Unit 4
Data Collection
An advantage of using primary data is that, for the specific purposes of their study, researchers collect information. In essence, the questions asked by the researchers are tailored to elicit the information that will assist them with their research. Researchers, using surveys, interviews and direct observations, collect the data themselves.
For instance, direct observations in the area of workplace health research may involve a researcher watching people at work. The researcher could count and code the number of times she sees practices or behaviours relevant to her interest, such as instances of improper lifting posture or the amount of hostile or disrespectful interactions employees over a period of time engage with customers and clients.
Let's say, to take another example, a research team wants to find out about the experiences of employees in return for work following a work-related injury. Part of the study may involve telephone interviews with workers about how long they have been off work and their experiences with the return-to-work process. The responses of the employees-considered primary data-will provide specific information about the return-to-work process to the researchers; e.g., they may learn about the frequency of job accommodation offers, and the reasons for refusing such offers by some employees.
Direct personal investigation-
In this method the investigator has to collect the information personally from the sources concerned. Here the investigator has to go to the field personally for making enquiries and collects information from the informants or respondents by face-to-face interviews. Its advantages and disadvantages are as follows.
Advantages:
i. The information thus obtained is more reliable and accurate.
Ii. There is direct contact between investigator and respondents.
Iii. It is possible to collect supplementary information.
Iv. The questions can be put in their own language and according to education level.
v. When the respondent is approached personally by the investigator, the response is likely to be more encouraging.
Disadvantages:
i. This method is not suitable if the field of study is too wide.
Ii. It is time consuming, costly and need more manpower.
Iii. It is perfectly subjective. It depends upon the intelligence, skill, tact, insight etc. of the investigator.
Iv. There may be personal prejudice and bias of the investigator.
Indirect oral investigation-
When the sphere of investigation is broader and collection of data by direct contact with informants is not possible, then the indirect oral investigation method is used in such research. Information for investigation under this method is not obtained from those persons who have a direct connection with the problem, but the information is collected by oral inquiries from such persons or parties which are indirectly related to the fact or situation. For example: For information about the income of the workers, the investigator directly asks from the mill owner, and not from workers.
Uses of this Method: This is useful in the following situations:
1. Where the area of investigation is broader.
2. Where it is impossible to directly contact the informant.
3. The concerned person is reluctant to give information or is unable to provide information due to ignorance.
4. There is a possibility of biased behaviour while getting information from related people.
5. When investigation is to be kept secret.
Merits:
- This method is less expensive.
- This method is easy and suitable.
- It is the most suitable method for a wide area.
- Secret information is received by this method.
- Under this method help from experts can be obtained.
Demerits:
1. This method does not give high level of purity in the investigation.
2. There is lack of homogeneity in the data collected.
3. The informants are likely to have biased behaviour.
4. Ignorance is found on the part of informants.
Data reporting is the process of collecting and formatting raw data and translating it into a digestible format to assess the ongoing performance of your organization.
A data report is nothing more than a recorded list of facts and figures. Take the population census, for example. This is a technical document that transmits basic information on how many and what kind of people live in a certain country. It can be displayed in the text, or in a visual format, such as a graph or chart. But it is static information that can be used to assess current conditions.
A company’s data reporting often summarizes financial information such as revenues, accounts receivables, and net profits. This provides a timely record of the financial health of the company, or a segment of your finances, such as sales. A sales director might report on KPIs according to location, stage of the funnel, and close rate, to provide an accurate picture of the total sales pipeline.
Why is data reporting important?
Data provides a path that measures progress in every area of our lives. It informs our professional decisions as well as our day-to-day matters. A data report will tell us where to spend the most time and resources, and what needs more organization or attention.
Accurate data reporting plays an important role in every industry. The use of business intelligence in healthcare can help physicians save lives by providing more effective and efficient patient care. In education, data reports can be used to analyse how attendance records relate to seasonal weather patterns, or how acceptance rates intersect neighbourhood areas.
Schedule is the tool or instrument used to collect data from the respondents while interview is conducted. Schedule contains questions, statements (on which opinions are elicited) and blank spaces/tables for filling up the respondents. The features of schedules are:
- The schedule is presented by the interviewer. The questions are asked and the answers are noted down by him.
