Coding: Meaning and essentials

The process of identifying and classifying each answer with a numerical score or other character symbol. The numerical score or symbol is called a code, and serves as a rule for interpreting, classifying, and recording data.  Identifying responses with codes is necessary if data is to be processed by computer.

Coded data is often stored electronically in the form of a data matrix – a rectangular arrangement of the data into rows (representing cases) and columns (representing variables) The data matrix is organized into fields, records, and files:

Field: A collection of characters that represents a single type of data.

Record: A collection of related fields, i.e., fields related to the same case (or respondent).

File: A collection of related records, i.e. records related to the same sample.

Tabular Representation of Data

Presentation of data is of utter importance nowadays. After all everything that’s pleasing to our eyes never fails to grab our attention. Presentation of data refers to an exhibition or putting up data in an attractive and useful manner such that it can be easily interpreted.

Tabular Representation

A table facilitates representation of even large amounts of data in an attractive, easy to read and organized manner. The data is organized in rows and columns. This is one of the most widely used forms of presentation of data since data tables are easy to construct and read.

Components of Data Tables

  • Table Number: Each table should have a specific table number for ease of access and locating. This number can be readily mentioned anywhere which serves as a reference and leads us directly to the data mentioned in that particular table.
  • Title: A table must contain a title that clearly tells the readers about the data it contains, time period of study, place of study and the nature of classification of data.
  • Headnotes: A headnote further aids in the purpose of a title and displays more information about the table. Generally, headnotes present the units of data in brackets at the end of a table title.
  • Stubs: These are titles of the rows in a table. Thus a stub display information about the data contained in a particular row.
  • Caption: A caption is the title of a column in the data table. In fact, it is a counterpart if a stub and indicates the information contained in a column.
  • Body or field: The body of a table is the content of a table in its entirety. Each item in a body is known as a ‘cell’.
  • Footnotes: Footnotes are rarely used. In effect, they supplement the title of a table if required.
  • Source: When using data obtained from a secondary source, this source has to be mentioned below the footnote.

Construction of Data Tables

There are many ways for construction of a good table. However, some basic ideas are:

  • The title should be in accordance with the objective of study: The title of a table should provide a quick insight into the table.
  • Comparison: If there might arise a need to compare any two rows or columns then these might be kept close to each other.
  • Alternative location of stubs: If the rows in a data table are lengthy, then the stubs can be placed on the right-hand side of the table.
  • Headings: Headings should be written in a singular form. For example, ‘good’ must be used instead of ‘goods’.
  • Footnote: A footnote should be given only if needed.
  • Size of columns: Size of columns must be uniform and symmetrical.
  • Use of abbreviations: Headings and sub-headings should be free of abbreviations.
  • Units: There should be a clear specification of units above the columns.

The Advantages of Tabular Representation

  • Ease of representation: A large amount of data can be easily confined in a data table. Evidently, it is the simplest form of data presentation.
  • Ease of analysis: Data tables are frequently used for statistical analysis like calculation of central tendency, dispersion etc.
  • Helps in comparison: In a data table, the rows and columns which are required to be compared can be placed next to each other. To point out, this facilitates comparison as it becomes easy to compare each value.
  • Economical: Construction of a data table is fairly easy and presents the data in a manner which is really easy on the eyes of a reader. Moreover, it saves time as well as space.

Processing of Data: Editing field and office editing

Data editing is defined as the process involving the review and adjustment of collected survey data. Data editing helps define guidelines that will reduce potential bias and ensure consistent estimates leading to a clear analysis of the data set by correct inconsistent data using the methods later in this article. The purpose is to control the quality of the collected data. Data editing can be performed manually, with the assistance of a computer or a combination of both.

Data analysis is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, while being used in different business, science, and social science domains. In today’s business, data analysis is playing a role in making decisions more scientific and helping the business achieve effective operation.

EDITING is the process of checking and adjusting responses in the completed questionnaires for omissions, legibility, and consistency and readying them for coding and storage.

Purpose of Editing

Purpose of Editing For consistency between and among responses. For completeness in responses– to reduce effects of item non-response. To better utilize questions answered out of order. To facilitate the coding process.

Basic Principles of Editing

  1. Checking of the no. of Schedules / Questionnaire)
  2. Completeness (Completed in filling of questions)
  3. Legibility.
  4. To avoid Inconstancies in answers.
  5. To Maintain Degree of Uniformity.
  6. To Eliminate Irrelevant Responses.

