that can lessen these issues at the outset of a study,strengthening its capability to discern truths about the population.

Sampling theory is part of the mathematical discipline of probability theory. Probability is usedin mathematical statistics to study the sampling distributions of sample statistics and, moregenerally, the properties of statistical procedures. The use of any statistical method is valid

when the system or population under consideration satisfies the assumptions of the method.The difference in point of view between classic probability theory and sampling theory is,roughly, that probability theory starts from the given parameters of a total population todeduce probabilities that pertain to samples. Statistical inference, however, moves in theopposite direction—inductively inferring from samples to the parameters of a larger or totalpopulation.

A common goal for a statistical research project is to investigate causality, and in particular todraw a conclusion on the effect of changes in the values of predictors or independent variableson dependent variables. There are two major types of causal statistical studies: experimentalstudies and observational studies. In both types of studies, the effect of differences of anindependent variable (or variables) on the behavior of the dependent variable are observed.The difference between the two types lies in how the study is actually conducted. Each can bevery effective. An experimental study involves taking measurements of the system under study,manipulating the system, and then taking additional measurements using the same procedureto determine if the manipulation has modified the values of the measurements. In contrast, anobservational study does not involve experimental manipulation. Instead, data are gatheredand correlations between predictors and response are investigated. While the tools of dataanalysis work best on data from randomized studies, they are also applied to other kinds of data– like natural experiments and observational studies – for which a statistician would use amodified, more structured estimation method (e.g., Difference in differences estimation andinstrumental variables, among many others) that produce consistent estimators.

Experiments

The basic steps of a statistical experiment are:

Planning the research, including finding the number of replicates of the study, using thefollowing information: preliminary estimates regarding the size of treatment effects, alternativehypotheses, and the estimated experimental variability. Consideration of the selection ofexperimental subjects and the ethics of research is necessary. Statisticians recommend thatexperiments compare (at least) one new treatment with a standard treatment or control, toallow an unbiased estimate of the difference in treatment effects.

Design of experiments, using blocking to reduce the influence of confounding variables, andrandomized assignment of treatments to subjects to allow unbiased estimates of treatmenteffects and experimental error. At this stage, the experimenters and statisticians write theexperimental protocol that will guide the performance of the experiment and which specifiesthe primary analysis of the experimental data.

Performing the experiment following the experimental protocol and analyzing the data

following the experimental protocol.

Further examining the data set in secondary analyses, to suggest new hypotheses for futurestudy.

Documenting and presenting the results of the study.

An example of an observational study is one that explores the association between smoking andlung cancer. This type of study typically uses a survey to collect observations about the area ofinterest and then performs statistical analysis. In this case, the researchers would collectobservations of both smokers and non-smokers, perhaps through a case-control study, andthen look for the number of cases of lung cancer in each group.

Types of data

Because variables conforming only to nominal or ordinal measurements cannot be reasonablymeasured numerically, sometimes they are grouped together as categorical variables, whereasratio and interval measurements are grouped together as quantitative variables, which can beeither discrete or continuous, due to their numerical nature. Such distinctions can often beloosely correlated with data type in computer science, in that dichotomous categorical variablesmay be represented with the Boolean data type, polytomous categorical variables witharbitrarily assigned integers in the integral data type, and continuous variables with the realdata type involving floating point computation. But the mapping of computer science data typesto statistical data types depends on which categorization of the latter is being implemented.

The issue of whether or not it is appropriate to apply different kinds of statistical methods todata obtained from different kinds of measurement procedures is complicated by issuesconcerning the transformation of variables and the precise interpretation of research questions."The relationship between the data and what they describe merely reflects the fact that certainkinds of statistical statements may have truth values which are not invariant under sometransformations. Whether or not a transformation is sensible to contemplate depends on thequestion one is trying to answer" .

Statistics, estimators and pivotal quantities

Consider independent identically distributed (IID) random variables with a given probabilitydistribution: standard statistical inference and estimation theory defines a random sample asthe random vector given by the column vector of these IID variables.[19] The population beingexamined is described by a probability distribution that may have unknown parameters.

A statistic is a random variable that is a function of the random sample, but not a function ofunknown parameters. The probability distribution of the statistic, though, may have unknownparameters.

Consider now a function of the unknown parameter: an estimator is a statistic used to estimatesuch function. Commonly used estimators include sample mean, unbiased sample variance andsample covariance.

A random variable that is a function of the random sample and of the unknown parameter, butwhose probability distribution does not depend on the unknown parameter is called a pivotalquantity or pivot. Widely used pivots include the z-score, the chi square statistic and Student'st-value.

Between two estimators of a given parameter, the one with lower mean squared error is said tobe more efficient. Furthermore, an estimator is said to be unbiased if its expected value is equalto the true value of the unknown parameter being estimated, and asymptotically unbiased if itsexpected value converges at the limit to the true value of such parameter.

Other desirable properties for estimators include: UMVUE estimators that have the lowestvariance for all possible values of the parameter to be estimated (this is usually an easierproperty to verify than efficiency) and consistent estimators which converges in probability tothe true value of such parameter.

This still leaves the question of how to obtain estimators in a given situation and carry thecomputation, several methods have been proposed: the method of moments, the maximumlikelihood method, the least squares method and the more recent method of estimatingequations.

Error

Working from a null hypothesis, two basic forms of error are recognized:

Type I errors where the null hypothesis is falsely rejected giving a "false positive".

Type II errors where the null hypothesis fails to be rejected and an actual difference betweenpopulations is missed giving a "false negative".

Standard deviation refers to the extent to which individual observations in a sample differ from

a central value, such as the sample or population mean, while Standard error refers to anestimate of difference between sample mean and population mean.

A statistical error is the amount by which an observation differs from its expected value, aresidual is the amount an observation differs from the value the estimator of the expectedvalue assumes on a given sample (also called prediction).

Mean squared error is used for obtaining efficient estimators, a widely used class of estimators.Root mean square error is simply the square root of mean squared error.

Many statistical methods seek to minimize the residual sum of squares, and these are called"methods of least squares" in contrast to Least absolute deviations. The latter gives equalweight to small and big errors, while the former gives more weight to large errors. Residual sumof squares is also differentiable, which provides a handy property for doing regression. Leastsquares applied to linear regression is called ordinary least squares method and least squaresapplied to nonlinear regression is called non-linear least squares. Also in a linear regressionmodel the non deterministic part of the model is called error term, disturbance or more simplynoise. Both linear regression and non-linear regression are addressed in polynomial leastsquares, which also describes the variance in a prediction of the dependent variable (y axis) as afunction of the independent variable (x axis) and the deviations (errors, noise, disturbances)from the estimated (fitted) curve.

Measurement processes that generate statistical data are also subject to error. Many of theseerrors are classified as random (noise) or systematic (bias), but other types of errors (e.g.,blunder, such as when an analyst reports incorrect units) can also be important. The presenceof missing data and/or censoring may result in biased estimates and specific techniques havebeen developed to address these problems.

Interval estimation

Most studies only sample part of a population, so results don't fully represent the wholepopulation. Any estimates obtained from the sample only approximate the population value.Confidence intervals allow statisticians to express how closely the sample estimate matches thetrue value in the whole population. Often they are expressed as 95% confidence intervals.Formally, a 95% confidence interval for a value is a range where, if the sampling and analysiswere repeated under the same conditions (yielding a different dataset), the interval wouldinclude the true (population) value in 95% of all possible cases. This does not imply that theprobability that the true value is in the confidence interval is 95%. From the frequentistperspective, such a claim does not even make sense, as the true value is not a random variable.Either the true value is or is not within the given interval. However, it is true that, before any

data are sampled and given a plan for how to construct the confidence interval, the probabilityis 95% that the yet-to-be-calculated interval will cover the true value: at this point, the limits ofthe interval are yet-to-be-observed random variables. One approach that does yield an intervalthat can be interpreted as having a given probability of containing the true value is to use acredible interval from Bayesian statistics: this approach depends on a different way ofinterpreting what is meant by "probability", that is as a Bayesian probability.

In principle confidence intervals can be symmetrical or asymmetrical. An interval can beasymmetrical because it works as lower or upper bound for a parameter (left-sided interval orright sided interval), but it can also be asymmetrical because the two sided interval is builtviolating symmetry around the estimate. Sometimes the bounds for a confidence interval arereached asymptotically and these are used to approximate the true bounds.

Statistics rarely give a simple Yes/No type answer to the question under analysis. Interpretationoften comes down to the level of statistical significance applied to the numbers and often refersto the probability of a value accurately rejecting the null hypothesis (sometimes referred to asthe p-value).

The standard approach is to test a null hypothesis against an alternative hypothesis. A criticalregion is the set of values of the estimator that leads to refuting the null hypothesis. Theprobability of type I error is therefore the probability that the estimator belongs to the criticalregion given that null hypothesis is true (statistical significance) and the probability of type IIerror is the probability that the estimator doesn't belong to the critical region given that thealternative hypothesis is true. The statistical power of a test is the probability that it correctlyrejects the null hypothesis when the null hypothesis is false.

Referring to statistical significance does not necessarily mean that the overall result issignificant in real world terms. For example, in a large study of a drug it may be shown that thedrug has a statistically significant but very small beneficial effect, such that the drug is unlikelyto help the patient noticeably.

While in principle the acceptable level of statistical significance may be subject to debate, thep-value is the smallest significance level that allows the test to reject the null hypothesis. This islogically equivalent to saying that the p-value is the probability, assuming the null hypothesis istrue, of observing a result at least as extreme as the test statistic. Therefore, the smaller thep-value, the lower the probability of committing type I error.