- The list of questions is a more formal document, it need not be attractive.
- The schedule can be used in a very narrow sphere of social research.
The main purposes of schedule are threefold:
- To provide a standardized tool for observation or interview in order to attain objectivity,
- To act as memory tickler i.e., the schedule keeps the memory of the interviewer/ observer refreshed and keeps him reminded of the different aspects that are to be particularly observed, and
- To facilitate the work of tabulation and analysis.
Types of Schedules
There are several kinds of schedule.
1.Rating Schedules is a schedule used to obtain opinions, preferences etc, respondents over statements on the phenomenon studied. The schedule consists of positive and negative statements of opinion on the phenomenon.
2.Documents Schedules are used to collect data/information from recorded evidences and/or case histories. Here the blanks, functional issues related blanks and the like to be filled up from records and documents are present.
3.Survey Schedules are like questionnaires.
4.Observation Schedules are schedules used when observational method of data collection is used. These could be structured or unstructured interview schedules are used for collecting data when interview method of communication with the respondents is used.
Essentials of a Good Schedule
A good schedule must have the following features
- Content: Should cover questions or statements relating to all significant aspects of the study.
- Dissectional: Should look into the problem analytically, dissecting every, major and significant components of the problem.
- Context: Should suit the context in which it is applied. Different types of studies need different schedules.
- Criterion: Should use sound logic in classifying respondents-based opinions expressed.
- Construction: Should be constructed in such a way that questions statements progress gradually and in order. Better it is sub-divided into parts, each part deeding with a certain sub topic of the issue studied. For each objective, a separate part may be devoted.
- Language: Should be linguistically superbly designed. Clear and straight forward language be used.
- Reliable: Should be reliable such that same results are obtained whenever the schedule is used when everything else remains same.
- Mechanical Aspects: Paper used, margin space given, spacing, printing, size of letters, etc. should be normal.
- Size: Should not too length nor too short. Should give fair coverage to the topic.
- Qualities to be Avoided: Long, complex, presumptuous, personal, embarrassing, hypothetical issues, morality oriented, upsetting type and necessary questions must be avoided.
To sum up, accurate information and accurate response are the two essential conditions of a good schedule. Accurate communication is affected by proper wording of questions so as to produce desired sense without any ambiguity. Accurate response is said to have been achieved when replies contain the information sought for. The response is achieved by stimulating the respondents to fill the schedule. Besides, the physical structure of the schedule should be attractive; the questions asked or information sought should be adequate and relevant to the enquiry, so that final generalization may be based upon it. The information sought should not only be valid, it should also be capable of being tabulated and if possible being subjected to statistical analysis.
Procedure for Formulating a Schedule
- Study the different aspects of the problem. The problem under study should first of all be split up into various aspects. The determination of these aspects will depend upon clear understanding of the problem under study.
- Sub-divide the problem to get necessary information. Each aspect has again to be broken up into a number of sub-parts. These sub-parts should be quite exhaustive to give a full and complete picture of-the aspect under study.
- Class questions. Care should be taken to see that the questions convey the exact sense. Respondents will be willing to supply information without any hesitation, bias or distortion of facts, if questions are exact and clear.
- Serialization of Questions. In order to obtain well-organised information, it is necessary that the questions should be presented to the respondents in a well-ordered serial. It has been experienced to various field studies that the change in the order of questions affects the answers adversely.
- Testing the validity of schedule. Whatever may be the degree of precaution taken, some slips are based to be left out and these can be located when the schedule is put into a reliability and validity test.
- Division. The schedule be divided into adequate number of divisions. Introductory part, instructional part, issues related parts, etc. are certain parts by which the schedule is divided into parts.
- Appropriate form of questions. Use appropriate forms of questions at appropriate places. Open ended, close ended, pictorial, Yes or No (Questions), multiple choice questions, etc. can be used.
A questionnaire is a list of questions or items used to gather data from respondents about their attitudes, experiences, or opinions. Questionnaires can be used to collect quantitative and/or qualitative information.
Questionnaires are commonly used in market research as well as in the social and health sciences. For example, a company may ask for feedback about a recent customer service experience, or psychology researchers may investigate health risk perceptions using questionnaires.