Types of Editing

  1. Field Editing

Preliminary editing by a field supervisor on the same day as the interview to catch technical omissions, check legibility of handwriting, and clarify responses that are logically or conceptually inconsistent.

Field editing is the preliminary editing of data by a field supervisor 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 are present from interviews, a call-back should be made rather than guessing what the respondent “would have probably said.”

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. In central or in-house editing, all the questionnaires undergo thorough editing. It is a rigorous job performed by central office staff.

  1. Office Editing

Editing performed by a central office staff; often done more rigorously than field editing.

Interactive editing

The term interactive editing is commonly used for modern computer-assisted manual editing. Most interactive data editing tools applied at National Statistical Institutes (NSIs) allow one to check the specified edits during or after data entry, and if necessary, to correct erroneous data immediately. Several approaches can be followed to correct erroneous data:

  • Re-contact the respondent
  • Compare the respondent’s data to his data from the previous year
  • Compare the respondent’s data to data from similar respondents
  • Use the subject matter knowledge of the human editor

Selective editing

Selective editing is an umbrella term for several methods to identify the influential errors, and outliers. Selective editing techniques aim to apply interactive editing to a well-chosen subset of the records, such that the limited time and resources available for interactive editing are allocated to those records where it has the most effect on the quality of the final estimates of published figures. In selective editing, data is split into two streams:

  • The critical stream
  • The non-critical stream

Significance of Processing of Data

Data processing is the conversion of data into usable and desired form. This conversion or “processing” is carried out using a predefined sequence of operations either manually or automatically. Most of the processing is done by using computers and other data processing devices, and thus done automatically. The output or “processed” data can be obtained in various forms. Example of these forms include image, graph, table, vector file, audio, charts or any other desired format. The form obtained depends on the software or method used. When done itself it is referred to as automatic data processing. Data centers are the key component as it enables processing, storage, access, sharing and analysis of data.

Importance of data processing includes increased productivity and profits, better decisions, more accurate and reliable. Further cost reduction, ease in storage, distributing and report making followed by better analysis and presentation are other advantages. The need to process data is now widely realized and reflected in every field of work. Let the work be done in a business atmosphere or for educational research purpose, data management systems are used by every business. It is a multidimensional process which is involved in almost every field of human life. Generally speaking, the term “Data Processing” is used where you have to collect innumerable data files from different sources.

Methods of Data Processing

There are number of methods and types of data processing. Based on the data processing system and the requirement of the project, suitable data processing methods can be used. Generally, Organizations employ computer systems to carry out a series of operations on the data to present, interpret, or to obtain information. The process includes activities like data entry, summary, calculation, storage, etc. A useful and informative output is presented in various appropriate forms such as diagrams, reports, graphics, etc. Data processing is  mainly  important in business and scientific operations. Business data is repeatedly processed, and usually needs large volumes of output. Scientific data requires numerous computations and usually needs fast-generating outputs. Three methods of data processing have been presented below:

Manual Data Processing

Data is processed manually without using any machine or tool to get the required results. In manual data processing, all the calculations and logical operations are performed manually on the data. Similarly, data is transferred manually from one place to another. This method of data processing is very slow, and errors may also occur in the output. Mostly, Data is processed manually in many small business firms as well as government offices & institutions. In an educational institute, for example, marks sheets, fee receipts, and other financial calculations (or transactions) are performed by hand.

This method is avoided as far as possible because of the very high probability of error, labour intensive and very time-consuming. This type of data processing forms the very primitive stage when technology was not available, or it was not affordable. With the advancement of technology, the dependency on manual methods has drastically decreased. This also makes processing expensive and requires large manpower depending on the data required to be processed. Example includes selling of commodity on shop.

Mechanical Data Processing

In this method, data is processed by using different devices like typewriters, mechanical printers or other mechanical devices. This method of data processing is faster and more accurate than manual data processing. These are faster than the manual mode but still forms the early stages of data processing. With invention and evolution of more complex machines with better computing power this type of processing also started fading away. Examination boards and printing press use mechanical data processing devices frequently. Any device which facilitates data processing can be considered under this category. The output from this method is still very limited.