Some problems are usually associated with this framework (See criticism of hypothesis testing):

A difference that is highly statistically significant can still be of no practical significance, but itis possible to properly formulate tests to account for this. One response involves going beyondreporting only the significance level to include the p-value when reporting whether a hypothesis

is rejected or accepted. The p-value, however, does not indicate the size or importance of theobserved effect and can also seem to exaggerate the importance of minor differences in largestudies. A better and increasingly common approach is to report confidence intervals. Althoughthese are produced from the same calculations as those of hypothesis tests or p-values, theydescribe both the size of the effect and the uncertainty surrounding it.

Fallacy of the transposed conditional, aka prosecutor's fallacy: criticisms arise because thehypothesis testing approach forces one hypothesis (the null hypothesis) to be favored, sincewhat is being evaluated is probability of the observed result given the null hypothesis and notprobability of the null hypothesis given the observed result. An alternative to this approach isoffered by Bayesian inference, although it requires establishing a prior probability.

Rejecting the null hypothesis does not automatically prove the alternative hypothesis.

As everything in inferential statistics it relies on sample size, and therefore under fat tailsp-values may be seriously mis-computed.

Some well-known statistical tests and procedures are:

Analysis of variance (ANOVA)

Chi-squared test

Correlation

Factor analysis

Mann–Whitney U

Mean square weighted deviation (MSWD)

Pearson product-moment correlation coefficient

Regression analysis

Spearman's rank correlation coefficient

Student's t-test

Time series analysis

Conjoint Analysis

Misuse of statistics can produce subtle, but serious errors in description andinterpretation—subtle in the sense that even experienced professionals make such errors, andserious in the sense that they can lead to devastating decision errors. For instance, social policy,

medical practice, and the reliability of structures like bridges all rely on the proper use ofstatistics.

Even when statistical techniques are correctly applied, the results can be difficult to interpretfor those lacking expertise. The statistical significance of a trend in the data—which measuresthe extent to which a trend could be caused by random variation in the sample—may or maynot agree with an intuitive sense of its significance. The set of basic statistical skills (andskepticism) that people need to deal with information in their everyday lives properly isreferred to as statistical literacy.

Ways to avoid misuse of statistics include using proper diagrams and avoiding bias.Misuse canoccur when conclusions are overgeneralized and claimed to be representative of more thanthey really are, often by either deliberately or unconsciously overlooking sampling bias.Bargraphs are arguably the easiest diagrams to use and understand, and they can be made eitherby hand or with simple computer programs. Unfortunately, most people do not look for bias orerrors, so they are not noticed. Thus, people may often believe that something is true even if itis not well represented. To make data gathered from statistics believable and accurate, thesample taken must be representative of the whole.

The concept of correlation is particularly noteworthy for the potential confusion it can cause.Statistical analysis of a data set often reveals that two variables (properties) of the populationunder consideration tend to vary together, as if they were connected. For example, a study ofannual income that also looks at age of death might find that poor people tend to have shorterlives than affluent people. The two variables are said to be correlated; however, they may ormay not be the cause of one another. The correlation phenomena could be caused by a third,previously unconsidered phenomenon, called a lurking variable or confounding variable. For thisreason, there is no way to immediately infer the existence of a causal relationship between thetwo variables.

"Applied statistics" comprises descriptive statistics and the application of inferentialstatistics.Theoretical statistics concerns both the logical arguments underlying justification ofapproaches to statistical inference, as well encompassing mathematical statistics. Mathematicalstatistics includes not only the manipulation of probability distributions necessary for derivingresults related to methods of estimation and inference, but also various aspects ofcomputational statistics and the design of experiments.

There are two applications for machine learning and data mining: data management and dataanalysis. Statistics tools are necessary for the data analysis.

Statistics is applicable to a wide variety of academic disciplines, including natural and socialsciences, government, and business. Statistical consultants can help organizations and

companies that don't have in-house expertise relevant to their particular questions.

Statistical computing

The rapid and sustained increases in computing power starting from the second half of the 20thcentury have had a substantial impact on the practice of statistical science. Early statisticalmodels were almost always from the class of linear models, but powerful computers, coupledwith suitable numerical algorithms, caused an increased interest in nonlinear models (such asneural networks) as well as the creation of new types, such as generalized linear models andmultilevel models.

Traditionally, statistics was concerned with drawing inferences using a semi-standardizedmethodology that was "required learning" in most sciences. This has changed with use ofstatistics in non-inferential contexts. What was once considered a dry subject, taken in manyfields as a degree-requirement, is now viewed enthusiastically. Initially derided by somemathematical purists, it is now considered essential methodology in certain areas.

In number theory, scatter plots of data generated by a distribution function may betransformed with familiar tools used in statistics to reveal underlying patterns, which may thenlead to hypotheses.

Methods of statistics including predictive methods in forecasting are combined with chaostheory and fractal geometry to create video works that are considered to have great beauty.

The process art of Jackson Pollock relied on artistic experiments whereby underlyingdistributions in nature were artistically revealed.[citation needed] With the advent ofcomputers, statistical methods were applied to formalize such distribution-driven naturalprocesses to make and analyze moving video art.

Methods of statistics may be used predicatively in performance art, as in a card trick based ona Markov process that only works some of the time, the occasion of which can be predictedusing statistical methodology.[9]

Statistic

A statistic (singular) or sample statistic is a single measure of some attribute of a sample (e.g.,its arithmetic mean value). It is calculated by applying a function (statistical algorithm) to thevalues of the items of the sample, which are known together as a set of data.

More formally, statistical theory defines a statistic as a function of a sample where the functionitself is independent of the sample's distribution; that is, the function can be stated beforerealization of the data. The term statistic is used both for the function and for the value of the

function on a given sample.

A statistic is distinct from a statistical parameter, which is not computable because often thepopulation is much too large to examine and measure all its items. However, a statistic, whenused to estimate a population parameter, is called an estimator. For instance, the sample meanis a statistic that estimates the population mean, which is a parameter.

When a statistic (a function) is being used for a specific purpose, it may be referred to by aname indicating its purpose: in descriptive statistics, a descriptive statistic is used to describethe data; in estimation theory, an estimator is used to estimate a parameter of the distribution(population); in statistical hypothesis testing, a test statistic is used to test a hypothesis.However, a single statistic can be used for multiple purposes – for example the sample meancan be used to describe a data set, to estimate the population mean, or to test a hypothesis.

In calculating the arithmetic mean of a sample, for example, the algorithm works by summingall the data values observed in the sample and then dividing this sum by the number of dataitems. This single measure, the mean of the sample, is called a statistic; its value is frequentlyused as an estimate of the mean value of all items comprising the population from which thesample is drawn. The population mean is also a single measure; however, it is not called astatistic, because it is not obtained from a sample; instead it is called a population parameter,because it is obtained from the whole population.

Other examples of statistics include

Sample mean discussed in the example above and sample median

Sample variance and sample standard deviation

Sample quantiles besides the median, e.g., quartiles and percentiles

Test statistics, such as t statistics, chi-squared statistics, f statistics

Order statistics, including sample maximum and minimum

Sample moments and functions thereof, including kurtosis and skewness

Various functionals of the empirical distribution function

A statistic is an observable random variable, which differentiates it both from a parameter thatis a generally unobservable quantity describing a property of a statistical population, and froman unobservable random variable, such as the difference between an observed measurementand a population average. A parameter can only be computed exactly if the entire populationcan be observed without error; for instance, in a perfect census or for a population of

standardized test takers.

Statisticians often contemplate a parameterized family of probability distributions, any memberof which could be the distribution of some measurable aspect of each member of a population,from which a sample is drawn randomly. For example, the parameter may be the averageheight of 25-year-old men in North America. The height of the members of a sample of 100such men are measured; the average of those 100 numbers is a statistic. The average of theheights of all members of the population is not a statistic unless that has somehow also beenascertained (such as by measuring every member of the population). The average height thatwould be calculated using the all of the individual heights of all 25-year-old North Americanmen is a parameter, and not a statistic. [10]

Financial accounting

Financial accounting (or financial accountancy) is the field of accounting concerned with thesummary, analysis and reporting of financial transactions pertaining to a business. This involvesthe preparation of financial statements available for public consumption. Stockholders,suppliers, banks, employees, government agencies, business owners, and other stakeholdersare examples of people interested in receiving such information for decision making purposes.

Financial accountancy is governed by both local and international accounting standards.Generally Accepted Accounting Principles (GAAP) is the standard framework of guidelines forfinancial accounting used in any given jurisdiction. It includes the standards, conventions andrules that accountants follow in recording and summarising and in the preparation of financialstatements. On the other hand, International Financial Reporting Standards (IFRS) is a set ofinternational accounting standards stating how particular types of transactions and otherevents should be reported in financial statements. IFRS are issued by the InternationalAccounting Standards Board (IASB). With IFRS becoming more widespread on the internationalscene, consistency in financial reporting has become more prevalent between globalorganisations.

While financial accounting is used to prepare accounting information for people outside theorganisation or not involved in the day-to-day running of the company, managementaccounting provides accounting information to help managers make decisions to manage thebusiness.

Objectives of financial accounting

Financial accounting and financial reporting are often used as synonyms.

1. According to International Financial Reporting Standards, the objective of financial reportingis:

To provide financial information about the reporting entity that is useful to existing andpotential investors, lenders and other creditors in making decisions about providing resourcesto the entity.[4]

2. According to the European Accounting Association:

Capital maintenance is a competing objective of financial reporting.