Use cases for questionnaires
You can use the questionnaire method of data collection for a number of purposes:
- To determine what your market wants related to the product or service you provide (for market analysis)
- To get helpful feedback from customers after a purchase
- To get intel on customer demographics and preferences to use for product (or service) development
- To gauge the effectiveness of your customer service and monitor customer satisfaction
- To determine and inform marketing initiatives
- To improve business processes
Drafting, otherwise known as technical drawing, is the creation of accurate representations of objects, buildings, or houses for technical, architectural, or engineering purposes. Someone who is skilled in drafting is called a draftsman or draftsperson.
In drafting, objects are drawn to scale, and usually comprise a top view, a main view and a side view of the object or building. They are usually very detailed and are often used as blueprints for the construction or assembly of the object. While they are sometimes used for engineering plans, they are more common in architecture.
Someone who works as a drafter is known as a draftsperson, draftsman, building designer, building drafter, or drafting officer.
If you’re looking to renovate, remodel, build a custom home or expand on your home, a draftsperson can create the technical house plans you’ll need to have ready for your building contractor. Even if you have an engineer and existing design, a draftsman is needed to create a blueprint for the construction team to follow.
What’s the difference between a draftsman and an architect?
While both a draftsman and an architect can do similar tasks when it comes to preparing technical drawings with CAD and working on building projects, there are some main points of difference to note.
Firstly, to become qualified an architect must complete an undergraduate degree followed by a Master of Architecture, before completing two years of practical experience. Once this education is complete, they can register for their licence to practice as an architect in their state.
To become a drafter, a TAFE course or other program that teaches CAD is enough to be qualified. It will usually only take about two years to become qualified in comparison to an architect’s education taking seven years.
When it comes to the job, architects usually have more responsibilities and will see a project from start to completion. They have the necessary skills to develop more complex plans and can help with anything from design and documentation to project management and contract administration.
A draftspersons main responsibility is the production of technical designs, and they are not required to obtain a license to perform these tasks.
When would I need to hire a draftsman?
If you’re looking to build or renovate your home or put on an addition and your plans aren’t too intricate, it’s common to hire a draftsperson rather than an architect. It’s also a more affordable option.
Getting a draftsman quote
Do you need drafting services? Yellow Pages has hundreds of listings for draftsmen right across Australia. Get free quotes and compare the results to find the right person for your job. You can also read reviews and ratings for draftsmen in your area.
Be sure to ask them if they offer any work guarantees, whether they adjust plans if any problems arise during construction, if their plans are guaranteed to meet the local building codes and regulations, and whether they will work directly with your builder. You can also ask them about their education and certifications and request references.
Presentation-
A presentation is a form of communication in which the speaker conveys information to the audience. In an organization presentations are used in various scenarios like talking to a group, addressing a meeting, demonstrating or introducing a new product, or briefing a team. It involves presenting a particular subject or issue or new ideas/thoughts to a group of people.
It is considered as the most effective form of communication because of two main reasons:
- Use of non-verbal cues.
- Facilitates instant feedback.
In small presentations, we can make use of a blackboard, graphs, charts and slides as a visual aid. Whereas, in large presentations, we can make use of films, slides, videos, animations or modern computer graphics, as a visual aid.
Factors Affecting Presentation
Audience Analysis
When the speaker has made some background research about the audience in a proper manner, it results in excelling in the presentation. However, when the audience analysis is poor, it will result in an ineffective presentation.
Communication environment
The effectiveness of the presentation is highly influenced by the communication environment. Maximum people of the audience notice the surrounding. These surroundings include the speaker, stage, background, lighting, aeration and so forth. If the arrangements are not proper then it will have an adverse impact on the presentation.
Personal Appearance
How the speaker is appearing to the audience has a great impact. A person who is well dressed up, wearing limited accessories and looking professional, can influence the audience by their communication easily.
Use of Visuals
Visuals are like the cherry on the cake. It makes the presentation more effective. If the presenters use visual aids, then it is supposed to be better prepared. Also, they appear more persuasive, credible and interesting to the audience.