Electronic Data Processing

This is a modern technique to process data. The data is processed through a computer; Data and set of instructions are given to the computer as input, and the computer automatically processes the data according to the given set of instructions. The computer is also known as Electronic Data Processing Machine. Electronic Data Processing is the fastest and best available method with highest reliability and accuracy. Technology used is the latest as this method uses computers. Manpower required is minimal. Processing can be done through various programs and predefined set of rules. Processing of large amount of data with high accuracy is almost impossible which makes it best among the available types of data processing. For example, in a computerized education environment results of students are prepared through a computer; in banks, accounts of customers are maintained (or processed) through computers, etc.

Applications of Data Processing

  • Data Analysis: In a science or engineering field, the terms data processing and information systems are considered too broad, and the more specialized term data analysis is typically used. Data analysis makes use of specialized and highly accurate algorithms and statistical calculations that are less often observed in the typical general business environment.
  • Commercial Data Processing: Commercial data processing involves a large volume of input data, relatively few computational operations, and a large volume of output. For example, an insurance company needs to keep records on tens or hundreds of thousands of policies, print and mail bills, and receive and post payments.
  • Almost all fields: It is impossible to think of any area which is untouched by data processing or its use. Let it be agriculture, manufacturing or service industry, meteorological department, urban planning, transportation systems, banking and educational institutions. It is required at all places with varied level of complexity.
  • Real World Applications: With the implementation of proper security algorithms and protocols, it can be ensured that the inputs and the processed information is safe and stored securely without unauthorized access or changes. With properly processed data, researchers can write scholarly materials and use them for educational purposes. The same can be applied for evaluation of economic and such areas and factors. Healthcare industry retrieves information quickly of information and even save lives. Apart from that, illness details and records of treatment techniques can make it less time-consuming for finding solutions and help in reducing the suffering of the patients.

Types of Data Processing

There are number of methods and techniques which can be adopted for processing of data depending upon the requirements, time availability, software and hardware capability of the technology being used for data processing. There are number of types of data processing methods.

Batch Processing

This is one of the widely used type of data processing which is also known as Serial/Sequential, Tacked/Queued  offline processing. The fundamental of this type of processing is that different jobs of different users are processed in the order received. Once the stacking of jobs is complete they are provided/sent for processing while maintaining the same order. This processing of a large volume of data helps in reducing the processing cost thus making it data processing economical. Batch Processing is a method where the information to be organized is sorted into groups to allow for efficient and sequential processing.

Online Processing is a method that utilizes Internet connections and equipment directly attached to a computer. It is used mainly for information recording and research. Real-Time Processing is a technique that can respond almost immediately to various signals to acquire and process information. Distributed Processing is commonly utilized by remote workstations connected to one big central workstation or server. ATMs are good examples of this data processing method. Examples include: Examination, payroll and billing system.

Real time processing

As the name suggests this method is used for carrying out real-time processing. This is required where the results are displayed immediately or in lowest time possible. The data fed to the software is used almost instantaneously for processing purpose. The nature of processing of this type of data processing requires use of internet connection and data is stored/used online. No lag is expected/acceptable in this type and receiving and processing of transaction is carried out simultaneously. This method is costly than batch processing as the hardware and software capabilities are better. Example includes banking system, tickets booking for flights, trains, movie tickets, rental agencies etc. This technique can respond almost immediately to various signals to acquire and process information. These involve high maintenance and upfront cost attributed to very advanced technology and computing power. Time saved is maximum in this case as the output is seen in real time. For example in banking transactions.

Online Processing

This processing method is a part of automatic processing method. This method at times known as direct or random-access processing. Under this method the job received by the system is processed at same time of receiving. This can be considered and often mixed with real-time processing. This system features random and rapid input of transaction and user defined/ demanded direct access to databases/content when needed. This is a method that utilizes Internet connections and equipment directly attached to a computer. This allows the data to be stored in one place and being used at an altogether different place. Cloud computing can be considered as an example which uses this type of processing. It is used mainly for information recording and research.

Distributed Processing

This method is commonly utilized by remote workstations connected to one big central workstation or server. ATMs are good examples of this data processing method. All the end machines run on a fixed software located at a particular place and make use of exactly same information and sets of instruction.