Qualities of financial accounting

Financial accounting is the preparation of financial statements that can be consumed by thepublic and the relevant stakeholders using either Historical Cost Accounting (HCA) or ConstantPurchasing Power Accounting (CPPA). When producing financial statements, they must complyto the following:

Relevance: Financial accounting which is decision-specific. It must be possible for accountinginformation to influence decisions. Unless this characteristic is present, there is no point incluttering statements.

Materiality: information is material if its omission or misstatement could influence theeconomic decisions of users taken on the basis of the financial statements.

Reliability: accounting must be free from significant error or bias. It should be easily reliedupon by managers. Often information that is highly relevant isn’t very reliable, and vice versa.

Understandability: accounting reports should be expressed as clearly as possible and shouldbe understood by those to whom the information is relevant.

Comparability: financial reports from different periods should be comparable with oneanother in order to derive meaningful conclusions about the trends in an entity’s financialperformance and position over time. Comparability can be ensured by applying the sameaccounting policies over time.

Three components of financial statements

Statement of Cash Flows

The Statement of Cash Flows considers the inputs and outputs in concrete cash within a statedperiod. The general template of a cash flow statement is as follows: Cash Inflow - Cash Outflow+ Opening Balance = Closing Balance

Example 1: in the beginning of September, Ellen started out with $5 in her bank account. Duringthat same month, Ellen borrowed $20 from Tom. At the end of the month, Ellen bought a pairof shoes for $7. Ellen's cash flow statement for the month of September looks like this:

Cash inflow: $20

Cash outflow: $7

Opening balance: $5

Closing balance: $20 – $7 + $5 = $18

Example 2: in the beginning of June, WikiTables, a company that buys and resells tables, sold 2tables. They'd originally bought the tables for $25 each, and sold them at a price of $50 pertable. The first table was paid out in cash however the second one was bought in credit terms.WikiTables' cash flow statement for the month of June looks like this:

Cash inflow: $50 - How much WikiTables received in cash for the first table. They didn'treceive cash for the second table (sold in credit terms).

Cash outflow: $50 - How much they'd originally bought the 2 tables for.

Opening balance: $0

Closing balance: $50 – $50 + $0 = $0 - Indeed, the cash flow for the month of June forWikiTables amounts to $0 and not $50.

Important: the cash flow statement only considers the exchange of actual cash, and ignoreswhat the person in question owes or is owed.

Profit and Loss Statement (also called Statement of Comprehensive Income)

In case of service organisations they are called as profit & loss a/c as income statement.

the profit or loss is determined by:

Sales (revenue) – Cost of Sales – total expenses + total income – tax paid = profit/loss

If there's a negative balance, it's a loss

if there's a positive balance, it's a profit

Statement of Financial Position (also called Balance Sheet)[edit]

The balance sheet is the statement showing assets & liabilities. As per the proforma, on its rightit shows assets and on its left side it shows liabilities. It helps know the status of a company. Thedifference between current assets and current liabilities is called working capital. The assets aremainly divided into 2 types:

fixed assets and

current assets

The liabilities are

long term liabilities and

short term liabilities or current liabilities.

The statements assist detailed study and analysis in each segment. For suppose in case of if youanalyse the income or profit and loss statement that means you analyse the real meaning tohow much earned or sustained loss when compare to last financial year to this year.

Basic accounting concepts[edit]

THE STABLE MEASURING UNIT ASSUMPTION One of the basic principles in accounting is “TheMeasuring Unit principle:

The unit of measure in accounting shall be the base money unit of the most relevantcurrency. This principle also assumes the unit of measure is stable; that is, changes in its generalpurchasing power are not considered sufficiently important to require adjustments to the basicfinancial statements.”

Historical Cost Accounting, i.e., financial capital maintenance in nominal monetary units, isbased on the stable measuring unit assumption under which accountants simply assume thatmoney, the monetary unit of measure, is perfectly stable in real value for the purpose ofmeasuring (1) monetary items not inflation-indexed daily in terms of the Daily CPI and (2)constant real value non-monetary items not updated daily in terms of the Daily CPI during lowand high inflation and deflation.

UNITS OF CONSTANT PURCHASING POWER The stable measuring unit assumption is not appliedduring hyperinflation. IFRS requires entities to implement capital maintenance in units ofconstant purchasing power in terms of IAS 29 Financial Reporting in HyperinflationaryEconomies.

Financial accountants produce financial statements based on the accounting standards in agiven jurisdiction. These standards may be the Generally Accepted Accounting Principles of arespective country, which are typically issued by a national standard setter, or InternationalFinancial Reporting Standards (IFRS), which are issued by the International AccountingStandards Board (IASB).

Financial accounting serves the following purposes:

producing general purpose financial statements

producing information used by the management of a business entity for decision making,planning and performance evaluation

producing financial statements for meeting regulatory requirements.

Objectives of Financial Accounting

Systematic recording of transactions: basic objective of accounting is to systematically recordthe financial aspects of business transactions (i.e. book-keeping). These recorded transactionsare later on classified and summarized logically for the preparation of financial statements andfor their analysis and interpretation.

Ascertainment of result of above recorded transactions: accountant prepares profit and lossaccount to know the result of business operations for a particular period of time. If expensesexceed revenue then it is said that business running under loss. The profit and loss accounthelps the management and different stakeholders in taking rational decisions. For example, ifbusiness is not proved to be remunerative or profitable, the cause of such a state of affair canbe investigated by the management for taking remedial steps.

Ascertainment of the financial position of business: businessman is not only interested inknowing the result of the business in terms of profits or loss for a particular period but is alsoanxious to know that what he owes (liability) to the outsiders and what he owns (assets) on acertain date. To know this, accountant prepares a financial position statement of assets andliabilities of the business at a particular point of time and helps in ascertaining the financialhealth of the business.

Providing information to the users for rational decision-making: accounting as a ‘language ofbusiness’ communicates the financial result of an enterprise to various stakeholders by meansof financial statements. Accounting aims to meet the financial information needs of thedecision-makers and helps them in rational decision-making.

To know the solvency position: by preparing the balance sheet, management not only revealswhat is owned and owed by the enterprise, but also it gives the information regarding concern’sability to meet its liabilities in the short run (liquidity position) and also in the long-run (solvencyposition) as and when they fall due.

Financial accounting vs cost accounting

Financial accounting aims at finding out results of accounting year in the form of Profit andLoss Account and Balance Sheet. Cost Accounting aims at computing cost of production/servicein a scientific manner and facilitate cost control and cost reduction.

Financial accounting reports the results and position of business to government, creditors,investors, and external parties.

Cost Accounting is an internal reporting system for an organization’s own management fordecision making.

In financial accounting, cost classification based on type of transactions, e.g. salaries, repairs,insurance, stores etc. In cost accounting, classification is basically on the basis of functions,activities, products, process and on internal planning and control and information needs of theorganization.

Financial accounting aims at presenting ‘true and fair’ view of transactions, profit and loss fora period and Statement of financial position (Balance Sheet) on a given date. It aims atcomputing ‘true and fair’ view of the cost of production/services offered by the firm. [11]

Research

Research comprises "creative work undertaken on a systematic basis in order to increase thestock of knowledge, including knowledge of humans, culture and society, and the use of thisstock of knowledge to devise new applications."[1] It is used to establish or confirm facts,reaffirm the results of previous work, solve new or existing problems, support theorems, ordevelop new theories. A research project may also be an expansion on past work in the field. Totest the validity of instruments, procedures, or experiments, research may replicate elements of

prior projects, or the project as a whole. The primary purposes of basic research (as opposed toapplied research) are documentation, discovery, interpretation, or the research anddevelopment (R&D) of methods and systems for the advancement of human knowledge.Approaches to research depend on epistemologies, which vary considerably both within andbetween humanities and sciences. There are several forms of research: scientific, humanities,artistic, economic, social, business, marketing, practitioner research, life,technological,etc.

Forms of research

Scientific research is a systematic way of gathering data and harnessing curiosity. This researchprovides scientific information and theories for the explanation of the nature and the propertiesof the world. It makes practical applications possible. Scientific research is funded by publicauthorities, by charitable organizations and by private groups, including many companies.Scientific research can be subdivided into different classifications according to their academicand application disciplines. Scientific research is a widely used criterion for judging the standingof an academic institution, such as business schools, but some argue that such is an inaccurateassessment of the institution, because the quality of research does not tell about the quality ofteaching (these do not necessarily correlate).

Research in the humanities involves different methods such as for example hermeneutics andsemiotics, and a different, more relativist epistemology. Humanities scholars usually do notsearch for the ultimate correct answer to a question, but instead explore the issues and detailsthat surround it. Context is always important, and context can be social, historical, political,cultural, or ethnic. An example of research in the humanities is historical research, which isembodied in historical method. Historians use primary sources and other evidence tosystematically investigate a topic, and then to write histories in the form of accounts of thepast.

Artistic research, also seen as 'practice-based research', can take form when creative works areconsidered both the research and the object of research itself. It is the debatable body ofthought which offers an alternative to purely scientific methods in research in its search forknowledge and truth.

The Merriam-Webster Online Dictionary defines research in more detail as "a studious inquiryor examination; especially investigation or experimentation aimed at the discovery andinterpretation of facts, revision of accepted theories or laws in the light of new facts, orpractical application of such new or revised theories or laws".

