Which type of research is used most often to make projections or to check the reason why something is or is not happening?

In order to continue enjoying our site, we ask that you confirm your identity as a human. Thank you very much for your cooperation.

Business forecasting involves making informed guesses about certain business metrics, regardless of whether they reflect the specifics of a business, such as sales growth, or predictions for the economy as a whole. Financial and operational decisions are made based on economic conditions and how the future looks, albeit uncertain.

• Forecasting is valuable to businesses so that they can make informed business decisions.
• Financial forecasts are fundamentally informed guesses, and there are risks involved in relying on past data and methods that cannot include certain variables.
• Forecasting approaches include qualitative models and quantitative models.

Companies use forecasting to help them develop business strategies. Past data is collected and analyzed so that patterns can be found. Today, big data and artificial intelligence has transformed business forecasting methods. There are several different methods by which a business forecast is made. All the methods fall into one of two overarching approaches: qualitative and quantitative.

While there might be large variations on a practical level when it comes to business forecasting, on a conceptual level, most forecasts follow the same process:

1. A problem or data point is chosen. This can be something like "will people buy a high-end coffee maker?" or "what will our sales be in March next year?"
2. Theoretical variables and an ideal data set are chosen. This is where the forecaster identifies the relevant variables that need to be considered and decides how to collect the data.
3. Assumption time. To cut down the time and data needed to make a forecast, the forecaster makes some explicit assumptions to simplify the process.
4. A model is chosen. The forecaster picks the model that fits the dataset, selected variables, and assumptions.
5. Analysis. Using the model, the data is analyzed, and a forecast is made from the analysis.
6. Verification. The forecast is compared to what actually happens to identify problems, tweak some variables, or, in the rare case of an accurate forecast, pat themselves on the back.

Once the analysis has been verified, it must be condensed into an appropriate format to easily convey the results to stakeholders or decision-makers. Data visualization and presentation skills are helpful here.

There are two key types of models used in business forecasting—qualitative and quantitative models.

Qualitative models have typically been successful with short-term predictions, where the scope of the forecast was limited. Qualitative forecasts can be thought of as expert-driven, in that they depend on market mavens or the market as a whole to weigh in with an informed consensus.

Qualitative models can be useful in predicting the short-term success of companies, products, and services, but they have limitations due to their reliance on opinion over measurable data. Qualitative models include:

1. Market research: Polling a large number of people on a specific product or service to predict how many people will buy or use it once launched.
2. Delphi method: Asking field experts for general opinions and then compiling them into a forecast.

Quantitative models discount the expert factor and try to remove the human element from the analysis. These approaches are concerned solely with data and avoid the fickleness of the people underlying the numbers. These approaches also try to predict where variables such as sales, gross domestic product, housing prices, and so on, will be in the long term, measured in months or years. Quantitative models include:

1. The indicator approach: The indicator approach depends on the relationship between certain indicators, for example, GDP and the unemployment rate remaining relatively unchanged over time. By following the relationships and then following leading indicators, you can estimate the performance of the lagging indicators by using the leading indicator data.
2. Econometric modeling: This is a more mathematically rigorous version of the indicator approach. Instead of assuming that relationships stay the same, econometric modeling tests the internal consistency of datasets over time and the significance or strength of the relationship between datasets. Econometric modeling is applied to create custom indicators for a more targeted approach. However, econometric models are more often used in academic fields to evaluate economic policies.
3. Time series methods: Time series use past data to predict future events. The difference between the time series methodologies lies in the fine details, for example, giving more recent data more weight or discounting certain outlier points. By tracking what happened in the past, the forecaster hopes to get at least a better than average view of the future. This is one of the most common types of business forecasting because it is inexpensive and no better or worse than other methods.
   

Forecasting can be dangerous. Forecasts become a focus for companies and governments mentally limiting their range of actions by presenting the short to long-term future as pre-determined. Moreover, forecasts can easily break down due to random elements that cannot be incorporated into a model, or they can be just plain wrong from the start.

But business forecasting is vital for businesses because it allows them to plan production, financing, and other strategies. However, there are three problems with relying on forecasts:

1. The data is always going to be old. Historical data is all we have to go on, and there is no guarantee that the conditions in the past will continue in the future.
2. It is impossible to factor in unique or unexpected events, or externalities. Assumptions are dangerous, such as the assumption that banks were properly screening borrowers prior to the subprime meltdown. Black swan events have become more common as our reliance on forecasts has grown.
3. Forecasts cannot integrate their own impact. By having forecasts, accurate or inaccurate, the actions of businesses are influenced by a factor that cannot be included as a variable. This is a conceptual knot. In a worst-case scenario, management becomes a slave to historical data and trends rather than worrying about what the business is doing now.

Negatives aside, business forecasting is here to stay. Appropriately used, forecasting allows businesses to plan ahead for their needs, raising their chances of staying competitive in the markets. That's one function of business forecasting that all investors can appreciate.

Definition and Purpose

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, on what does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?

What do these studies tell you?

1. Can provide a basis for applying ethical decision-making to practice.
2. Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
3. Brings clarity to general guiding practices and principles of an individual or group.
4. Philosophy informs methodology.
5. Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
6. Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
7. Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.

What these studies don't tell you?

1. Limited application to specific research problems [answering the "So What?" question in social science research].
2. Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
3. While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
4. There are limitations in the use of metaphor as a vehicle of philosophical analysis.
5. There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa;  Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide. Washington, D.C.: Falmer Press, 1994; Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, CSLI, Stanford University, 2013.