Multiprocessing

This type of processing perhaps the most widely used types of data processing. It is used almost everywhere and forms the basis of all computing devices relying on processors. Multi-processing makes use of CPUs (more than one CPU). The task or sets of operations are divided between CPUs available simultaneously thus increasing efficiency and throughput. The break down of jobs which needs be performed are sent to different CPUs working parallel within the mainframe. The result and benefit of this type of processing is the reduction in time required and increasing the output. Moreover, CPUs work independently as they are not dependent on other CPU, failure of one CPU does not result in halting the complete process as the other CPUs continue to work. Examples include processing of data and instructions in computer, laptops, mobile phones etc.

Time sharing

Time based used of CPU is the core of this data processing type. The single CPU is used by multiple users. All users share same CPU but the time allocated to all users might differ. The processing takes place at different intervals for different users as per allocated time. Since multiple users can uses this type it is also referred as multi access system. This is done by providing a terminal for their link to main CPU and the time available is calculated by dividing the CPU time between all the available users as scheduled.

Dichotomous, Multiple type Questions in Survey

Dichotomous

The dichotomous question is a question that can have two possible answers. Dichotomous questions are usually used in a survey that asks for a Yes/No, True/False, Fair/Unfair or Agree/Disagree answers. They are used for a clear distinction of qualities, experiences, or respondent’s opinions.

If you want information only about product users, you may want to ask this type of question to “opt-out” those who haven’t bought your products or services. It is important that you ask this type of question if there are only two possible answers. Avoid using a dichotomous question to inquire about feelings and emotions as it is a neutral area where people would prefer to answer “maybe,” or “occasionally”.

Dichotomous questions (Yes/No) may seem simple, but they have few problems both on the part of the survey respondent and in terms of analysis. Yes/No questions often force customers to choose between options that may not be that simple and may lead to a customer deciding on an option that doesn’t truly capture their feelings.

The benefits of dichotomous questions are that they are easy and short. Also, you can simplify the survey experience. Dichotomous questions have the advantage to ease responses and ease the analysis of the data.

Multiple type Questions

Survey questions can use either a closed-ended or open-ended format to collect answers from individuals. And you can use them to gather feedback from a host of different audiences, including your customers, colleagues, prospects, friends, and family.

Multiple choice questions are the most popular survey question type. They allow your respondents to select one or more options from a list of answers that you define. They’re intuitive, easy to use in different ways, help produce easy-to-analyze data, and provide mutually exclusive choices. Because the answer options are fixed, your respondents have an easier survey-taking experience.

Perhaps, most important, you’ll get structured survey responses that produce clean data for analysis.

The most basic variation is the single-answer multiple choice question. Single answer questions use a radio button (circle buttons representing options in a list) format to allow respondents to click only one answer. They work well for binary questions, questions with ratings, or nominal scales.

Advantages of Multiple Choice Questions

  • They are less complicated and less time consuming:

Imagine the pain a respondent goes through while having to type in answers when they can simply answer the questions at the click of a button. Here is where multiple choice lessens the complications.

Many-a-times the survey creator would want to ask straightforward questions to the respondent, the best practice is to provide the choices instead of them coming up with answers, this in-turn saves their valuable time.

  • Responses get a specific structure and are easy to analyz:

Surveys are often developed with respondents in mind, how will they answer the questions? This is where multiple choice gives a specific structure to responses, therefore becomes the best choice.

Let’s say at your workplace you receive a survey asking about the best restaurant, to host the Christmas party. Honestly speaking giving specific options isn’t going to hurt, rather, as a surveyor, you are sure that the answer will be from one of the options given to the respondents.

It will be easier for the surveyor to analyze the data as it will be free from any errors (as respondents won’t be typing in answers) and the surveyor would atleast know that not a random restaurant would be chosen.

  • Helps respondent comprehend how they should answer:

One of the positives of multiple choice options is that they help respondents understand how they should answer. In this manner, the surveyor can choose how generalist or specific the responses need to be.

At all times, the surveyor needs to be careful on the choice of question in order to be able to receive responses that are easy to analyze.

  • They appear to look good on handheld devices:

It is estimated that 1 out of 5 people take surveys on handheld devices like mobile phones or tablets. Considering the fact that there is no mouse or keyboard to use, multiple choice questions make it easier for the respondent to choose as there is no scrolling involved.

Disguised and Undisguised Observation Research

Disguised Observation is a technique employed, often in product testing, where a respondent or groups of respondents are unaware that they are being observed.