Steps in conducting research

Research is often conducted using the hourglass model structure of research. The hourglassmodel starts with a broad spectrum for research, focusing in on the required informationthrough the method of the project (like the neck of the hourglass), then expands the research in

the form of discussion and results. The major steps in conducting research are:

Identification of research problem

Literature review

Specifying the purpose of research

Determine specific research questions

Specification of a conceptual framework, usually a set of hypotheses[9]

Choice of a methodology (for data collection)

Data collection

Verify data

Analyzing and interpreting the data

Reporting and evaluating research

Communicating the research findings and, possibly, recommendations

The steps generally represent the overall process; however, they should be viewed as anever-changing iterative process rather than a fixed set of steps. Most research begins with ageneral statement of the problem, or rather, the purpose for engaging in the study. Theliterature review identifies flaws or holes in previous research which provides justification forthe study. Often, a literature review is conducted in a given subject area before a researchquestion is identified. A gap in the current literature, as identified by a researcher, thenengenders a research question. The research question may be parallel to the hypothesis. Thehypothesis is the supposition to be tested. The researcher(s) collects data to test thehypothesis. The researcher(s) then analyzes and interprets the data via a variety of statisticalmethods, engaging in what is known as empirical research. The results of the data analysis inconfirming or failing to reject the Null hypothesis are then reported and evaluated. At the end,the researcher may discuss avenues for further research. However, some researchers advocatefor the flip approach: starting with articulating findings and discussion of them, moving "up" toidentification research problem that emerging in the findings and literature review introducingthe findings. The flip approach is justified by the transactional nature of the research endeavorwhere research inquiry, research questions, research method, relevant research literature, andso on are not fully known until the findings fully emerged and interpreted.

Plato in Meno talks about an inherent difficulty, if not a paradox, of doing research that can be

paraphrase in the following way, "If you know what you're searching for, why do you search forit?! [i.e., you have already found it] If you don't know what you're searching for, what are yousearching for?!"

Generally, research is understood to follow a certain structural process. Though step order mayvary depending on the subject matter and researcher, the following steps are usually part ofmost formal research, both basic and applied:

Observations and Formation of the topic: Consists of the subject area of ones interest andfollowing that subject area to conduct subject related research. The subject area should not berandomly chosen since it requires reading a vast amount of literature on the topic to determinethe gap in the literature the researcher intends to narrow. A keen interest in the chosen subjectarea is advisable. The research will have to be justified by linking its importance to alreadyexisting knowledge about the topic.

Hypothesis: A testable prediction which designates the relationship between two or morevariables.

Conceptual definition: Description of a concept by relating it to other concepts.

Operational definition: Details in regards to defining the variables and how they will bemeasured/assessed in the study.

Gathering of data: Consists of identifying a population and selecting samples, gatheringinformation from and/or about these samples by using specific research instruments. Theinstruments used for data collection must be valid and reliable.

Analysis of data: Involves breaking down the individual pieces of data in order to drawconclusions about it.

Data Interpretation: This can be represented through tables, figures and pictures, and thendescribed in words.

Test, revising of hypothesis

Conclusion, reiteration if necessary.

A common misconception is that a hypothesis will be proven (see, rather, Null hypothesis).Generally, a hypothesis is used to make predictions that can be tested by observing theoutcome of an experiment. If the outcome is inconsistent with the hypothesis, then the

hypothesis is rejected (see falsifiability). However, if the outcome is consistent with thehypothesis, the experiment is said to support the hypothesis. This careful language is usedbecause researchers recognize that alternative hypotheses may also be consistent with theobservations. In this sense, a hypothesis can never be proven, but rather only supported bysurviving rounds of scientific testing and, eventually, becoming widely thought of as true.

A useful hypothesis allows prediction and within the accuracy of observation of the time, theprediction will be verified. As the accuracy of observation improves with time, the hypothesismay no longer provide an accurate prediction. In this case, a new hypothesis will arise tochallenge the old, and to the extent that the new hypothesis makes more accurate predictionsthan the old, the new will supplant it. Researchers can also use a null hypothesis, which state norelationship or difference between the independent or dependent variables. A null hypothesisuses a sample of all possible people to make a conclusion about the population.

The historical method comprises the techniques and guidelines by which historians usehistorical sources and other evidence to research and then to write history. There are varioushistory guidelines that are commonly used by historians in their work, under the headings ofexternal criticism, internal criticism, and synthesis. This includes lower criticism and sensualcriticism. Though items may vary depending on the subject matter and researcher, the followingconcepts are part of most formal historical research:

Identification of origin date

Evidence of localization

Recognition of authorship

Analysis of data

Identification of integrity

Attribution of credibility

Research methods

The goal of the research process is to produce new knowledge or deepen understanding of atopic or issue. This process takes three main forms (although, as previously discussed, theboundaries between them may be obscure):

Exploratory research, which helps to identify and define a problem or question.

Constructive research, which tests theories and proposes solutions to a problem or question.

Empirical research, which tests the feasibility of a solution using empirical evidence.

There are two major types of empirical research design: qualitative research and quantitativeresearch. Researchers choose qualitative or quantitative methods according to the nature ofthe research topic they want to investigate and the research questions they aim to answer:

Qualitative research

Understanding of human behavior and the reasons that govern such behavior. Asking a broadquestion and collecting data in the form of words, images, video etc that is analyzed andsearching for themes. This type of research aims to investigate a question without attemptingto quantifiably measure variables or look to potential relationships between variables. It isviewed as more restrictive in testing hypotheses because it can be expensive andtime-consuming, and typically limited to a single set of research subjects.[citation needed]Qualitative research is often used as a method of exploratory research as a basis for laterquantitative research hypotheses.[citation needed] Qualitative research is linked with thephilosophical and theoretical stance of social constructionism.

Quantitative research

Systematic empirical investigation of quantitative properties and phenomena and theirrelationships. Asking a narrow question and collecting numerical data to analyze utilizingstatistical methods. The quantitative research designs are experimental, correlational, andsurvey (or descriptive). Statistics derived from quantitative research can be used to establishthe existence of associative or causal relationships between variables. Quantitative research islinked with the philosophical and theoretical stance of positivism.

The quantitative data collection methods rely on random sampling and structured datacollection instruments that fit diverse experiences into predetermined responsecategories.[citation needed] These methods produce results that are easy to summarize,compare, and generalize.[citation needed] Quantitative research is concerned with testinghypotheses derived from theory and/or being able to estimate the size of a phenomenon of

interest. Depending on the research question, participants may be randomly assigned todifferent treatments (this is the only way that a quantitative study can be considered a trueexperiment).[citation needed] If this is not feasible, the researcher may collect data onparticipant and situational characteristics in order to statistically control for their influence onthe dependent, or outcome, variable. If the intent is to generalize from the researchparticipants to a larger population, the researcher will employ probability sampling to selectparticipants.

In either qualitative or quantitative research, the researcher(s) may collect primary orsecondary data. Primary data is data collected specifically for the research, such as throughinterviews or questionnaires. Secondary data is data that already exists, such as census data,which can be re-used for the research. It is good ethical research practice to use secondary datawherever possible.

Mixed-method research, i.e. research that includes qualitative and quantitative elements, usingboth primary and secondary data, is becoming more common.

Big data has brought big impacts on research methods that now researchers do not put mucheffort on data collection, and also methods to analyze easily available huge amount of datahave also changed.

Nonempirical refers to an approach that is grounded in theory as opposed to using observationand experimentation to achieve the outcome. As such, nonempirical research seeks solutions toproblems using existing knowledge as its source. This, however, does not mean that new ideasand innovations cannot be found within the pool existing and established knowledge.Nonempirical is not an absolute alternative to empirical research because they may be usedtogether to strengthen a research approach. Neither one is less effective than the other sincethey have their particular purpose within life and in science. A simple example of a nonempiricaltask could the prototyping of a new drug using a differentiated application of existingknowledge; similarly, it could be the development of a business process in the form of a flowchart and texts where all the ingredients are from established knowledge. Empirical research onthe other hand seeks to create new knowledge through observations and experiments in whichestablished knowledge can either be contested or supplements.

There have been many controversies about research methods stemmed from a philosophical

positivism promise to distinguish the science from other practices (especially religion) by itsmethod. This promise leads to methodological hegemony and methodology wars where diverseresearchers, often coming from opposing paradigms, try to impose their own methodology onthe entire field or even on the science practice in general as the only legitimate one.

Epistemologies of different national sciences and cultural communities may differ and, thus,they may produce different methods of research. For example, psychological research in Russiatends to be rooted in philosophy while in the US and UK in empirism. Rich countries (anddominant cultural communities within them) and their national sciences may dominatescientific discourse through funding and publications. This academic hegemony can translateinto impositions of certain research methodologies through the gatekeeping process ofinternational academic publications, conference presentation selection, institutional reviewboards, and funding.

Academic publishing describes a system that is necessary in order for academic scholars to peerreview the work and make it available for a wider audience. The system varies widely by field,and is also always changing, if often slowly. Most academic work is published in journal articleor book form. There is also a large body of research that exists in either a thesis or dissertationform. These forms of research can be found in databases explicitly for theses and dissertations.In publishing, STM publishing is an abbreviation for academic publications in science,technology, and medicine.

Original research is research that is not exclusively based on a summary, review or synthesis ofearlier publications on the subject of research. This material is of a primary source character.The purpose of the original research is to produce new knowledge, rather than to present theexisting knowledge in a new form (e.g., summarized or classified).

Original research can take a number of forms, depending on the discipline it pertains to. Inexperimental work, it typically involves direct or indirect observation of the researchedsubject(s), e.g., in the laboratory or in the field, documents the methodology, results, andconclusions of an experiment or set of experiments, or offers a novel interpretation of previousresults. In analytical work, there are typically some new (for example) mathematical resultsproduced, or a new way of approaching an existing problem. In some subjects which do nottypically carry out experimentation or analysis of this kind, the originality is in the particular wayexisting understanding is changed or re-interpreted based on the outcome of the work of theresearcher.

