Methodology of Scientific Research (Методология научного исследования). Учебное пособие

Examples of public sector activity range from delivering social security, administering urban planning and organizing national defenses.

The organization of the public sector (public ownership) can take several forms, including:

▪Direct administration funded through taxation; the delivering organization generally has no specific requirement to meet commercial success criteria, and production decisions are determined by government.

▪Publicly owned corporations (in some contexts, especially manufacturing, «state-owned enterprises«); which differ from direct administration in that they have greater commercial freedoms and are expected to operate according to commercial criteria, and production decisions are not generally taken by government (although goals may be set for them by government).

▪Partial outsourcing (of the scale many businesses do, e.g. for IT services), is considered a public sector model.

A borderline form is complete outsourcing.

A precise definition of outsourcing has yet to be agreed upon. Thus, the term is used inconsistently. However, outsourcing is often viewed as involving the contracting out of a business function – commonly one previously performed in-house – to an external provider. In this sense, two organizations may enter into a contractual agreement involving an exchange of services and payments. Of recent concern is the ability of businesses to outsource to suppliers outside the nation, sometimes referred to as offshoring or offshore outsourcing (which are odd terms because doing business with another country does not mean you have to go offshore.) In addition, several related terms have emerged to grasp various aspects of the complex relationship between economic organizations or networks, such as nearshoring, multisourcing and strategic outsourcing.

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Reasons

Or contracting out, with a privately owned corporation delivering the entire service on behalf of government. This may be considered a mixture of private sector operations with public ownership of assets, although in some forms the private sector's control and/or risk is so great that the service may no longer be considered part of the public sector. (See the United Kingdom's Private Finance Initiative.)

In spite of their name, public companies are not part of the public sector; they are a particular kind of private sector company that can offer their shares for sale to the general public.

Similar UN standards apply to natural capital and human capital measurement to assist in measurements required by TBL, e.g. the ecoBudget standard for reporting ecological footprint.

In the private sector, a commitment to corporate social responsibility implies a commitment to some form of TBL reporting. This is distinct from the more limited changes required to deal only with ecological issues.

Often, sustainable businesses have progressive environmental and human rights policies. In general, business is described as green if it matches the following four criteria:

1.It incorporates principles of sustainability into each of its business decisions.

2.It supplies environmentally friendly products or services that replaces demand for nongreen products and/or services.

3.It is greener than traditional competition.

4.It has made an enduring commitment to environmental principles in its business operations. A sustainable business is any organization that participates in environmentally friendly or green activities to ensure that all processes, products, and manufacturing activities adequately address current environmental concerns while maintaining a profit. In other words, it is a business that «meets the needs of the present world without compromising the ability of the future generations to meet their own

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needs». It is the process of assessing how to design products that will take advantage of the current environmental situation and how well a company’s products perform with renewable resources. The Brundtland Report emphasized that sustainability is a three-legged stool of people, planet, and profit. Sustainable businesses with the supply chain try to balance all three through the triple-bottom-line concept–using sustainable development and sustainable distribution to impact the environment, business growth, and the society.

Everyone affects the sustainability of the marketplace and the planet in some way. Sustainable development within a business can create value for customers, investors, and the environment. A sustainable business must meet customer needs while, at the same time, treating the environment well.

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Designing the experiment

This stage of the scientific method involves designing the steps that will test and evaluate the hypothesis, manipulating one or more variables to generate analyzable data.

The experiment should be designed with later statistical tests in mind, by making sure that the experiment has controls and a large enough sample group to provide statistically valid results.

Statistics tutorial

Research data

The results of a science investigation often contain much more data or information than the researcher needs. This datamaterial, or information, is called raw data.

To be able to analyze the data sensibly, the raw data is processed into «output data«. There are many methods to process the data, but basically the scientist organizes and summarizes the raw data into a more sensible chunk of data. Any type of organized information may be called a «data set«.

Then, researchers may apply different statistical methods to analyze and understand the data better (and more accurately). Depending on the research, the scientist may also want to use statistics descriptively or for exploratory research.

What is great about raw data is that you can go back and check things if you suspect something different is going on than you originally thought. This happens after you have analyzed the meaning of the results.

The raw data can give you ideas for new hypotheses, since you get a better view of what is going on. You can also control the variables which might influence the conclusion (e.g. third variables).

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Central tendency and normal distribution

This part of the statistics tutorial will help you understand distribution, central tendency and how it relates to data sets.

Much data from the real world is normal distributed, that is, a frequency curve which has the most frequent number near the middle. This is a reason why researchers very often measure the central tendency in statistical research, such as the mean (arithmetic mean or geometric mean), median or mode.

The central tendency may give a fairly good idea about the nature of the data (mean, median and mode shows the «middle value»), especially when combined with measurements on how the data is distributed. Scientists normally calculate the standard deviation to measure how the data is distributed.

But there are various methods to measure ho data is distributed: variance, standard deviation, standard error of the mean, standard error of the estimate or «range» (which states the extremities in the data).

To create the graph of the normal distribution for something, you'll normally use the arithmetic mean of a «big enough sample» and you will have to calculate the standard deviation.