Opening and Closing Presentation
The beginning of the presentation should be interesting enough to retain the attention of the audience. The ending of the presentation should leave a deep impact on the audience.
Organization of Presentation
When the facts and data are organized in a proper manner, clarity is ensured. Further, it makes the message understandable and keeps the enthusiasm of the audience intact. Also, it improves the image of the speaker.
Language and Words
The quality of the presentation also depends on the language and words. The speaker should speak in the language with which the audience is familiar. So, he could choose some catchy words to grab the interest of the audience.
Voice Quality
The presenter’s voice quality also has an influence on the audience. A strong and striking voice can easily draw the attention of the audience.
Body Language
The audience listens to the speaker who maintains eye contact while speaking. It shows the confidence, knowledge, friendliness and experience of the speaker.
Answering Questions
If the speaker is capable of handling the questions of the audience tactfully asked when the presentation ends. It helps in impressing the audience. Then, the presentation is said to be successful.
Several types of secondary data exist. They can include information collected by Statistics Canada from the national population census and other government information. Administrative data is one type of secondary data that is increasingly used. This term refers to information routinely collected as part of an organization, institution or agency's day-to-day operation. There are a number of examples: registrations of motor vehicles, hospital intake and discharge records, records of workers' compensation claims, and more. Secondary data tends to be readily available and inexpensive to obtain in comparison to primary data. Moreover, because the data collection is comprehensive and routine, administrative data tends to have large samples. In addition, administrative information (and many kinds of secondary data) is collected over a long period of time. That makes it possible for researchers to detect changes over time. Returning to the above-mentioned return-to-work study, the researchers could also examine secondary data in addition to the data provided by their primary data (i.e., survey results). To determine the amount of time workers were receiving wage replacement benefits, they could look at workers' compensation lost time claims data.
The researchers may be able to determine which factors predict a shorter absence of work among injured workers with a combination of these two data sources. Such data could then help improve the return of other injured workers to work.
Data collected by someone other than the actual user is secondary data. It means that the data is already available, and it is being analysed by someone. The secondary data includes magazines, journals, books, newspapers, etc. It may be either data published or data unpublished.
Published information is available in multiple resources, including
- Government publications
- Public records
- Historical and statistical documents
- Business documents
- Technical and trade journals
Unpublished data includes
- Diaries
- Letters
- Unpublished biographies, etc.
Observation is a technique that uses vision as its main data collection method. It means the use of the eyes instead of the voice and the ears. Observation is accurate observation and observation of phenomena as they arise with regard to cause and effect or mutual relationships. Observation is observing other people's behaviour as it occurs without controlling it. Therefore, recording data without asking questions is called a method of observation. The following are some examples of the data collection method of observation:
Observing the behaviour of salesmen on sales calls.
- Observing the conduct of clients in advertisements.
- Observing the consumer's response to the display of a specific product.
- Observing the retailers' stocking pattern.
a) Observation-
Systematic viewing or intentional study by the eye is the observational method of data collection. In a natural or simulated situation, the observation may be done. Observation can be done either openly or via hidden cameras. This technique is useful for gathering data that individuals are unwilling or unable to provide. The merits of observation follow:
- It provides information on consumers' actual behaviour. It therefore provides greater precision than other methods.
- Limited chances of bias.
- It is the simplest method and the most non-technical one. A small amount of training can make his observation ideal.
It helps to obtain data that consumers are reluctant or unable to provide. Any rigid position does not bind the researcher. According to the changes in the problem, he can change.
It makes it possible for a researcher to record events as they happen. It is possible through observation to collect more satisfactory and in-depth material. To classify the data, a researcher is in a better position.
Limitations of Observation Method:
- Only certain aspects of consumer behaviour are helpful.
- Due to the nature of certain elements, planned observation is very often not possible. Observation is restricted by the length of events.
- It only provides data on how customers behave and does not provide any information as to why customers behave. It is not possible to quantify observational data.
- There is a probability of bias. Observation gives incorrect results as a result.
- This tool is time-consuming and costly.
b) Experimental
In establishing cause-and-effect relations, these are more efficient. Data collection is performed in an experiment in such a way that relatively unambiguous interpretation is permitted. Experiments determine and prove relationships of cause and effect in the realm of the sciences. These are also used in the New Century's marketing research efforts, although these were also in use during the last century.