The degree of originality of the research is among major criteria for articles to be published inacademic journals and usually established by means of peer review. Graduate students arecommonly required to perform original research as part of a dissertation.

The controversial trend of artistic teaching becoming more academics-oriented is leading toartistic research being accepted as the primary mode of enquiry in art as in the case of otherdisciplines. One of the characteristics of artistic research is that it must accept subjectivity as

opposed to the classical scientific methods. As such, it is similar to the social sciences in usingqualitative research and intersubjectivity as tools to apply measurement and critical analysis.[12]

Applied research

Applied research is a form of systematic inquiry involving the practical application of science. Itaccesses and uses some part of the research communities' (the academia's) accumulatedtheories, knowledge, methods, and techniques, for a specific, often state-, business-, orclient-driven purpose. Applied research is contrasted with pure research (basic research) indiscussion about research ideals, methodologies, programs, and projects. SApplied researchdeals with solving practical problems and generally employs empirical methodologies. Becauseapplied research resides in the messy real world, strict research protocols may need to berelaxed. For example, it may be impossible to use a random sample. Thus, transparency in themethodology is crucial. Implications for interpretation of results brought about by relaxing anotherwise strict canon of methodology should also be considered.[citation needed] SinceApplied Research has a provisional close to the problem and close to the data orientation it mayalso use a more provisional conceptual framework such as working hypothesis or pillarquestions.The OECD's Frascati Manual describes Applied Research as one of the three forms ofresearch, along with Basic research & Experimental Development. [13]

Qualitative research

Qualitative research is a broad methodological approach that encompasses many researchmethods. The aim of qualitative research may vary with the disciplinary background, such as apsychologist seeking to gather an in-depth understanding of human behavior and the reasonsthat govern such behavior. Qualitative methods examine the why and how of decision making,not just what, where, when, or "who", and has a strong basis in the field of sociology tounderstand government and social programs, and is popular among political science, socialwork, and special education and education majors.

In the conventional view by statisticians, qualitative methods produce information only on theparticular cases studied (e.g., ethnographies paid for by governmental funds which may involveresearch teams), and any more general conclusions are considered propositions (informedassertions). Quantitative methods can then be used to seek empirical support for such researchhypotheses.

In contrast, a qualitative researcher holds that understanding of a phenomenon or situation or

event comes from exploring the totality of the situation (e.g., phenomenology, symbolicinteractionism), often has access to large amounts of "hard data". It may begin as a groundedtheory approach with the researcher having no previous understanding of the phenomenon; orthe study may commence with propositions and proceed in a scientific and empirical waythroughout the research process.

The main difference between qualitative and quantitative methods is flexibleness. As a whole,quantitative methods have relative inflexibleness.

Qualitative methods are usually more flexible, allowing more naturalness and acclimatizationfor the interaction and collaboration between the researcher and the participant.

In qualitative methods, the relation between the participant and the researcher are informal tothe particular extent that it is in the quantitative.

In the early 1900s, some researchers rejected positivism, the theoretical idea that there is anobjective world which we can gather data from and "verify" this data through empiricism. Theseresearchers embraced a qualitative research paradigm, attempting to make qualitative researchas "rigorous" as quantitative research and creating myriad methods for qualitative research. Ofcourse, such developments were necessary as qualitative researchers won national centerawards, in collaboration with their research colleagues at other universities and departments;and university administration funded Ph.D.s in both arenas through the ensuing decades. Mosttheoretical constructs involve a process of qualitative analysis and understanding, andconstruction of these concepts.

Qualitative researchers face many choices for techniques to generate data ranging fromgrounded theory development and practice, narratology, storytelling, transcript poetry, classicalethnography, state or governmental studies, research and service demonstrations, focusgroups, case studies, participant observation, qualitative review of statistics, or shadowing,among many others. Qualitative methods are used in various methodological approaches, suchas action research which has sociological basis, or actor-network theory.

The most common method used to generate data in qualitative research is an interview whichmay be structured, semi-structured or unstructured. Other ways to generate data include groupdiscussions or focus groups, observations, reflective field notes, texts, pictures, and othermaterials. Very popular among qualitative researchers are the studies of photographs, publicand official documents, personal documents, and historical items in addition to images in themedia and literature fields.

To analyse qualitative data, the researcher seeks meaning from all of the data that is available.The data may be categorized and sorted into patterns (i.e., pattern or thematic analyses) as theprimary basis for organizing and reporting the study findings (e.g., activities in the home;interactions with government). Qualitative researchers, often associated with the educationfield, typically rely on the following methods for gathering information: Participant Observation,

Non-participant Observation, Field Notes, Reflexive Journals, Structured Interview,Semi-structured Interview, Unstructured Interview, and Analysis of documents and materials.

The data that is obtained is streamlined (texts of thousands of pages in length) to a definitetheme or pattern, or representation of a theory or systemic issue or approach. This step in atheoretical analysis or data analytic technique is further worked on (e.g., gender analysis maybe conducted; comparative policy analysis may be developed). An alternative researchhypothesis is generated which finally provides the basis of the research statement forcontinuing work in the fields.

Some distinctive qualitative methods are the use of focus groups and key informant interviews,the latter often identified through sophisticated and sometimes, elitist, snowballing techniques.The focus group technique involves a moderator facilitating a small group discussion betweenselected individuals on a particular topic, with video and handscribed data recorded, and isuseful in a coordinated research approach studying phenomenon in diverse ways in differentenvironments with distinct stakeholders often excluded from traditional processes. This methodis a particularly popular in market research and testing new initiatives with users/workers.

The research then must be "written up" into a report, book chapter, journal paper, thesis ordissertation, using descriptions, quotes from participants, charts and tables to demonstrate thetrustworthiness of the study findings.

Qualitative methods are often part of survey methodology, including telephone surveys andconsumer satisfaction surveys.

In fields that study households, a much debated topic is whether interviews should beconducted individually or collectively (e.g. as couple interviews).

There are several different research approaches, or research designs, that qualitativeresearchers use. In the academic social sciences, the most frequently used qualitative researchapproaches include the following points:

Basic/generic/pragmatic qualitative research, which involves using an eclectic approach takenup to best match the research question at hand. This is often called the mixed-methodapproach.

Ethnographic Research. An example of applied ethnographic research is the study of aparticular culture and their understanding of the role of a particular disease in their culturalframework.

Grounded Theory is an inductive type of research, based or "grounded" in the observations ordata from which it was developed; it uses a variety of data sources, including quantitative data,

review of records, interviews, observation and surveys.[25]

Phenomenology describes the "subjective reality" of an event, as perceived by the studypopulation; it is the study of a phenomenon.

Philosophical Research is conducted by field experts within the boundaries of a specific fieldof study or profession, the best qualified individual in any field of study to use an intellectualanalysis, in order to clarify definitions, identify ethics, or make a value judgment concerning anissue in their field of study their lives.

Critical Social Research, used by a researcher to understand how people communicate anddevelop symbolic meanings.

Ethical Inquiry, an intellectual analysis of ethical problems. It includes the study of ethics asrelated to obligation, rights, duty, right and wrong, choice etc.

Social Science and Governmental Research to understand social services, governmentoperations, and recommendations (or not) regarding future developments and programs,including whether or not government should be involved.

Activist Research which aims to raise the views of the underprivileged or "underdogs" toprominence to the elite or master classes, the latter who often control the public view orpositions.

Foundational Research, examines the foundations for a science, analyzes the beliefs, anddevelops ways to specify how a knowledge base should change in light of new information.

Historical Research allows one to discuss past and present events in the context of thepresent condition, and allows one to reflect and provide possible answers to current issues andproblems. Historical research helps us in answering questions such as: Where have we comefrom, where are we, who are we now and where are we going?

Visual Ethnography. It uses visual methods of data collection, including photo, voice, photoelicitation, collaging, drawing, and mapping. These techniques have been used extensively as aparticipatory qualitative technique and to make the familiar strange.

Autoethnography, the study of self, is a method of qualitative research in which theresearcher uses their personal experience to address an issue.

The most common analysis of qualitative data is observer impression. That is, expert orbystander observers examine the data, interpret it via forming an impression and report theirimpression in a structured and sometimes quantitative form.

Coding is an interpretive technique that both organizes the data and provides a means tointroduce the interpretations of it into certain quantitative methods. Most coding requires theanalyst to read the data and demarcate segments within it, which may be done at different

times throughout the process. Each segment is labeled with a "code" – usually a word or shortphrase that suggests how the associated data segments inform the research objectives. Whencoding is complete, the analyst prepares reports via a mix of: summarizing the prevalence ofcodes, discussing similarities and differences in related codes across distinct originalsources/contexts, or comparing the relationship between one or more codes.

Some qualitative data that is highly structured (e.g., close-end responses from surveys or tightlydefined interview questions) is typically coded without additional segmenting of the content. Inthese cases, codes are often applied as a layer on top of the data. Quantitative analysis of thesecodes is typically the capstone analytical step for this type of qualitative data. The mostcommon form of coding is open-ended coding, while other more structured techniques such asaxial coding or integration are described. However, more important than coding are qualitiessuch as the "theoretical sensitivity" of the researcher.

Contemporary qualitative data analyses are sometimes supported by computer programs,termed Computer Assisted Qualitative Data Analysis Software which has replaced the detailedhand coding and labeling of the past decades. These programs do not supplant the interpretivenature of coding but rather are aimed at enhancing the analyst’s efficiency at datastorage/retrieval and at applying the codes to the data. Many programs offer efficiencies inediting and revising coding, which allow for work sharing, peer review, and recursiveexamination of data. The university goals were to place such programs on computermainframes and analyze large data sets which is not easily conducted past 1,000 to 2,000 pagesof text.

