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How do people interpret Information? Explain.

How do people interpret Information? Explain.
When we ask two groups why each has a different way of interpreting written material in common, we are presuming groups with similar drifts of experience internally and variant externally (in reference to the other group), and thus as much as compel an answer based on such experiences shared versus not shared.

But when we take a given individual as against another individual at random, we are looking, logically, to more merely than assumed shared/not-shared backgrounds. Just as individuals can be expected to vary in their reactions to psychoactive pharmaceuticals, we can reasonably ask whether biological factors are ever at work when presenting the same written material at which variant interpretations result. While experience and background can be assumed to play into such differences, of course, we still can ask, and should ask, to what extent biological differences at an individual level -- or shared biological differences within groups -- can account for differences.

One way to observe such phenomena relies on observations of games or other activities that unwittingly restrict the ways in which we bring information to a given input. Thus the exercise of presenting a Rorschach figure (seemingly meaningless blotches of an abstract character) can suggest to a clinician characteristics not only of background but of psychic dispositions to interact in certain ways. Anthropologists of a certain school of though once used these tests to help determine what were considered "modal" or generalizable traits of a groups of people. Psychiatrists use them still to determine tendencies of mechanisms that we utilize to interpret our world and in particular our cultures and personal relationships to it.

From the vantage of biology, the primary factor that is most relevant to such methods is a primitive pair of mechanisms that remained vital to evolving organisms. Stendhal (On Love) was a brilliant Frenchman who tried to explain love and infatuation in terms of what he called "crystallization", by which he meant what a theorist in behavior will describe as interactions between "projections" and "identifications".

Every psychologist knows these concepts and applies them similarly, if more broadly, than Stendhal. What most psychologists do not do, however, is place these two curious biological interlocutors in still broader biological contexts that would give far more meaning and relevance to how they really work for us, by which I mean in ways that can also account for the very phenomenon that you, David, inquire of.

Now a theorist of behavior (moi) will suggest some parallels whose application can take on generalizable features that make their utility expand greatly. Thus "identification" is a process that brings to awareness an EXPOSURE, that is, a state of mind recognizing a stimulus as relevant to it in some manner that will typically be interpreted in terms of safe/dangerous, good/bad, and the like. Whereas the "projection", says such theorists, describes a naturally occurring reaction of the brain-mind to the identification. Thus when we "project" upon someone we are reacting to an exposure that has generated a linkage of putative meaning to someone, the same someone who is now interpreted in regard of that projection.

And these projections (both representing the originating identifications or, in turn offering themselves as grist for a secondary identification, and then a secondary projection, and so on) largely account for how and why experiences, both shared and not shared, influence our interpretations of common stimuli.

Without actually intending to, researchers have to all intents and purposes shown that what the theorist calls "identifications" tend to markedly predominate in the right hemisphere (regardless language placement), and projections in the left. And because we know that projections and identifications lead to one another, we can assume the commisural tracts are the anatomical means of delivering them hither and yon, leading us to ask where the assimilated results are given "meaning".

You would think such curiosities would generate interest if only because the projections associated with mental illness are but exaggerated examples of the same processes, and so the drugs responding should be evaluated to see if their primary influence is hemispherically relevant. But of course that is a bit of an aside from the question.

But because there are many generalized and specific factors that activate these processes, and because drift potentials of these processes posses memory capacity and reflect motivational calibrations, we are led to further appreciate how universal, how omnipresent, are these two modalities that so influence our thoughts and tendencies. Most all of these are subconscious, so that when we observe the influence of these processes it is not uncommon that the triggering phenomena will be precisely such as David has inquired after.

I hope that was a clear enough explanation and that it wasn't overly verbose. But I am confident that it in fact addresses much of the content of your question.