The basis of marketing science has become experimentation. In addition, the design of experiments makes it possible to analyse research findings rationally. It also provides a role model against which it is possible to compare other research designs. An experiment is defined as follows by Boyd, Westfall, and Stanch': An experiment is a research process in which one or more variables are manipulated under conditions that allow data to be collected in an unconfused manner, showing the effects, if any, of such variables.' Therefore, the difference between experimental research and non-experimental research, according to this definition, may be a matter of degree rather than kind.
Experiments create artificial conditions in order to obtain the data necessary for the research to be completed. In order to measure the data in the most precise manner or format, such conditions may also be necessary. Because of this phenomenon, situations are invariably created to conduct experiments and respondents may not feel at home while cooperating with researchers during experiments.
Nevertheless, this approach has the benefit that researchers can analyze the actual cause-and-effect relationships between any two variables that are relevant to the study. Other variables are either not present or, to the minimum extent, present.
Thus, the data gathered by the researchers are representatives of the actual relationships of cause and effect between the two variables given. In addition, it is possible to change one of the variables (only in a controlled experimental setting) and to measure the effects of such modifications on the other. Experiments are therefore popular among researchers, particularly those in scientific fields.
Two types of experiments are available: laboratory and field. In laboratories, laboratory experiments are conducted. Test subjects are brought to these laboratories and different tests are administered. A TV commercial or journal advertisement could be shown to them. A small programme prepared by trained artists could also be made for them to witness. Then, either on a recording medium or in writing, the responses of test subjects are measured. This approach is artificial, provides results quickly, is less expensive and requires fewer attempts. In the field where test subjects are normally found or identified, field experiments are conducted. Test subjects are asked questions, TV commercials are shown or some leaflets are made available for reading. It is even possible to tell them to try some products. Their responses are measured in some leaflets that are read. It is even possible to tell them to try some products. Their responses are measured and dutifully recorded in the sport. This approach is reliable, time-consuming, more expensive, and involves more effort. If the vital factors to be considered are not cost and operational problems, field experiments are the ones that the researcher should always choose. In addition, Boyd et al have confirmed that laboratory experiments produce results much similar to those produced by field experiments, but not comparable to those produced by descriptive studied problems can also be studied by non-experimental methods; researchers would have to be careful when using them because their results may not be very useful or reliable.
c) Interview
1. DIRECT PERSONAL INTERVIEWS
The investigator personally meets the individuals concerned and collects from them the information required. This method can prove very expensive and time-consuming if the area to be covered is vast. Nevertheless, this approach is important for certain laboratory experiments or localized queries. Errors are likely to influence the outcomes because of the investigator's personal bias.
2. INDIRECT PERSONAL INTERVIEWS
Whenever direct sources do not exist, we interview third parties or witnesses who have information, or the informants hesitate to respond for some reason or another. The reliance is not placed solely on the evidence of one witness, because some of the informants are likely to intentionally give wrong information.
3. COLLECTION THROUGH QUESTIONNAIRES
In general, the questionnaires are sent by email to inquire about several relevant questions. There is a space for entering the requested data in the questionnaires. The informants are requested within a certain period to return the questionnaires to the investigator. This technique is inexpensive, reasonably expeditious and good for comprehensive inquiries. However, when there is no incentive involved, only a small percentage of recipients respond to questionnaires.
4. COLLECTION THROUGH ENUMERATORS
In this method, the data was collected by trained enumerators. They help the informants correctly create the entries in the schedules or questionnaires. If the enumerator is well trained, experienced, and discreet, through this method, you can get the most reliable information. Enumerator driven approach works best for a large scale governmental or an organizational inquiry. This method cannot be adopted by private individuals or institutions as its casting would be prohibitive for them.
5. COLLECTION THROUGH LOCAL SOURCES
In this method, the agents or local correspondents gather and send the information requested, using their judgment as to the best way to obtain it, but there is no formal data collection. This technique is cheap and expeditious, but it only provides estimates. It may involve the bias of local agents.