Common Qualitative Data Analysis Software includes:

MAXQDA

QDA MINER

ATLAS.ti

Dedoose (mixed methods)

NVivo

A frequent criticism of coding method by individuals from other research tracks is that it seeksto transform qualitative data into empirically valid data, which contain: actual value range,structural proportion, contrast ratios, and scientific objective properties; thereby draining thedata of its variety, richness, and individual character. Analysts respond to this criticism bythoroughly expositing their definitions of codes and linking those codes soundly to the

underlying data, therein bringing back some of the richness that might be absent from a merelist of codes.

Some qualitative datasets are analyzed without coding. A common method here is recursiveabstraction, where datasets are summarized; those summaries are therefore furthered intosummary and so on. The end result is a more compact summary that would have been difficultto accurately discern without the preceding steps of distillation.

A frequent criticism of recursive abstraction is that the final conclusions are several timesremoved from the underlying data. While it is true that poor initial summaries will certainlyyield an inaccurate final report, qualitative analysts can respond to this criticism. They do so,like those using coding method, by documenting the reasoning behind each summary step,citing examples from the data where statements were included and where statements wereexcluded from the intermediate summary.

Some data analysis techniques, often referred to as the tedious, hard work of research studiessimilar to field notes, rely on using computers to scan and reduce large sets of qualitative data.At their most basic level, numerical coding relies on counting words, phrases, or coincidences oftokens within the data; other similar techniques are the analyses of phrases and exchanges inconversational analyses. Often referred to as content analysis, a basic structural building blockto conceptual analysis, the technique utilizes mixed methodology to unpack both small andlarge corpuses. Content analysis is frequently used in sociology to explore relationships, such asthe change in perceptions of race over time, or the lifestyles of temporal contractors. Contentanalysis techniques thus help to provide broader output for a larger, more accurate conceptualanalysis.

Mechanical techniques are particularly well-suited for a few scenarios. One such scenario is fordatasets that are simply too large for a human to effectively analyze, or where analysis of themwould be cost prohibitive relative to the value of information they contain. Another scenario iswhen the chief value of a dataset is the extent to which it contains "red flags" (e.g., searchingfor reports of certain adverse events within a lengthy journal dataset from patients in a clinicaltrial) or "green flags" (e.g., searching for mentions of your brand in positive reviews ofmarketplace products). Many researchers would consider these procedures on their data setsto be misuse of their data collection and purposes.

A frequent criticism of mechanical techniques is the absence of a human interpreter; computeranalysis is relatively new having arrived in the late 1980s to the university sectors. And whilemasters of these methods are able to write sophisticated software to mimic some humandecisions, the bulk of the "analysis" is still nonhuman. Analysts respond by proving the value oftheir methods relative to either a) hiring and training a human team to analyze the data or b) byletting the data go untouched, leaving any actionable nuggets undiscovered; almost all codingschemes indicate probably studies for further research.

Data sets and their analyses must also be written up, reviewed by other researchers, circulated

for comments, and finalized for public review. Numerical coding must be available in thepublished articles, if the methodology and findings are to be compared across research studiesin traditional literature review and recommendation formats.

Research

Research is the process of solving problems and finding facts in an organised way. Research isdone by using what is known (if anything), and building on it. Additional knowledge can be gotby proving (or falsifying) existing theories, and by trying to better explain observations.Research should be systematic, organized and objective.

Researchers take part in field or laboratory experiments, reading relevant books, journals orinternet sites, taking notes and making conclusions.

The scientific method is the usual way of doing this kind of research. It is meant to improveunderstanding of biology, engineering, physics, chemistry and many other fields. As a result,scientific research makes it possible to understand the world, and to discover useful things.

Any research should be:

Systematic: from an hypothesis or working objective, scientists gather data according to ascheme set out in advance. From this scientists change ideas or add new knowledge to thatalready existing. The approach used in research is the scientific method.

Organized: members of a research group use the same definitions, standards and principles.To achieve this, the research is planned in detail.

Objective: conclusions got from research must be based on observed and measured facts, noton subjective impressions. The conclusions should be unbiased.

Basic activities at research process

Studying available information on the subject.

Physical or computer modeling.

Measuring the phenomena.

Comparing the obtained results.

Interpreting the results with the current knowledge, considering the variables which mighthave influenced the result.

Types of research

Basic research, also called fundamental research or pure research, aims to understand nature.

Applied research aims at using the new knowledge to do something.

Operations research

Operational research (OR) encompasses a wide range of problem-solving techniques andmethods applied in the pursuit of improved decision-making and efficiency, such as simulation,mathematical optimization, queueing theory and other stochastic-process models, Markovdecision processes, econometric methods, data envelopment analysis, neural networks, expertsystems, decision analysis, and the analytic hierarchy process.Nearly all of these techniquesinvolve the construction of mathematical models that attempt to describe the system. Becauseof the computational and statistical nature of most of these fields, OR also has strong ties tocomputer science and analytics. Operational researchers faced with a new problem mustdetermine which of these techniques are most appropriate given the nature of the system, thegoals for improvement, and constraints on time and computing power.

The major subdisciplines in modern operational research, as identified by the journalOperations Research, are:

Computing and information technologies

Financial engineering

Manufacturing, service sciences, and supply chain management

Policy modeling and public sector work

Revenue management

Simulation

Stochastic models

Transportation

As a discipline, operational research originated in the efforts of military planners during WorldWar I (convoy theory and Lanchester's laws). In the decades after the two world wars, thetechniques were more widely applied to problems in business, industry and society. Since that

time, operational research has expanded into a field widely used in industries ranging frompetrochemicals to airlines, finance, logistics, and government, moving to a focus on thedevelopment of mathematical models that can be used to analyse and optimize complexsystems, and has become an area of active academic and industrial research.

With expanded techniques and growing awareness of the field at the close of the war,operational research was no longer limited to only operational, but was extended to encompassequipment procurement, training, logistics and infrastructure. Operations Research also grew inmany areas other than the military once scientists learned to apply its principles to the civiliansector. With the development of the simplex algorithm for linear programming in 1947 and thedevelopment of computers over the next three decades, Operations Research can now "solveproblems with hundreds of thousands of variables and constraints. Moreover, the largevolumes of data required for such problems can be stored and manipulated very efficiently."

Problems addressed

Critical path analysis or project planning: identifying those processes in a complex projectwhich affect the overall duration of the project

Floorplanning: designing the layout of equipment in a factory or components on a computerchip to reduce manufacturing time (therefore reducing cost)

Network optimization: for instance, setup of telecommunications networks to maintainquality of service during outages

Allocation problems

Facility location

Assignment Problems:

Assignment problem

Generalized assignment problem

Quadratic assignment problem

Weapon target assignment problem

Bayesian search theory: looking for a target

Optimal search

Routing, such as determining the routes of buses so that as few buses are needed as possible

Supply chain management: managing the flow of raw materials and products based onuncertain demand for the finished products

Efficient messaging and customer response tactics

Automation: automating or integrating robotic systems in human-driven operationsprocesses

Globalization: globalizing operations processes in order to take advantage of cheapermaterials, labor, land or other productivity inputs

Transportation: managing freight transportation and delivery systems (Examples: LTLshipping, intermodal freight transport, travelling salesman problem)

Scheduling:

Personnel staffing

Manufacturing steps

Project tasks

Network data traffic: these are known as queueing models or queueing systems.

Sports events and their television coverage

Blending of raw materials in oil refineries

Determining optimal prices, in many retail and B2B settings, within the disciplines of pricingscience

Operational research is also used extensively in government where evidence-based policy is

used.

Management science

However, in modern times the term management science may also be used to refer to theseparate fields of organizational studies or corporate strategy.[citation needed] Like operationalresearch itself, management science (MS) is an interdisciplinary branch of applied mathematicsdevoted to optimal decision planning, with strong links with economics, business, engineering,and other sciences. It uses various scientific research-based principles, strategies, and analyticalmethods including mathematical modeling, statistics and numerical algorithms to improve anorganization's ability to enact rational and meaningful management decisions by arriving atoptimal or near optimal solutions to complex decision problems. In short, management sciences

help businesses to achieve their goals using the scientific methods of operational research.

The management scientist's mandate is to use rational, systematic, science-based techniques toinform and improve decisions of all kinds. Of course, the techniques of management science arenot restricted to business applications but may be applied to military, medical, publicadministration, charitable groups, political groups or community groups.

Management science is concerned with developing and applying models and concepts that mayprove useful in helping to illuminate management issues and solve managerial problems, as wellas designing and developing new and better models of organizational excellence.

The application of these models within the corporate sector became known as management

science.

Some of the fields that have considerable overlap with Operations Research and ManagementScience include:

Business Analytics

Data mining

Decision analysis

Engineering

Financial engineering

Forecasting

Game theory

Graph theory

Industrial engineering

Logistics

Mathematical modeling

Mathematical optimization

Probability and statistics

Project management

Policy analysis

Simulation

Social network/Transportation forecasting models

Stochastic processes

Supply chain management

Applications of management science are abundant such as in airlines, manufacturingcompanies, service organizations, military branches, and government. The range of problemsand issues to which management science has contributed insights and solutions is vast. It

includes:

Scheduling airlines, trains, buses etc.