Data Collection and Interpretation
Data interpretation is part of daily life for most people. Interpretation is the process of making sense of numerical data that has been collected, analyzed, and presented. People interpret data when they turn on the television and hear the news anchor reporting on a poll, when they read advertisements claiming that one product is better than another, or when they choose grocery store items that claim they are more effective than other leading brands.
A common method of assessing numerical data is known as statistical analysis , and the activity of analyzing and interpreting data in order to make predictions is known as inferential statistics . Informed consumers recognize the importance of judging the reasonableness of data interpretations and predictions by considering sources of bias such as sampling procedures or misleading questions, margins of error , confidence intervals , and incomplete interpretations.
Why Is Accurate Data Collection Important?
The repercussions of inaccurate or improperly interpreted data are wide-ranging. For example, every 10 years a major census is done in the United States. The results are used to help determine the number of congressional seats that are assigned to each district; where new roads will be built; where new schools and libraries are needed; where new nursing homes, hospitals, and day care centers will be located; where new parks and recreational centers will be built; and the sizes of police and fire departments.
In the past 30 years there has been a major shift in the U.S. population. People have migrated from the northern states toward the southern states, and the result has been a major shift in congressional representation. With a net change of nearly 30 percent (a 17 percent drop in the Northeast and Midwest coupled with a 12 percent gain in the South), the South has gone from a position of less influence to one of greater influence in Congress as a result of population-basedreapportionment . This is just one of many possible examples that reveal how data gathering and interpretation related to population can have a marked affect on the whole country.
Gathering Reliable Data
The process of data interpretation begins by gathering data. Because it is often difficult, or even impossible, to look at all the data (for example, to
poll every high school student in the United States), data are generally obtained from a smaller unit, a subset of the population known as a sample . Then data from the sample are used to predict (or infer) what the characteristics of the population as a whole may be. For example, a telephone survey of one thousand car owners in the United States might be conducted to predict the popularity of various cars among all U.S. car owners. The one thousand U.S. car owners who are surveyed are the sample and all car owners in the United States are the population.
But there both an art and science to collecting high-quality data. Several key elements must be considered: bias, sample size, question design, margin of error, and interpretation.
Avoiding Bias. In order for data interpretation to be reliable, a number of factors must be in place. First and perhaps foremost, an unbiased sample must be used. In other words, every person (or item) in the population should have an equal chance of being in the sample.
For example, what if only Ford owners were surveyed in the telephone survey? The survey would be quite likely to show that Fords were more popular. A biased sample is likely to skew the data, thus making data interpretation unreliable. If we want to know what sorts of cars are preferred by U.S. car owners, we need to be sure that our sample of car owners is representative of the entire car owner population.
One way of ensuring an unbiased sample is to choose randomly from the population. However, it is often difficult to design a study that will produce a truly unbiased sample. For example, suppose a surveyor decides to choose car owners at random to participate in a phone interview about car preferences. This may sound like a good plan, but car owners who do not have telephones or whose telephone numbers are unavailable will not have a chance to participate in the survey. Maybe car owners with unlisted telephone numbers have very different car preferences than the broader population, but we will never know if they are not included in the sample.
Biased sampling continues to challenge census takers. In 1990, nearly 35 percent of the households that were mailed census forms did not mail them back. If a form is not returned, the Census Bureau must send someone to the person's house. Even with census takers visiting homes door to door, the Census Bureau was still unable to contact one out of every five of the families who did not return their census form.
Although this may not sound like a lot, consider that in 1990 there were approximately 250 million people in the United States. If a household contains an average of four people, that means that there were 62.5 million forms mailed out. Multiplying that figure by 35 percent (the number of households that did not return the forms) gives the staggering figure of 21.875 million forms that were not returned. Of the 21.875 million households that did not return forms, census takers were unable to track down 20 percent, or 4.375 million households.
Why is this biased sampling? It is believed that of the more than 4 million households not counted, the overwhelming majority was from poorer sections of large cities. This implies that certain parts of the country may be over-represented in Congress and are the recipients of more federal funds than may be deserved.
Achieving a Large Enough Sample. A second important factor in data collection is whether the chosen sample is large enough. Are one thousand car owners a sufficient number of car owners from which to infer the opinion of all car owners? In order to answer this question, the margin of error needs to be calculated.
The margin of error is a statistic that represents a range in which the surveyor feels confident that the population as a whole will fall. A sufficient sample size needs to have a small margin of error, usually around 5 percent. To determine the margin of error (m ), divide one by the square root of the sample size (s ): . Therefore, the sample of one thousand car owners gives us a margin of error of about 3 percent, an allowable margin of error.