Participate observation is characterized as either undisguised or disguised. In undisguised observation, the observed individuals know that the observer is present for the purpose of collecting info about their behavior. This technique is often used to understand the culture and behavior of groups or individuals. In contrast, in disguised observation, the observed individuals do not know that they are being observed. This technique is often used when researchers believe that the individuals under observation may change their behavior as a result of knowing that they were being recorded.

For a great example of disguised research, see the Rosenhan experiment in which several researchers seek admission to twelve different mental hospitals to observe patient-staff interactions and patient diagnosing and releasing procedures. There are several benefits to doing participant observation. Firstly, participant research allows researchers to observe behaviors and situations that are not usually open to scientific observation. Furthermore, participant research allows the observer to have the same experiences as the people under study, which may provide important insights and understandings of individuals or groups.

However, there are also several drawbacks to doing participant observation. Firstly, participant observers may sometimes lose their objectivity as a result of participating in the study. This usually happens when observers begin to identify with the individuals under study, and this threat generally increases as the degree of observer participation increases. Secondly, participant observers may unduly influence the individuals whose behavior they are recording.

This effect is not easily assessed, however, it generally more prominent when the group being observed is small, or if the activities of the participant observer are prominent. Lastly, disguised observation raises some ethical issues regarding obtaining information without respondents’ knowledge.

For example, the observations collected by an observer participating in an internet chat room discussing how racists advocate racial violence may be seen as incriminating evidence collected without the respondents’ knowledge. The dilemma here is of course that if informed consent were obtained from participants, respondents would likely choose not to cooperate.

Experimental: Field, Laboratory

Field

They randomly assign subjects (or other sampling units) to either treatment or control groups in order to test claims of causal relationships. Random assignment helps establish the comparability of the treatment and control group, so that any differences between them that emerge after the treatment has been administered plausibly reflect the influence of the treatment rather than pre-existing differences between the groups. The distinguishing characteristics of field experiments are that they are conducted real-world settings and often unobtrusively. This is in contrast to laboratory experiments, which enforce scientific control by testing a hypothesis in the artificial and highly controlled setting of a laboratory. Field experiments have some contextual differences as well from naturally-occurring experiments and quasi-experiments. While naturally-occurring experiments rely on an external force (e.g. a government, nonprofit, etc.) controlling the randomization treatment assignment and implementation, field experiments require researchers to retain control over randomization and implementation. Quasi-experiments occur when treatments are administered as-if randomly (e.g. U.S. Congressional districts where candidates win with slim-margins, weather patterns, natural disasters, etc.).

Field experiments encompass a broad array of experimental designs, each with varying degrees of generality. Some criteria of generality (e.g. authenticity of treatments, participants, contexts, and outcome measures) refer to the contextual similarities between the subjects in the experimental sample and the rest of the population. They are increasingly used in the social sciences to study the effects of policy-related interventions in domains such as health, education, crime, social welfare, and politics.

Characteristics

Under random assignment, outcomes of field experiments are reflective of the real-world because subjects are assigned to groups based on non-deterministic probabilities. Two other core assumptions underlie the ability of the researcher to collect unbiased potential outcomes: excludability and non-interference. The excludability assumption provides that the only relevant causal agent is through the receipt of the treatment. Asymmetries in assignment, administration or measurement of treatment and control groups violate this assumption.

Limitations

There are limitations of and arguments against using field experiments in place of other research designs (e.g. lab experiments, survey experiments, observational studies, etc.). Given that field experiments necessarily take place in a specific geographic and political setting, there is a concern about extrapolating outcomes to formulate a general theory regarding the population of interest. However, researchers have begun to find strategies to effectively generalize causal effects outside of the sample by comparing the environments of the treated population and external population, accessing information from larger sample size, and accounting and modeling for treatment effects heterogeneity within the sample. Others have used covariate blocking techniques to generalize from field experiment populations to external populations.

Noncompliance issues affecting field experiments (both one-sided and two-sided noncompliance) can occur when subjects who are assigned to a certain group never receive their assigned intervention. Other problems to data collection include attrition (where subjects who are treated do not provide outcome data) which, under certain conditions, will bias the collected data. These problems can lead to imprecise data analysis; however, researchers who use field experiments can use statistical methods in calculating useful information even when these difficulties occur.

