Subject matter stands for looking into topical issues, themes and domains
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• Now we want to inform about how people influence statistics.
• The key words are subjects and subject matters and subjective.
• Subjective are views that are strongly influenced by the opinion of one person. It is a personal opinion.
• Inter-subjective is an opinion that is shared with many other persons/professionals.
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The nature of subject matter issues
• Subject in the philosophical sense refers to a
(human) being that has subjective experience and consciousness. It is a word for humans, and for many other issues.
• Subject matter, in general, is anything which can be studied , described and analyzed.
• The nature of human(s) is to give meaning to their lives. A subject is expressing its own views by acting, behaving, or expressing opinions. This can also be called the inherent subjectivity of humans.
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More on subject matters
• Subject also refers to an area of study or research. In research we can speak about the subjects that we study. That can be humans or other “subjects of interest”.
• The “subjects” and their behavior are normally the
“objects” of the statistical research.
• Another meaning for the word subject: subjects are those who are ruled by rulers.
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The nature of objects
• Objects are no humans, they are things.
• These are mostly physical things that people (humans) move around.
• To complicate:
– An object can be a topic of study/research.
– Objects, as a topic for research, can become a subject matter for study purposes, when humans start to study it .
– And subjects can be also topics that can be studied.
• To summarize: When humans study a topic it becomes their subject matter issue.
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The human subject’s behavior
• When humans travel they make use of the transport systems.
• Subjects decide the means of transport and how they use it: trains, directions, frequency and distances.
• When we measure the use of transport we look at what people are doing.
• Subject matter refers to a matter presented for consideration in discussion, thought or study.
• Other words for subject matters are topics, themes and domains.
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• Producing statistics is a work done by humans.
Therefore humans are always part of the outcomes of statistics, because they influence the results.
• In other words. The statistics you get are also the result of the humans that report about them and that work on them.
• That fact is influencing the quality of the source of the basic data.
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We speak about subject matter and inter-subjectivity.
• Subject matters:
– When it is about humans (their characteristics)
– About their behaviors (labor, education, health)
– About the things they influence (production, nature)
– About the things they move around (goods, etc.)
• In science we speak about inter-subjectivity when a group of professionals come to the some conclusions.
– The more experts share conclusions the more we may assume that that opinion is correct. But that is not always true. Scientists need to stay alert.
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How to define statistical topics
In the VSS we use a classification for the subject matter themes:
– Social and demographic.
– Economic statistics.
– Environment and Multi-Domain Statistics.
– Methodology.
– Strategy and Managerial issues of Official statistics.
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How to classify subject matter issues
• When we define differences we do this on the basis of criteria.
• Experts can have different opinions about these criteria.
• Social and demographic is the behavior based on the actions of individuals and households.
• Economic behavior is mostly the behavior of enterprise units.
• Environment is the behavior of individuals and households and enterprise units and government that effect the environment.
• Multi-domain issues are a collection of topics that were not considered in one of the three groups above.
• Expert opinions can differ. Some see poverty as a social issue, other might see it as an economic issue. Therefore it is classified as a multi –domain issue.
• Classifications are extremely important, they exist in all themes on all subject matter topics.
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Social statistics
• Social statistics are about individuals or households with regard to their actions, and what and how they do it.
• Time use is the most personal manner of acting.
• Different categories of work time use can be defined: paid and unpaid, formally and informally, unpaid for the own family, unpaid for friends and neighbors, unpaid for organizations.
• Other categories are personal time use, cooking and eating, etc.
• For each topic we need classifications in order to describe that type of behavior.
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Classifications are key in statistics
• Before we make produce statistics we use classifications to categorize the population we want to describe, as well as its behavior.
• For demographics: age and sex are key.
• For education: types of schooling.
• For labor: types of professions.
• In economics the units (firms are classified by type of unit like economic activity and size).
• Other classifications are about products and trade.
• Each topic has its own set of classifications.
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Use of classifications
• Classifications are used to analyze the populations. Therefore a combination of classifications are used.
• Classifications need to be based on some principles and criteria.
• Different classifications can be used to serve different purposes of research. For certain topics a range of classifications is used.
• When classifications need to be related to each other we speak about coordination.
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Types of classifications and VSS.
• Economic activities.
• Products produced and services delivered.
• Products, goods and services traded.
• Types of government spending.
• Types of firms and changes of status of firms.
• Types of peoples and households.
• Types of education.
• Types of jobs.
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Statistical Coordination
• Statistical coordination takes place within and between agencies.
• We have institutional and technical coordination.
• Government work in statistics is enhanced by the possibilities of information technology.
• And by structuring and harmonizing the content, methodologies, dissemination formats and documentation.
