SOME BASIC CONCEPTS
Statistics may be defined as the body of techniques used to facilitate the collection, organization,
presentation, analysis, and interpretation of data to make decisions.
The core ideas include.
(1) Techniques for collecting data
(2) Techniques for organizing and presenting data
(3) Techniques for analyzing data
(4) Interpreting results from analysis for informed decisions.
TYPES OF STATISTICS
Statistics can be classified into two main branches: descriptive and inferential.
DESCRIPTIVE STATISTICS
Descriptive statistics are used to summarize or describe data in meaningful and useful ways.
Descriptive statistics include statistics (or numbers), tables, charts, and graphs. It is important to note
that the purpose of descriptive statistics stops at summarizing and describing data. It does not include
any attempts to make inferences beyond summarizing and describing.
INFERENTIAL STATISTICS
Inferential statistics are methods that employ probability theory for deducing and making predictions
from the data that has been obtained.
POPULATION AND SAMPLE
Population refer to the number of people or items under study. We could learn about why students are
late for school by asking all the students in a school for their reason. Considering all the students for the
data collection means you are considering the entire population.
However, there are times when considering all the students will be burdensome, so a few of
them are selected to represent the whole student body. When a portion of the population is considered
in research to represent the entire population, that portion is called a sample.
The main difference between a population and a sample is that a population includes all of the elements
from a set of data whiles a sample consists of one or more observations from the population.
TYPES OF VARIABLES
A variable refers to an item of data that is liable to vary or change. For example, the age of students, the number of
siblings, the color of cars, etc. There are two major types of variables.
QUANTITATIVE VARIABLES
These are variables that can be quantified by either measuring or counting. They are numeric in nature.
Examples include age, temperature, weight, height, etc. A quantitative variable can be continuous or
discrete variables are variables whose values are obtained by counting. For example, the number of students in
a class. Continuous variables refer to variables that are measured. They take any values on a
continuous scale. Examples of continuous variables include weight, height, age, temperature, color, and
volume.
QUALITATIVE VARIABLES
Qualitative variables cannot be measured or counted. They are therefore non-numeric and can only be
described or classified according to the characteristics or attributes they possess. Examples of
qualitative random variables include color, sex, rank in the army, marital status, and grade.
MEASUREMENT SCALES
Data can further be classified according to the measurement scale on which they fall -nominal, ordinal, interval, and ratio.
NOMINAL SCALE
The nominal scale is the most limited level of measurement. It applies to qualitative data only. On the
nominal scale, no order is required. For example, sex is nominal; so, we can list its categories as male
(followed by) female or female (followed by) male. Similarly, marital status is nominal. This is because
there is no unique order in which its categories married, single, divorced, widowed, etc. are listed. On
the nominal scale, categories are mutually exclusive and exhaustive. Categories are said to be mutually
exclusive if an individual or item can belong to one and only one category. In other words, once an
individual or item has been included in one category, it must be excluded from any other category.
Categories are said to be exhaustive if an individual or item can belong to at least one of them.
ORDINAL SCALE
The next higher scale of measurement is the ordinal scale which also applies to qualitative data only. On
the ordinal scale, order is necessary; meaning that one category is lower than the other or vice versa.
For example, Grades are ordinal, as excellent is higher than very good, which in turn is higher than good,
and so on interval and ratio.
NOMINAL SCALE
The nominal scale is the most limited level of measurement. It applies to qualitative data only. On the
nominal scale, no order is required. For example, sex is nominal; so, we can list its categories as: male
(followed by) female or female (followed by) male. Similarly, marital status is nominal. This is because
there is no unique order in which its categories married, single, divorced, widowed, etc. are listed. On
the nominal scale, categories are mutually exclusive and exhaustive. Categories are said to be mutually
exclusive if an individual or item can belong to one and only one category. In other words, once an
individual or item has been included in one category, it must be excluded from any other category.
Categories are said to be exhaustive if an individual or item can belong to at least one of them.