Editing the raw data is the usual first step in the analysis. Editing detects mistakes and omissions, corrects them whenever possible and certifies the achievement of minimum data quality standards.
The duty of the editor is to guarantee that information is;
1. Precise, precise,
2. In accordance with the intent of the question or with other information,
3. Entered uniformly,
4. Complete, and
5. Arranged for coding and tabulation simplification.
Data editing can be achieved in two ways: field editing and in-house editing, also called central editing.
Field editing is a field supervisor's preliminary editing of data on the same day as the interview. Its purpose is to identify technical omissions, check legibility, and clarify responses that are logically or conceptually inconsistent.
When gaps from interviews are present, instead of guessing what the respondent "would probably have said," a call-back should be made.
A second important task of the supervisor is to re-interview a few respondents, at least on some pre-selected questions, as a validity check. All the questionnaires undergo thorough editing in central or in-house editing. It is a rigorous job done by employees of the central office.
Sorting is the process of arranging data into meaningful order so that you can analyse it more effectively. For example, you might want to order sales data by calendar month so that you can produce a graph of sales performance. You can use Discoverer to sort data as follows:
- Sort text data into alphabetical order
- Sort numeric data into numerical order
- Group sort data to many levels, for example, you can sort on City within Month within Year
Sorting worksheet data also makes it easier to analyse. For example, you might want to sort sales data from most profitable sales to least profitable sales to show the relative position of your company's bestselling products.
Discoverer offers great flexibility when sorting data within data. You can do this to many different levels. For example, you can sort by City within Region.
Census includes the total process of collecting, compiling, analysing, evaluating, publishing and disseminating statistical data regarding the population and housing and their geographical location. Population characteristics include demographic, social and economic data and are provided as of a particular date (reference period).
Census Methods:
Population censuses typically use one of two approaches:
- De facto – meaning enumeration of individuals as of where they are found in the census, regardless of where they normally reside.
- De jure - meaning enumeration of individuals as of where they usually reside, regardless of where they are on census day.
Sample-
A sample is defined as a smaller set of data that a researcher chooses or selects from a larger population by using a pre-defined selection method. These elements are known as sample points, sampling units, or observations. Creating a sample is an efficient method of conducting research. In most cases, it is impossible or costly and time-consuming to research the whole population. Hence, examining the sample provides insights that the researcher can apply to the entire population.
Universe-
A universe, in statistics, refers to a population comprising the units or informants of data, whether animate or inanimate, relating to a problem under study. In other words, it is the totality of the phenomenon studied or the set of objects of a statistical investigation.
Thus, when secondary data are not available for the problem being studied, the decision can be made to correct the primary data using any appropriate method. The required information can also be obtained using the following methods, namely the census method and the sample method.
For example, if we are going to study the average expenditure made by the students of a certain school that consists of 5,000 students, the universe, in this case, will be the “school” that consists of all the 5,000 students and the unit or the informant, in that case, will be each of these 5,000 students belonging to that school.
Census method refers to the complete enumeration of a universe. A universe may be a place, a group of people or a specific locality through which we collect the data. Census method is necessary in some cases like population census, Agriculture Census, animal census etc for gaining vast knowledge. But in contrary this & method is not applicable as well as needed to some social problems because it is costly and time consuming. It is difficult to study the whole universe because financially aid requires for it to complete the study. For this purpose, we use sampling method to pick up a simple from the whole universe. Census method is perplexed and take more time in data collection.
Suitability of Census Method
The census method is suitable only in the following cases.
- Where the universe is not vast.
- Where there is enough time to collect data.
- Where higher degree of accuracy is required.
- Where there is enough availability of finance.
The census method is most commonly used by the government in connection with the national population, housing census, agriculture census, etc. where the vast knowledge about these fields is required. Whenever the entire population is studied to collect the detailed data about every unit, then the census method is applied.
One of the major advantages of census method is the accuracy as each and every unit of the population is studied before drawing any conclusions of the research. When more and more data are collected the degree of correctness of the information also increases. Also, the results based on this method are less biased.
The census method can be applied in a situation where the separate data for every unit in the population is to be collected, such that the separate actions for each is taken. For example, the preparation of the voter’s list for election purposes, income tax assessment, recruitment of personnel, etc. are some of the areas where the census method is adopted. This method can be used where the population is comprised of heterogeneous items, i.e., different characteristics.