But, the distribution will not be normal distributed if the distribution is skewed (naturally) or has outliers (often rare outcomes or measurement errors) messing up the data. One example of a distribution which is not normally distributed is the F- distribution, which is skewed to the right.

So, often researchers double check that their results are normally distributed using range, median and mode. If the distribution is not normally distributed, this will influence which statistical test/method to choose for the analysis.

Hypothesis testing

How do we know whether a hypothesis is correct or not? Why use statistics to determine this?

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Using statistics in research involves a lot more than make use of statistical formulas or getting to know statistical software.

Making use of statistics in research basically involves:

–learning basic statistics;

–understanding the relationship between probability and statistics;

–comprehension of inferential statistics;

–knowledge of how statistics relates to the scientific method.

Statistics in research is not just about formulas and calculation. (Many wrong conclusions have been conducted from not understanding basic statistical concepts)

Statistics inference helps us to draw conclusions from samples of a population.

When conducting experiments, a critical part is to test hypotheses against each other. Thus, it is an important part of the statistics tutorial for the scientific method.

Hypothesis testing is conducted by formulating an alternative hypothesis which is tested against the null hypothesis, the common view. The hypotheses are tested statistically against each other.

The researcher can work out a confidence interval, which defines the limits when you will regard a result as supporting the null hypothesis and when the alternative research hypothesis is supported.

This means that not all differences between the experimental group and the control group can be accepted as supporting the alternative hypothesis – the result need to differ significantly statistically for the researcher to accept the alternative hypothesis. This is done using a significance test (another article).

Depending on the hypothesis, you will have to choose between one-tailed and two tailed tests.

Sometimes the control group is replaced with experimental probability – often if the research treats a phenomenon which is ethically problematic, economically too costly or overly time-

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consuming, then the true experimental design is replaced by a quasi-experimental approach.

Often there is a publication bias when the researcher finds the alternative hypothesis correct, rather than having a «null result», concluding that the null hypothesis provides the best explanation.

If applied correctly, statistics can be used to understand cause and effect between research variables.

It may also help identify third variables, although statistics can also be used to manipulate and cover up third variables if the person presenting the numbers does not have honest intentions (or sufficient knowledge) with their results.

Misuse of statistics is a common phenomenon, and will probably continue as long as people have intentions about trying to influence others. Proper statistical treatment of experimental data can thus help avoid unethical use of statistics. Philosophy of statistics involves justifying proper use of statistics and establishing the ethics in statistics.

Here is another great statistics tutorial which integrates statistics and the scientific method.

Reliability and experimental error

Statistical tests make use of data from samples. These results are then generalized to the general population. How can we know that it reflects the correct conclusion?

Contrary to what some might believe, errors in research are an essential part of significance testing. Ironically, the possibility of a research error is what makes the research scientific in the first place. If a hypothesis cannot be falsified (e.g. the hypothesis has circular logic), it is not testable, and thus not scientific, by definition.

If a hypothesis is testable, to be open to the possibility of going wrong. Statistically this opens up the possibility of getting

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experimental errors in your results due to random errors or other problems with the research. Experimental errors may also be broken down into Type-I error and Type-II error. ROC Curves are used to calculate sensitivity between true positives and false positives.

A power analysis of a statistical test can determine how many samples a test will need to have an acceptable p-value in order to reject a false null hypothesis.

The margin of error is related to the confidence interval and the relationship between statistical significance, sample size and expected results. The effect size estimate the strength of the relationship between two variables in a population. It may help determine the sample size needed to generalize the results to the whole population.

Replicating the research of others is also essential to understand if the results of the research were a result which can be generalized or just due to a random «outlier experiment». Replication can help identify both random errors and systematic errors (test validity).

Cronbach's Alpha is used to measure the internal consistency or reliability of a test score.

Replicating the experiment/research ensures the reliability of the results statistically.

What you often see if the results have outliers, is a regression towards the mean, which then makes the result not be statistically different between the experimental and control group.

What should be the sample size?

Determining the sample size to be selected is an important step in any research study. For example let us suppose that some researcher wants to determine prevalence of eye problems in school children and wants to conduct a survey.

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The important question that should be answered in all sample surveys is «How many participants should be chosen for a survey»? However, the answer cannot be given without considering the objectives and circumstances of investigations.

The choosing of sample size depends on non-statistical considerations and statistical considerations. The non-statistical considerations may include availability of resources, manpower, budget, ethics and sampling frame. The statistical considerations will include the desired precision of the estimate of prevalence and the expected prevalence of eye problems in school children.

Following three criteria need to be specified to determine the appropriate samples size:

The Level of Precision

Also called sampling error, the level of precision, is the range in which the true value of the population is estimated to be. This is range is expressed in percentage points. Thus, if a researcher finds that 70% of farmers in the sample have adopted a recommend technology with a precision rate of ±5%, then the researcher can conclude that between 65 and 75% of farmers in the population have adopted the new technology.

The Confidence Level

The confidence interval is the statistical measure of the number of times out of 100 that results can be expected to be within a specified range.

For example, a confidence interval of 90% means that results of an action will probably meet expectations 90% of the time.

The basic idea described in Central Limit Theorem is that when a population is repeatedly sampled, the average value of an attribute obtained is equal to the true population value. In other words, if a confidence interval is 95%, it means 95 out of

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