ORDINAL SCALE
The next higher scale of measurement is the ordinal scale which also applies to qualitative data only. On
the ordinal scale, the order is necessary; meaning that one category is lower than the other or vice versa.
For example, Grades are ordinal, as excellent is higher than very good, which in turn is higher than good,
and so on It is important to note, however, that differences between categories cannot be determined or are
meaningless. If 4 denotes excellent, 3 denotes very good, 2 denotes good and 1 denotes fair; it does not
mean that an employee who is rated excellent is twice as competent as an employee who is rated good,
just because excellent is denoted by 4 and good is denoted by 2. As with the categories in nominal data,
categories in ordinal data are mutually exclusive and exhaustive.
INTERVAL SCALE
The interval scale is the next higher scale of measurement and applies to quantitative data only. The
interval scale has all the properties of the ordinal scale, with the additional property that there is a
meaningful difference between categories. There is no natural zero starting point. An example of a
variable on an interval scale is temperature.
RATIO SCALE
The ratio scale is the highest scale of measurement. It applies to quantitative and has all the properties
of an interval scale. In addition, the ratio scale has a meaningful zero starting point and a meaningful ratio
between two numbers. An example is weight. A weighing scale that reads 0 kg, for example, gives an
indication that there is absolutely no weight on it; and so, the zero starting point is meaningful.
SOURCES OF STATISTICAL DATA
Sources of data can be classified into two main groups depending on their origin. These two main groups
are primary sources and secondary sources. Data that are derived from primary sources are called
primary data and those obtained from secondary sources are called secondary data. In this session, you
will learn about primary and secondary sources of data.
PRIMARY SOURCES OF DATA
Primary data are data originally generated by an investigator or researcher for use by himself or herself.
The choice of method for collecting primary data is largely influenced by the nature of the problem at hand and the availability of money and time.
ADVANTAGES OF PRIMARY DATA
1. Targeted issues are addressed.
2. Data interpretation is better
3. Efficient spending for information
4. Greater control
DISADVANTAGES OF PRIMARY DATA
1. High cost
2. Time-consuming
3. Inaccurate feedback
4. More resources are required
SECONDARY SOURCES OF DATA
Secondary data are data that have already been gathered or published by someone else before and for
a purpose other than the current project. Ordinarily, secondary data is faster to collect and less
expensive compared with primary data. Secondary data are available in libraries, government agencies' internet, and so on.
ADVANTAGES OF SECONDARY DATA
1. Less expensive
2. Easily accessible
3. Immediately available.
4. Targeted issues are addressed.
DISADVANTAGES OF SECONDARY DATA
1. Not specific to the researcher's needs.
2. Problems with incomplete information.
3. Can adversely affect the quality of research.
METHODS FOR COLLECTING DATA
Methods for collecting data refer to the various ways data can be gathered. They include.
DIRECT OBSERVATION
This is a method of data collection where the researcher monitors the parameter, whose data is being
collected. Direct observation has been used by some sociologists, particularly in areas where interviews and the use of questionnaires are not suitable.
SURVEYS
In surveys, the investigator or researcher's task is to find a way to obtain information from individuals
who are often referred to as respondents. A survey conducted on an entire population is called a census
or complete enumeration; whilst those conducted on samples are called sample surveys. Surveys
involve mainly the use of questionnaires and/or interviews to obtain the required information from
respondents. In using the interview method, an investigator personally asks for the required information
and obtains verbal responses to his/her questions. A questionnaire may consist of questions or
statements or a mixture of the two which the respondents have to answer. Questionnaires are
particularly useful when the respondents must remain anonymous. This is because they can be
administered in ways that the respondents can feel confident that their identities are not known. A
questionnaire is, therefore, very important and by far the most popular method for collecting data.
EXPERIMENTS
Primary data may also be generated by researchers through experiments. Experimental research is
concerned with cause-and-effect relationships. An experiment can be conducted in laboratory or in a
field setting.
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