Assignment (assigning crew to flights, trains or buses; employees to projects)

Facility location (deciding the most appropriate location for the new facilities such as awarehouse, factory or fire station)

Network flows (managing the flow of water from reservoirs)

Health service (information and supply chain management for health services)

Game theory (identifying, understanding and developing the strategies adopted bycompanies)

Management science is also concerned with so-called "soft-operational analysis", whichconcerns methods for strategic planning, strategic decision support, and problem structuringmethods. In dealing with these sorts of challenges mathematical modeling and simulation arenot appropriate or will not suffice. Therefore, during the past 30 years, a number of

non-quantified modeling methods have been developed. These include:

stakeholder based approaches including metagame analysis and drama theory

morphological analysis and various forms of influence diagrams

approaches using cognitive mapping

the strategic choice approach

robustness analysis [16]

Educational research

Educational research refers to a variety of methods, in which individuals evaluate differentaspects of education including: “student learning, teaching methods, teacher training, andclassroom dynamics”.

Educational researchers have come to the consensus that educational research must beconducted in a rigorous and systematic way, although what this implies is often debated. Thereare a variety of disciplines which are each present to some degree in educational research.These include psychology, sociology, anthropology, and philosophy.The overlap in disciplinescreates a broad range from which methodology can be drawn.The findings of educationalresearch also need to be interpreted within the context in which they were discovered as theymay not be applicable in every time or place.

Characteristics

Educational research attempts to solve a problem.

Research involves gathering new data from primary or first-hand sources or using existingdata for a new purpose.

Research is based upon observable experience or empirical evidence.

Research demands accurate observation and description.

Research generally employs carefully designed procedures and rigorous analysis.

Research emphasizes the development of generalizations, principles or theories that will help

in understanding, prediction and/or control.

Research requires expertise—familiarity with the field; competence in methodology;technical skill in collecting and analyzing the data.

Research attempts to find an objective, unbiased solution to the problem and takes greatpains to validate the procedures employed.

Research is a deliberate and unhurried activity which is directional but often refines theproblem or questions as the research progresses.

Research is carefully recorded and reported to other persons interested in the problem.

There are two main approaches in educational research. The first is a basic approach. Thisapproach is also referred to as an academic research approach. The second approach is appliedresearch or a contract research approach. Both of these approaches have different purposeswhich influence the nature of the respective research.

Basic, or academic research focuses on the search for truth or the development of educationaltheory. Researchers with this background “design studies that can test, refine, modify, ordevelop theories”. Generally, these researchers are affiliated with an academic institution andare performing this research as part of their graduate or doctoral work.

The pursuit of information that can be directly applied to practice is aptly known as applied orcontractual research. Researchers in this field are trying to find solutions to existing educationalproblems. The approach is much more utilitarian as it strives to find information that willdirectly influence practice. Applied researchers are commissioned by a sponsor and areresponsible for addressing the needs presented by this employer. The goal of this research is“to determine the applicability of educational theory and principles by testing hypotheseswithin specific settings”.

The basis for educational research is the scientific method. The scientific method uses directedquestions and manipulation of variables to systematically find information about the teachingand learning process. In this scenario questions are answered by the analysis of data that iscollected specifically for the purpose of answering these questions.Hypotheses are written andsubsequently proved or disproved by data which leads to the creation of new hypotheses. Thetwo main types of data that are used under this method are qualitative and quantitative.

Qualitative research uses the data which is descriptive in nature. Tools that educationalresearchers use in collecting qualitative data include: observations, conducting interviews,conducting document analysis, and analyzing participant products such as journals, diaries,images or blogs,.

Types of qualitative research

Case study

Ethnography

Phenomenological Research

Narrative Research

Historical Research

Quantitative research uses data that is numerical and is based on the assumption that thenumbers will describe a single reality. Statistics are often applied to find relationships betweenvariables.

Types of quantitative research

Descriptive Survey Research

Experimental Research

Single — Subject Research

Causal — Comparative Research

Correlational Research

Meta-analysis

There also exists a new school of thought that these derivatives of the scientific method are fartoo reductionistic in nature,. Since educational research includes other disciplines such aspsychology, sociology, anthropology, science, and philosophy and refers to work done in a widevariety of contexts it is proposed that researchers should use "multiple research approachesand theoretical constructs". This could mean using a combination of qualitative and quantitativemethods as well as common methodology from the fields mentioned above. In social researchthis phenomenon is referred to as triangulation (social science). This idea is well summarized bythe work of Barrow in his text An introduction to philosophy of education:

"Since educational issues are of many different kinds and logical types, it is to be expected thatquite different types of research should be brought into play on different occasions. Thequestion therefore is not whether research into teaching should be conducted by means ofquantitative measures (on some such grounds as that they are more ‘objective’) or qualitativemeasures (on some such grounds as that they are more ‘insightful’), but what kind of research

can sensibly be utilized to look into this particular aspect of teaching as opposed to that."

Types of combined methods

Action Research

Program Evaluation [17]

Clinical research

Clinical research is a branch of healthcare science that determines the safety and effectiveness(efficacy) of medications, devices, diagnostic products and treatment regimens intended forhuman use. These may be used for prevention, treatment, diagnosis or for relieving symptomsof a disease. Clinical research is different from clinical practice. In clinical practice establishedtreatments are used, while in clinical research evidence is collected to establish a treatment.

The term clinical research refers to the entire bibliography of a drug/device/biologic, in fact anytest article from its inception in the lab to its introduction to the consumer market and beyond.Once the promising candidate or the molecule is identified in the lab, it is subjected topre-clinical studies or animal studies where different aspects of the test article (including itssafety toxicity if applicable and efficacy, if possible at this early stage) are studied.

In the United States, when a test article is unapproved or not yet cleared by the Food and DrugAdministration (FDA), or when an approved or cleared test article is used in a way that maysignificantly increase the risks (or decreases the acceptability of the risks), the data obtainedfrom the pre-clinical studies or other supporting evidence, case studies of off label use, etc. aresubmitted in support of an Investigational New Drug (IND) application[1] to the FDA for reviewprior to conducting studies that involve even one human and a test article if the results areintended to be submitted to or held for inspection by the FDA at any time in the future (in thecase of an already approved test article, if intended to submit or hold for inspection by the FDAin support of a change in labeling or advertising). Where devices are concerned the submissionto the FDA would be for an Investigational Device Exemption (IDE) application if the device is asignificant risk device or is not in some way exempt from prior submission to the FDA. Inaddition clinical research may require Institutional Review Board (IRB) or Research Ethics Board(REB) and possibly Other institutional Committee reviews, Privacy Board, Conflict of InterestCommittee, Radiation Safety Committee, Radioactive Drug Research Committee, etc. approvalwhether or not the research requires prior submission to the FDA. Clinical research reviewcriteria will depend on which federal regulations the research is subject to (e.g., (Department ofHealth and Human Services (DHHS) if federally funded, FDA as already discussed) and willdepend on which regulations the institutions subscribe to, in addition to any more stringentcriteria added by the institution possibly in response to state or local laws/policies or

accreditation entity recommendations. This additional layer of review (IRB/REB in particular) iscritical to the protection of human subjects especially when you consider that often researchsubject to the FDA regulation for prior submission is allowed to proceed, by those same FDAregulations, 30 days after submission to the FDA unless specifically notified by the FDA not toinitiate the study.

Clinical research is often conducted at academic medical centers and affiliated research studysites. These centers and sites provide the prestige of the academic institution as well as accessto larger metropolitan areas, providing a larger pool of medical participants.

The clinical research ecosystem involves a complex network of sites, pharmaceutical companiesand academic research institutions. This has led to a growing field of technologies used formanaging the data and operational factors of clinical research. Clinical research management isoften aided by eClinical systems to help automate the management and conducting of clinicaltrials.

In the European Union, the European Medicines Agency (EMA) acts in a similar fashion forstudies conducted in their region. These human studies are conducted in four phases inresearch subjects that give consent to participate in the clinical trials.

Clinical trials involving new drugs are commonly classified into four phases. Each phase of thedrug approval process is treated as a separate clinical trial. The drug-development process willnormally proceed through all four phases over many years. If the drug successfully passesthrough Phases I, II, and III, it will usually be approved by the national regulatory authority foruse in the general population. Phase IV are 'post-approval' studies.

Before pharmaceutical companies start clinical trials on a drug, they conduct extensivepre-clinical studies. [18]

Research ethics

Research ethics involves the application of fundamental ethical principles to a variety of topicsinvolving research, including scientific research. These include the design and implementationof research involving human experimentation, animal experimentation, various aspects ofacademic scandal, including scientific misconduct (such as fraud, fabrication of data andplagiarism), whistleblowing; regulation of research, etc. Research ethics is most developed as aconcept in medical research.

The academic research enterprise is built on a foundation of trust. Researchers trust that theresults reported by others are sound. Society trusts that the results of research reflect anhonest attempt by scientists and other researchers to describe the world accurately andwithout bias. But this trust will endure only if the scientific community devotes itself toexemplifying and transmitting the values associated with ethical research conduct.

There are many ethical issues to be taken into serious consideration for research. Sociologistsneed to be aware of having the responsibility to secure the actual permission and interests of allthose involved in the study. They should not misuse any of the information discovered, andthere should be a certain moral responsibility maintained towards the participants. There is aduty to protect the rights of people in the study as well as their privacy and sensitivity. Theconfidentiality of those involved in the observation must be carried out, keeping theiranonymity and privacy secure.

Research ethics in a medical context is dominated by principlism, an approach that has beencriticised as being decontextualised.