Using field experiments can also lead to concerns over interference between subjects. When a treated subject or group affects the outcomes of the nontreated group (through conditions like displacement, communication, contagion etc.), nontreated groups might not have an outcome that is the true untreated outcome. A subset of interference is the spillover effect, which occurs when the treatment of treated groups has an effect on neighboring untreated groups.

Participants are randomly allocated to each independent variable group. An example is Milgram’s experiment on obedience or Loftus and Palmer’s car crash study.

Laboratory

A laboratory experiment is an experiment conducted under highly controlled conditions (not necessarily a laboratory), where accurate measurements are possible.

The researcher decides where the experiment will take place, at what time, with which participants, in what circumstances and using a standardized procedure.

  • Strength: It is easier to replicate (i.e. copy) a laboratory experiment. This is because a standardized procedure is used.
  • Strength: They allow for precise control of extraneous and independent variables. This allows a cause and effect relationship to be established.
  • Limitation: The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e. low ecological validity. This means it would not be possible to generalize the findings to a real life setting.
  • Limitation: Demand characteristics or experimenter effects may bias the results and become confounding variables.

Mechanical observations

Human observation is self-explanatory, using human observers to collect data in the study. Mechanical observation involves using various types of machines to collect the data, which is then interpreted by researchers. With continuing improvements in technology, there are many “mechanical” ways of capturing data in observation studies, however, these new “gadgets” tend to be extremely expensive. The most commonly used and least expensive means of mechanically gathering data in an observation study is a video camera. A video camera offers a much more precise means of collecting data than what can simply be recorded by a human observer.

A number of imaginative methods of mechanical observation and device for making such observations have been developed. One of the most widely known devices of this type is the audiometer, a device used by the A C Nielsen Company to record when radio and television sets are turned on and the stations to which they are tuned. The newest generations of this system uses the Storage Instantaneous Audi-meter. This device automatically stores in electronic memory data on television stations tuned in. Nielsen has a central computer that dials these memories on the telephone twice a day and collects the information from them.

a) Voice pitch meters: measures emotional reactions.

b) Electronic checkout scanners: records purchase behavior.

c) Eye-tracking analysis: while subjects watch the advertisement.

Scaling Techniques: Likert Scale, Semantic Differential Scale

Likert Scale

The Likert scale is a five (or seven) point scale which is used to allow the individual to express how much they agree or disagree with a particular statement.

A Likert scale assumes that the strength/intensity of an attitude is linear, i.e. on a continuum from strongly agree to strongly disagree, and makes the assumption that attitudes can be measured.

Strongly Disagree Disagree Undecided Agree Strongly Agree
1 2 3 4 5

Likert Scales have the advantage that they do not expect a simple yes / no answer from the respondent, but rather allow for degrees of opinion, and even no opinion at all.

Therefore, quantitative data is obtained, which means that the data can be analyzed with relative ease.

Offering anonymity on self-administered questionnaires should further reduce social pressure, and thus may likewise reduce social desirability bias.

Semantic Differential Scale

A semantic differential scale is a survey or questionnaire rating scale that asks people to rate a product, company, brand, or any ‘entity’ within the frames of a multi-point rating option. These survey answering options are grammatically on opposite adjectives at each end. For example, love-hate, satisfied-unsatisfied, and likely to return-unlikely to return with intermediate options in between.

Advantages of semantic differential

  • The semantic differential has outdone the other scales like the Likert scale in vitality, rationality, or authenticity.
  • It has an advantage in terms of language too. There are two polar adjectives for the factor to be measured and a scale connecting both these polar.
  • It is more advantageous than a Likert scale. The researcher declares a statement and expects respondents to either agree or disagree with that.
  • Respondents can express their opinions about the matter in hand more accurately and entirely due to the polar options provided in the semantic differential.
  • In other question types like the Likert scale, respondents have to indicate the level of agreement or disagreement with the mentioned topic. The semantic differential scale offers extremely opposite adjectives on each end of the range. The respondents can precisely explain their feedback that researchers use for making accurate judgments from the survey.

Types

  1. Slider rating scale: Questions that feature a graphical slider give the respondent a more interactive way to answer the semantic differential scale question.
  2. Non-slider rating scale: The non-slider question uses typical radio buttons for a more traditional survey look and feel. Respondents are more used to answering.
  3. Open-ended questions: These questions give the users ample freedom to express their emotions about your organization, products, or services.
  4. Ordering: The ordering questions offer the scope to rate the parameters that the respondents feel are best or worst according to their personal experiences.
  5. Satisfaction rating: The easiest and eye-catchy semantic differential scale questions are the satisfaction rating questions.