Though the census method provides a complete data of the population under study, it is very costly and time-consuming. Often, this method is dropped down because of these constraints and the sampling method, where certain items representative of the larger group, is selected to draw the conclusions.
When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.
To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. There are two types of sampling methods:
- Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
- Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.
Probability sampling methods
- Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.
- There are four main types of probability sample.
1. Simple random sampling
- In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.
- To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.
- Example: Simple random sampling- You want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.
2. Systematic sampling
- Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.
- Example: Systematic sampling- All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people.
- If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.
3. Stratified sampling
- Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.
- To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender, age range, income bracket, job role).
- Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.
- Example: Stratified sampling -The company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.
4. Cluster sampling
- Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.
- If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling.
- This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.
- Example: Cluster sampling -The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters.
There are five types of sampling: Random, Systematic, Convenience, Cluster, and Stratified.
- Random sampling is analogous to putting everyone's name into a hat and drawing out several names. Each element in the population has an equal chance of occurring. While this is the preferred way of sampling, it is often difficult to do. It requires that a complete list of every element in the population be obtained. Computer generated lists are often used with random sampling. You can generate random numbers using the TI82 calculator.
- Systematic sampling is easier to do than random sampling. In systematic sampling, the list of elements is "counted off". That is, every kth element is taken. This is similar to lining everyone up and numbering off "1,2,3,4; 1,2,3,4; etc". When done numbering, all people numbered 4 would be used.
- Convenience sampling is very easy to do, but it's probably the worst technique to use. In convenience sampling, readily available data is used. That is, the first people the surveyor runs into.
- Cluster sampling is accomplished by dividing the population into groups -- usually geographically. These groups are called clusters or blocks. The clusters are randomly selected, and each element in the selected clusters are used.
- Stratified sampling also divides the population into groups called strata. However, this time it is by some characteristic, not geographically. For instance, the population might be separated into males and females. A sample is taken from each of these strata using either random, systematic, or convenience sampling.
Types of Universes
Finite universe
The finite universe is one in which the number of units of information is defined and limited. For example, the “college” cited above is a finite universe, since in this case the number of units of information, i.e., students, is defined and limited, i.e. 5000.
Infinite Universe
An infinite universe refers to a population in which the number of units that compose it cannot be definitively determined. For example, the population of stars in the sky, the population of temperatures at various points in the atmosphere, the population of heights, weights and ages of people in a country are examples of infinite universe. In addition, if a universe is very large, it is also considered an infinite universe, like the number of leaves of a tree.
Existing universe
An existing universe is one that already exists with all its units in the form of concrete objects. The researcher has nothing to do for its creation except its discovery and location. For example, a college, a university, a library, a country and a state, with their concrete objects, such as students, books and individuals, respectively, are examples of an existing universe. Such a universe, too, can be under a finite universe or under an infinite universe.
Experimental universe
An experimental universe is one that is constituted through the experiments carried out by a researcher and that is no longer in existence. For example, a record made of the number of faces and tails obtained by flipping a coin for a number of times, say 100, 200 or 500 times is a case of experimental universe.
Similarly, a record made of the number of times the number “5” is obtained by rolling a die for a certain number of times is an example of an experimental universe.
In this case, the universe no longer exists, but is created by the researcher himself through his experiments for a number of times. An experimental universe is generally infinite in character, as there is no limit to the number of times experiments can be conducted to record the events of a particular event.
Elementary Units
The attributes that are the object of study are called characteristics and the units that possess them are called elementary units. The set of such units is generally described as population. Thus, all units of any field of research constitute the universe and all elementary units (on the basis of one characteristic or more) constitute the population. Often, we do not find any difference between population and universe, so both terms are considered interchangeable. However, the researcher must necessarily define these terms precisely.
References:
- Research for Marketing Decisions Paul E. Green, Donald S. Tull
- Marketing Research- Text and Cases Harper W. Boyd Jr. , Ralph Westfall.
- Research methodology in Social sciences, O.R.Krishnaswamy, Himalaya Publication