Research ethics is different throughout different types of educational communities. Everycommunity has its own set of morals. In Anthropology research ethics were formed to protectthose who are being researched and to protect the researcher from topics or events that maybe unsafe or may make either party feel uncomfortable. It is a widely observed guideline thatAnthropologists use especially when doing ethnographic fieldwork.

Research informants participating in individual or group interviews as well as ethnographicfieldwork are often required to sign an informed consent form which outlines the nature of theproject. Informants are typically assured anonymity and will be referred to using pseudonyms.There is however growing recognition that these formal measures are insufficient and do notnecessarily warrant a research project 'ethical'. Research with people should therefore not bebased solely on dominant and de-contextualised understandings of ethics, but should benegotiated reflexively and through dialogue with participants as a way to bridge global and localunderstandings of research ethics.

In Canada, there are many different types of research ethic boards that approve applications forresearch projects. The most common document that Canadian Universities follow is theTri-Council Policy Statement. However, there are other types of documents geared towardsdifferent educational aspects such as: biology, clinical practices, bio-technics and even stem cellresearch. The Tri-Council is actually the top three government grant agencies in Canada. If onewas to do research in Canada and apply for funds, their project would have to be approved bythe Tri-Council.

Furthermore, it is the researchers ethical responsibility to not harm the humans they arestudying, they also have a responsibility to science, and the public, as well as to future students.

In terms of research publications, a number of key issues include and are not restricted to:

Honesty. Honesty and integrity is a duty of each author and person, expert-reviewer andmember of journal editorial boards.

Review process. The peer-review process contributes to the quality control and it is an

essential step to ascertain the standing and originality of the research.

Ethical standards. Recent journal editorials presented some experience of unscrupulousactivities.

Authorship. Who may claim a right to authorship?In which order should the authors belisted? [19]

Research design

A research design is the document of the study. The design of a study defines the study type(descriptive, correlational, semi-experimental, experimental, review, meta-analytic) andsub-type (e.g., descriptive-longitudinal case study), research question, hypotheses, independentand dependent variables, experimental design, and, if applicable, data collection methods and astatistical analysis plan. Research design is the framework that has been created to seekanswers to research questions.

The role of the design warrants that the proof gathered enables the researcher to tackle theresearch problem in a coherent and explicit way.

There are many ways to classify research designs, but sometimes the distinction is artificial andother times different designs are combined. Nonetheless, the list below offers a number ofuseful distinctions between possible research designs.

Descriptive (e.g., case-study, naturalistic observation, Survey)

Correlational (e.g., case-control study, observational study)

Semi-experimental (e.g., field experiment, quasi-experiment)

Experimental (Experiment with random assignment)

Review (Literature review, Systematic review)

Meta-analytic (Meta-analysis)

Other research types include: action research, causal, exploratory, historical, mixed-method andphilosophical.

Sometimes a distinction is made between "fixed" and "flexible" or, synonymously,"quantitative" and "qualitative" research designs. However, fixed designs need not be

quantitative, and flexible design need not be qualitative. In fixed designs, the design of thestudy is fixed before the main stage of data collection takes place. Fixed designs are normallytheory driven; otherwise it is impossible to know in advance which variables need to becontrolled and measured. Often, these variables are measured quantitatively. Flexible designsallow for more freedom during the data collection process. One reason for using a flexibleresearch design can be that the variable of interest is not quantitatively measurable, such asculture. In other cases, theory might not be available before one starts the research.

The choice of how to group participants depends on the research hypothesis and on how theparticipants are sampled. In a typical experimental study, there will be at least one"experimental" condition (e.g., "treatment") and one "control" condition ("no treatment"), butthe appropriate method of grouping may depend on factors such as the duration ofmeasurement phase and participant characteristics:

Cohort study

Cross-sectional study

Cross-sequential study

Longitudinal study

Confirmatory research tests a priori hypotheses — outcome predictions that are made beforethe measurement phase begins. Such a priori hypotheses are usually derived from a theory orthe results of previous studies. The advantage of confirmatory research is that the result ismore meaningful, in the sense that it is much harder to claim that a certain result is statisticallysignificant. The reason for this is that in confirmatory research, one ideally strives to reduce theprobability of falsely reporting a non-significant result as significant. This probability is known asα-level or a type I error. Loosely speaking, if you know what you are looking for, you should bevery confident when and where you will find it; accordingly, you only accept a result assignificant if it is highly unlikely to have been observed by chance.

Exploratory research on the other hand seeks to generate a posteriori hypotheses by examininga data-set and looking for potential relations between variables. It is also possible to have anidea about a relation between variables but to lack knowledge of the direction and strength ofthe relation. If the researcher does not have any specific hypotheses beforehand, the study isexploratory with respect to the variables in question (although it might be confirmatory forothers). The advantage of exploratory research is that it is easier to make new discoveries dueto the less stringent methodological restrictions. Here, the researcher does not want to miss apotentially interesting relation and therefore aims to minimize the probability of rejecting a realeffect or relation, this probability is sometimes referred to as β and the associated error is oftype II. In other words, if you want to see whether some of your measured variables could be

related, you would want to increase your chances of finding a significant result by lowering thethreshold of what you deem to be significant.

Sometimes, a researcher may conduct exploratory research but report it as if it had beenconfirmatory ('Hypothesizing After the Results are Known', HARKing); this is a questionableresearch practice bordering fraud.

A distinction can be made between state problems and process problems. State problems aimto answer what the state of a phenomenon is at a given time, while process problems deal withthe change of phenomena over time. Examples of state problems are the level of mathematicalskills of sixteen-year-old children or the level, computer skills of the elderly, the depression levelof a person, etc. Examples of process problems are the development of mathematical skillsfrom puberty to adulthood, the change in computer skills when people get older and howdepression symptoms change during therapy.

State problems are easier to measure than process problems. State problems just require onemeasurement of the phenomena of interest, while process problems always require multiplemeasurements. Research designs like repeated measures and longitudinal study are needed toaddress process problems.

In an experimental design, the researcher actively tries to change the situation, circumstances,or experience of participants (manipulation), which may lead to a change in behavior oroutcomes for the participants of the study. The researcher randomly assigns participants todifferent conditions, measures the variables of interest and tries to control for confoundingvariables. Therefore, experiments are often highly fixed even before the data collection starts.

In a good experimental design, a few things are of great importance. First of all, it is necessaryto think of the best way to operationalize the variables that will be measured. Therefore, it isimportant to consider how the variable(s) will be measured, as well as which methods would bemost appropriate to answer the research question. In addition, the statistical analysis has to betaken into account. Thus, the researcher should consider what the expectations of the study areas well as how to analyse this outcome. Finally, in an experimental design the researcher mustthink of the practical limitations including the availability of participants as well as howrepresentative the participants are to the target population. It is important to consider each ofthese factors before beginning the experiment. Additionally, many researchers employ poweranalysis before they conduct an experiment, in order to determine how large the sample mustbe to find an effect of a given size with a given design at the desired probability of making aType I or Type II error.

Non-experimental research designs do not involve a manipulation of the situation,circumstances or experience of the participants. Non-experimental research designs can bebroadly classified into three categories. First, relational designs, in which a range of variables ismeasured. These designs are also called correlation studies, because correlation data are mostoften used in analysis. It is important to clarify here that correlation does not imply causation,

and rather identifies dependence of one variable on another. Correlational designs are helpfulin identifying the relation of one variable to another, and seeing the frequency of co-occurrencein two natural groups (See correlation and dependence). The second type is comparativeresearch. These designs compare two or more groups on one or more variable, such as theeffect of gender on grades. The third type of non-experimental research is a longitudinal design.A longitudinal design examines variables such as performance exhibited by a group or groupsover time.

Famous case studies are for example the descriptions about the patients of Freud, who werethoroughly analysed and described.

Bell (1999) states “a case study approach is particularly appropriate for individual researchersbecause it gives an opportunity for one aspect of a problem to be studied in some depth withina limited time scale”.

Grounded theory research is a systematic research process that works to develop "a process,and action or an interaction about a substantive topic". [20]

References:

[1] https://en.wikipedia.org/wiki/Business_communication

[2] https://en.wikipedia.org/wiki/Communication

[3] https://en.wikipedia.org/wiki/Ethics_in_business_communication

[4] https://en.wikipedia.org/wiki/Organizational_communication

[5] https://en.wikipedia.org/wiki/Professional_communication

[6] https://en.wikipedia.org/wiki/Applied_mathematics

[7] https://en.wikipedia.org/wiki/Category:Applied_mathematics

[8] https://en.wikipedia.org/wiki/Business_statistics

[9] https://en.wikipedia.org/wiki/Statistics

[10] https://en.wikipedia.org/wiki/Statistic

[11] https://en.wikipedia.org/wiki/Financial_accounting

[12] https://en.wikipedia.org/wiki/Research

[13] https://en.wikipedia.org/wiki/Applied_research

[14] https://en.wikipedia.org/wiki/Qualitative_research

[15] https://simple.wikipedia.org/wiki/Research

[16] https://en.wikipedia.org/wiki/Operations_research

[17] https://en.wikipedia.org/wiki/Educational_research

[18] https://en.wikipedia.org/wiki/Clinical_research

[19] https://en.wikipedia.org/wiki/Research_ethics

[20] https://en.wikipedia.org/wiki/Research_design

Rodel Sy NavarroDirector and Business and Management ConsultantRodel Sy Navarro Business Consultancy Services (RSNBCS)P2 C4, Block - 1, Lot - 35, St. Monique Subdivision,Pantok, Binangonan, Rizal 1940Tel / Mobile: +63-0917-733-3563E-mail: rsnbcs@gmail.com ; Email: businesslawsph@gmail.com

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