• One unit in the system needs to take the leadership role in the aspects of nationwide statistical coordination.
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Institutional coordination
• Institutional coordination is needed to make sure that the various statistical units in own country are able to work together.
• This is reinforced by creating arrangements like stats councils, working groups and committees.
• The range of topics that need to be coordinated can even include international affairs. In that case a separate unit should be involved to coordinate these relations. Also in-country training needs coordination.
• Other form of coordination is the regional coordination between neighboring countries. (South-South)
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Technical coordination
• Technical coordination is needed to make sure that the various products of the statistical system can be related to each other when needed.
• Technical coordination can best be addressed by subject matter working groups or other technical working groups.
• These groups can discuss the national and international standards that these units need to adhere to.
• They deal with concepts, methods, classification and technology.
• Especially around the National Accounts it is needed that all data that flows into the system is well coordinated and harmonized.
• Harmonization is making data fit to be compared and related to with other data.
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Harmonized statistics
• Harmonization is the adjustment of differences and inconsistencies among different measurements , methods , procedures , schedules , specifications , or systems to make them uniform or mutually compatible.
• In statistics we adjust data sets to deal with inconsistencies for instance based on the use of different definitions or different units.
• In statistics we make data sets comparable so that they can relate to each other for accounting purposes.
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System definitions
• (1) A set of detailed methods , procedures , and routines established or formulated to carry out a specific activity , perform a duty , or solve a problem .
• (2) An organized , purposeful structure regarded as a whole and consisting of interrelated and interdependent elements
( components , entities , factors , members , parts etc.). These elements continually influence one another (directly or indirectly) to maintain their activity and the existence of the system, in order to achieve the goal of the system.
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Statistical system
• An organization of the relations between a well defined set of entities.
• An institutional statistical system is defined by the units that belong to the system and the relations that they have among each other.
• A conceptual system. All units that are described by the statistical and the relations that are defined between them.
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Ethics in statistics
• Ethical behavior is human behavior per excellence.
• Statistics are based on trust. Trust is about believing that statements that are made are correct within the limits that exist.
• In statistics there are two kinds of trust. First, the trust with the providers of raw data that this information only will be used for statistical purposes.
• Second, the trust with the users that they can believe the correctness of the results, based on methods used and the meta data.
• Statistics is the treatment of data sets that may present only a portion of the population that it describes. The essence of statistics is making statements based on limited observations.
• Only image, skills and transparency can make that people trust that type of work.
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Promoting trust with data providers
• Statistical organizations need to have legal frameworks that stipulate how these organizations have to behave when asking for data from data providers and using that data.
• Data needs to be treated in a very confidential way.
• Staff need to be faced with sanctions when there is a breach of these confidentiality rules.
• Confidentiality rules also apply to publication of the data. The data has to be anonymized.
• Individuals and firms should not be able to be identified as such in micro data sets and in tables.
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• Products of statistical offices need to be trusted by the users, in order to be used.
• The trust should first of all be in the staff of the organizations.
• Staff should be skilled and trained to apply the methods that are expected to be used.
• Staff should demonstrate in their work that they master these skills.
In articles and publications.
• Staff should produce meta data which informs users about the key features of the data presented and the methods applied.
• Staff needs to be transparent in their work to a high degree.
• Lack of transparency, of skill and of image will lead to a lack of trust an a lack of funding.
• Lack of trust may harm statistics.
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• Trust is based on the assumption that there is a truth that we can know and we should look for.
• This trust in the existence of truth is universal.
• When there is no truth possible there is no need to make statistics. Or we have biased statistics by definition.
• There is a truth in the natural sciences and in social sciences.
• We can learn more about this truth by applying the right methods. Methods learn us to approach the truth.
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• The truth only can be reached if we assume that all information collected is the correct information.
• Since humans provide information and can make errors and misreport for various reasons, we can only approach the truth.
• By definition in social statistics we can never be completely sure that we reach the absolute truth.
• The aim of the methods we apply is to come closer to the truth, knowing that we never can be certain.
• The methods we use have there own uncertainties.
Sampling has sampling errors and confidence intervals.
Sampling also has non sampling errors, because people err.
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• Even if we are not certain to reach the absolute truth, we can create evidence that makes it probable that we are approaching the truth.
• When we have a concept and we know that a feature exist we can look for the appropriate number.
• If no other methods exist people can make guesstimates.
• With methods we create evidence based information.
• When methods are correctly applied we create statistical facts.
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• Facts are statements to be believed unless proven that these statements are wrong.
• Statistical statements are more like strong evidence than hard facts.
• That is why we speak of “evidence based”.
• Pure statistical statements can by definition be wrong.
• But most statistics should be considered to be the best possible approach towards the truth.
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