Management > Managerial Statistics > Stages in the Statistical Survey
A statistical survey is a scientific process of collection and analysis of numeric data. Human population survey is used in government, health, social science, and marketing sectors. The need for new data starts when data do not yet exist or when existing data do not fully satisfy the needs of the person or organization.
Stages of a Statistical survey:
Statistical survey involve two stages, a) planning and b) execution
Planning:
Step-1: Identify Your Objective :
Nature of the problem to be investigated should be clearly defined in an unambiguous manner. There should be clear, precise and unambiguous reasons to collect data. We should be clear about the information we want to learn from the survey, and the actions we want to take once the survey is done and analyzed. If there is no clear objective of the survey then the assignment of the survey may result in wastage of resources.
Step-2: Do the cost Estimation:
Verify an existing statement for the survey. Decide the unit of measurement. Find relations between characteristics and define the scope of the survey. The cost of the survey depends on the scope of the survey and population size. Costing in terms of time required, mailing questionnaires, collecting responses, carrying out interviews should be considered.
Step-3: Plan the Logistics:
The scope of the survey decides areas to be covered and units to be studied. It also specifies the accuracy of the data required. Using these logistics for the survey are finalized. Depending upon the scope the method of data collection (census or sample) is decided. Then the method of approaching the participants (phone, email, in-person interviews, postal mail, etc.), type of data (primary or secondary), the time required for the survey, selection of people for collecting data is finalized.
Step-4: Analyze Your Resources:
It is checked whether the previous survey carried out is sufficient to satisfy the scope of the survey. Resources (both internal and external) should be identified in the light of the scope and proposed logistics for the survey. If internal resources are not sufficient outsourcing should be done and the work is got done by some external agency.
Step-5: Organize the Survey:
Before actual execution of the survey, a chronological and detailed timetable for each step of the project is to be made. Each task in each step should be explained in detail in sequence. This step avoids confusion, repeatability. It also avoids wastage of resources due to lack of accountability.
Step – 6: Training of Personnel:
In this step, the training is given to investigators, enumerators and supervisors as per need of the survey
Execution:
After planning procedure data is collected by execution survey. Control methods are used to check the accuracy, coverage, methods of measurements, analysis and interpretation. Then the collected data is carefully edited, classified, tabulated and presented in form diagrams, charts and graphs. Using this information inference of the survey is obtained.
Terms Used in Statistical Survey:
Units or Individuals:
The objects on which the characteristics are measured is called an individual or unit.
Population or Universe:
The totality of all units or individuals in a survey is called population or universe. If the number of individuals in the population is known it is called finite population and if it is not known, the population is called infinite population.
Example: During the study of employees in the marketing department, all the members of the department form population, while individual member forms the unit of population.
Parameter:
The measure of describing the characteristics of the population is called as a parameter.
Example: Age of person, the height of a person etc.
Sample:
When the population is very huge, it is not possible to study each and every individual of the population, then the sampling method is used. A subset or part of the population is called sample.
Example: There is 50 sales personnel in the sales department, then predetermined numbers (say 5) selected out 50 is called the sample.
Statistics:
The measure that describes characteristics of the sample is called as statistics.
Quantitative Characteristics:
A characteristic which is numerically measurable is called quantitative characteristics.
Examples: Age of managers, Height of persons, No. of children in a home.
Qualitative Characteristics:
A characteristic which is not numerically measurable is called qualitative characteristics. It is a description of attributes or properties that an object possesses.
Example: Beuty, intelligence, smoothness, durability.
Variable:
In a population, some characteristics remain the same for all units and some other varies from unit to unit. The qualitative characteristic that varies from unit to unit is called a variable. It is measurable characteristics.
Example: age, height, income. etc.
Attribute:
The qualitative characteristic that varies from unit to unit is called attribute. It is non-measurable characteristics.
Example: Intelligence, beauty, nationality, religion, etc.
Collection of Primary Data:
Primary Data:
- Data collected for the first time keeping in a view the objective of the survey is called primary data.
- It is collected by personal interview, questionnaire, through telephones and mails. It is collected by census method or sampling method.
Advantages of Primary Data:
- It is collected with specific goals and purpose which cuts out the possibility of wasting of resources.
- It is reliable because the researcher can replicate the procedure to check the results, due to the knowledge of procedures of data collection and analyzation.
- It is up-to-date- data since it is collected fresh.
- This data is collected from the population in question, hence can be considered as authentic and thus bias of third parties can be avoided.
- The course of the survey can be changed anytime as per the new requirement of the survey.
Disadvantages of Primary Data:
- It is the costliest method of data collection.
- It is a time-consuming method.
- This method requires a large sample space for the credibility of the data obtained.
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