Asking the Proper Questions. Informed citizens who are assessing survey results must consider the type of questions that are asked when a survey is conducted. Were the questions leading? Were they easy or difficult to understand? For example, suppose a study carried out by a local ice cream manufacturer states that 75 percent of Americans prefer ice cream. It seems self-evident that an ice cream company would not report a study that showed Americans do not like ice cream. So perhaps the question in the study was leading: for example, "Do you prefer ice cream or spinach?" It is therefore important to find out exactly what questions were asked and of whom.
Giving a Proper Interpretation. Data are often interpreted with a bias, and the results can therefore be misleading or incomplete. For example, a bath soap company claims that its soap is 99 percent pure. This statement is misleading because the soap manufacturer does not explain what "pure" is. When reading an unclarified percentage such as in the previous example, one needs to ask such questions. An example of another incomplete or misleading interpretation is that the average child watches approximately 5 hours of television a day. The reader should question what an "average child" is.
Considering Margin of Error. Margin of error is important to consider when statistics are reported. For example, we might read that the high school dropout rate declined from 18 percent to 16 percent with a margin of error of 3 percent. Because the 2-percentage point decline is smaller than the margin of error (3 percent), the new dropout rate may fall between 13 percent to 19 percent. We cannot be entirely sure that the high school dropout rate actually declined at all.
Confidence intervals, a term usually employed by statisticians, and related to margins of error, is reported by a percentage and is constructed to relay how confident one can be that the sample is representative of the population. The producers of this survey may only be 95 percent confident that their sample is representative of the population. If this is the case then there is a 5 percent chance that this sample data does not typify or carry over to the population of the United States. The margin of error represents the range of this 95-percent confidence interval (the range that represents plus or minus two standard deviations from the mean ).
Understanding and Interpreting Data
Figuring out what data means is just as important as collecting it. Even if the data collection process is sound, data can be misinterpreted. When interpreting data, the data user must not only attempt to discern the differences between causality and coincidence, but also must consider all possible factors that may have led to a result.
After considering the design of a survey, consumers should look at the reported data interpretation. Suppose a report states that 52 percent of all Americans prefer Chevrolet to other car manufacturers. The surveyors want you to think that more than half of all Americans prefer Chevrolet, but is this really the case? Perhaps not all those surveyed were Americans. Also, the 52 percent comes from the sample, so it is important to ask if the sample was large enough, unbiased, and randomly chosen. One also needs to be aware of margins of error and confidence intervals. If the margin of error for this survey is 5 percent than this means that the percentage of car owners in the United States who prefer Chevrolet could actually be between 47 and 57 percent (5 percent higher or lower than the 52 percent).
Similar questions are important to consider when we try to understand polls . During the 2000 presidential race, the evening news and newspapers were often filled with poll reports. For example, one poll stated 51 percent of Americans preferred George W. Bush, 46 percent preferred Al Gore, and 3 percent were undecided, with a margin of error of plus or minus 5 percent.
The news anchor then went on to report that most Americans prefer George W. Bush. However, given the data outlined above, this conclusion is questionable. Because the difference between George W. Bush and Al Gore is the same as the margin of error, it is impossible to know which candidate was actually preferred. In addition, if we do not know any of the circumstances behind the poll, we should be skeptical about its findings.
As another example, consider census data that shows a radical increase in the number of people living in Florida and Arizona along with a decrease in the number of people living in New York. One could easily (and falsely) conclude that the data "proves" that people are finding New York to be a less desirable place to live and therefore are moving away.
But this hasty conclusion could be missing the big picture. What if the data also reveals that the average age of New Yorkers has dropped since 1990? Further interpretation of the data may reveal that when New Yorkers grow older, they move to warmer climates to retire. This illustrates why data must be thoroughly interpreted before any conclusions can be drawn.
A Data Checklist. When reading any survey, listening to an advertisement, or hearing about poll results, informed consumers should ask questions about the soundness of the data interpretation. A recap of key points follows.
1.   Was the sample unbiased (representative of the whole population)?
2.   Was the sample large enough for the purpose of the survey (margin of error of the sample)?
3.   What type of questions did the surveyor ask? Were they simple and unambiguous? Were they leading (constructed in such a way to get the desired response)?
4.   Can the conclusions drawn be justified based on the information gathered?
5.   How was the survey done (mail, phone, interview)? Does the survey report mention margins of error or confidence intervals, and, if so, are these such that the conclusions drawn are warranted?
By using these checkpoints and learning to think critically about data collection and interpretation, individuals can become more savvy consumers of information.

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Leo Lingham


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