aiou course code 8614 -1 assignment autumn 2022
Course: Educational Statistics (8614)
Semester: Autumn, 2022
ASSIGNMENT No. 1
Q.1 Why is Statistics important for a teacher or researcher? (20)
The first reason is to be able to effectively conduct research. Without the use of statistics it would be very difficult to make decisions based on the data collected from a research project. For example, in the study cited in Chapter One, is the difference in recorded absenteeism between psychiatric and obstetrics nurses large enough to conclude that there is meaningful difference in absenteeism between the two units? There are two possibilities: The first possibility is that the difference between the two groups is a result of chance factors. In reality, the two jobs have approximately the same amount of absenteeism. The second possibility is that there is a real difference between the two units with the psychiatric unit being more nurses missing work. Without statistics we have no way of making an educated decision between the two possibilities. Statistics, however, provides us with a tool to make an educated decision. We will be able to decide which of the two possibilities is more likely to be true. We will base this decision on our knowledge of probability and inferential statistics. A second point about research should be made. It is extremely important for a researcher to know what statistics they want to use before they collect their data. Otherwise data might be collected that is uninterpretable. Unfortunately, when this happens it results in a loss of data, time, and money. Now many a student may by saying to themselves: “But I never plan on doing any research.” While you may never plan to be involved in research, it may find its way into your life. Certainly, it you decide to continue your education and work on a masters or doctoral degree, involvement in research will result from that decision. Secondly, more and more work places are conducting internal research or are becoming part of broader research studies. Thus, you may find yourself assigned to one of these studies. Finally, many classes on the undergraduate level may require you to conduct research (for example, a research methods or experimental psychology course). In each of these instances, a knowledge of measurements and statistics will be invaluable.
Q.2 Discuss different types of data. Also elaborate differences between primary and secondary data. (20)
Types of Data
In research, different methods are used to collect data, all of which fall into two categories, i.e. primary data and secondary data. It is a common classification based upon who collected the data.
1 Primary data :
As the name suggests, is one which is collected for the first time by the researcher himself. Primary data is originated by the researcher for the first time for addressing his research problem. It is also known as first hand raw data. The data can be collected using various methods like survey, observations, physical testing, mailed questionnaire, questionnaire filled and sent by enumerators, personal interviews, telephonic interviews, focus groups discussion, case studies, etc.
2 Secondary data :
Point towards the second hand information already collected and recorded by any other person with a purpose not relating to current research problem. It is readily available form of data and saves time and cast of the researcher. But as the data is gathered for the purpose other than the problem under investigation, so the usefulness of the data may be limited in a number of ways like relevance and accuracy. Also, the objectives and methods adopted to collect data may not be suitable to the current situation. Therefore, the researcher should be careful when using secondary data. Examples of secondary data are censuses data, publications, internal records of the organizations, reports, books, journal articles, websites etc.
Q.3 Explain ‘pictogram’ as a technique to explore/explain data. (20)
Pictograms: A pictogram is a graphical symbol that conveys its meaning through its pictorial resemblance to a physical object. A pictogram may include a symbol plus graphic elements such as border, back pattern, or color that is intended to covey specific information s. we can also say that a pictogram is a kind of graph that uses pictures instead of bars to represent data under analysis. A pictogram is also called “pictograph”, or simply “picto”.
A pictogram or pictograph represents the frequency of data as pictures of symbols. Each picture or symbols may represent one or more units of data.
Pictograms form a part of our daily lives. They are used in transport, medication, education, computers etc. they indicate, in iconic form, places, directions, actions or constraints on actions in either the real world (a road, a town, etc) or in virtual world (computer, internet etc.).
To successfully convey the meaning, a pictogram:
Q.4 Pie Chart is a common way to depict data. Discuss its usage and drawbacks.
Pie Chart
A pie chart displays data in an easy pie-slice format with varying sizes. The size of a slice tells how much data exists in one element. The bigger the slice, the more of that particular data was gathered and vice versa. Pie charts are mainly used to show comparison among various segments of data. When items are presented on a pie chart, it is easy to see which item has maximum frequency and which is not or which item is the most popular and which is not. The main purpose of using a pie chart is to show partwhole relationship. These charts are used for displaying data that are classified into nominal or ordinal categories.
How to Read a Pie Chart?
It is easy to read and interpret a pie-chart. Usually, a pie-chart has several bits of data, and each is pictured on a pie-chart as a pie slice. Some data have larger slices than others. So it is easy to decide which data have maximum frequency and which have minimum.
When to Use the Pie Chart?
There are some simple criteria that can be used to determine whether a pie chart is right choice or not for a given data.
Q.5 What do you understand by ‘measure of dispersion’? Also briefly discuss some common measures of dispersion. (20)
Measures of Dispersion :
Measures of central tendency focus on what is an average or in the middle of the distribution of scores. Often the information provided by these measures does not give us clear picture of the data and we need something more. It means that knowing the mean, median, and mode of a distribution does allow us to differentiate between two or more than two distributions; and we need additional information about the distribution. This additional information is provided by a series of measures which are commonly known as measures of dispersion.
There is dispersion when there is dissimilarity among the data values. The greater the dissimilarity, the greater the degree of dispersion will be.
Measures of dispersion are needed for four basic purposes.
- i) To determine the reliability of an
- ii) To serve as a basis for the control of the variability.
iii) To compare two or more series with regard to their variability.
Iv) To facilitate the use if other statistical measures.
Measure of dispersion enables us to compare two or more series with regards to their variability. It is also looked as a means of determining uniformity or consistency. A high degree would mean little consistency or uniformity whereas low degree of variation would mean greater uniformity or consistency among the data set. Commonly used measures of dispersion are range, quartile deviation, mean deviation, variance, and standard deviation.
Range:
The range is the simplest measure of spread and is the difference between the highest and lowest scores in a data set. In other words we can say that range is the distance between largest score and the smallest score in the distribution. We can calculate range as: Range = Highest value of the data – Lowest value of the data
For example, if lowest and highest marks scored in a test are 22 and 95 respectively, then Range = 95 – 22 = 73
The range is the easiest measure of dispersion, and is useful when you wish to evaluate whole of a dataset. But it is not considered a good measure of dispersion as it does not utilize the other information related to the spread. The outliers, either extreme low or extreme high value, can considerably affect the range. 