Structured and Unstructured Observations Research

Observation may take place in the natural or real life setting or in a laboratory. Observational procedures tend to vary from complete flexibility to the use of pre-coded detailed formal instrument. The observer may himself participate actively in the group he is observing or he may be an observer from outside or his presence may be unknown to the people he is observing.

Structured Observation:

Structured observation consists in a careful definition of categories under which the information is to be recorded, standardization of conditions of observation, and is used mostly in studies designed to provide systematic description or to test causal hypothesis.

The use of structured observational technique presupposes that the investigator knows what aspects of the situation under study are relevant to his research purposes and is in a position therefore to develop a specific plan for making and recording observations before he actually begins the collection of data. Structured observation may be employed in the natural field-setting or a laboratory-setting.

Structured observation, in so far as it is used mainly in studies starting with relatively specific formulation, normally allows for much less freedom of choice with respect to the content of observation than is allowed in unstructured observation. Since the situation and the problem are already explicit, the observer is in a position to set up in advance the categories in terms of which he will analyse the situation.

The categories are clearly defined to provide reliable data on the questions to be asked. Of course, such a definition of categories is the end-product of the researcher’s efforts at trying to solve specific coding problems.

To start with, the researcher may be faced with a large number of categories. It is important that the researcher decides upon an appropriate frame of reference for categorization and trains observers accordingly.

  1. E. Bales has developed a procedural system of categories for recording group interaction. He has proposed 12 standard behavioural categories applicable to a wide range of group situations. Behaviour of any group member is coded in terms of careful definition of each category.

The problem of recording observations during a structured observation. The most commonly used system of recording is one that provides the observer with a number of duplicate sheets containing the list of categories to be coded.

Mechanical recording instruments have been used in some studies. For example, Chapple devised an international chronograph. Helen has developed an audio-introspect meter. Bales and Gerbrands have devised an interactional recorder. All these devices are meant to facilitate recording of observational data according to a specific principle of categorization.

Sound recordings and motion pictures have been used when it is necessary to describe the overall nature of an event or to code certain action of a member in terms of a frame of reference provided by the entire event. Of course, each of these has obvious limitations.

Although such devices as motion pictures, tape-recording and television may be very helpful in affording an overall view of a social event, their use does not by itself solve the problem of gathering data for systematic purposes.

Relevant categories for recording behaviour must be established, time-units decided upon, methods set up for recording as to who initiated an action and who was the target. In sum, if the data are to be useful for research, they must be recorded in terms of such a formal scheme.

This problem is effectively tackled by ensuring some kind of a standardization in the observational instrument. There are, however, some special problems in achieving reliable and valid observations.

These are as follows:

(1) One problem derives from the inadequate definition of the kinds of behaviour that are to be accepted as corresponding to a given concept. For example, if the concept of adjustment was not operationally defined, different observers may be inclined to regard different kinds of behaviour as empirical referents of the concept.

(2) Another factor that may lower the reliability of even a well-trained and skilled observer is the degree of confidence one must have in one’s judgement before marking a given category. For example, observers may assign the same observational items to different categories because they may themselves manifest different tendencies to perceive evidence of a particular behaviour.

(3) The constant error introduced by the observer because of the distortion of his perceptions (for various reasons) is one of the major sources of unreliability.

(4) The load of work can also hamper reliability. The result of overloading is often that the observer cannot record all relevant data and may unwittingly record some aspects rather inadequately, thus, introducing bias.

As was suggested earlier, reliability can be increased by careful training of observers. A well-developed observational procedure can be damaged by differences among different observers or by failure to understand the rules for its use. It is necessary, therefore, that a good period of time be devoted to train the observers.

Such a training entails several phases:

(i) Explanation of purposes and theory in the given study,

(ii) Explanation of categories and the rules for their use,

(iii) Purpose of each category for a theoretic scheme, and

(iv) Practice by observer-trainees, discussion on concrete difficulties and reliability-test of observers.

It should be remembered that all this may not always eliminate the constant bias shared by two or more observers. In such a case, the bias can be minimized by same events.

Lastly, we need to consider the relation of the observer with the observed. The observer must carefully prepare his entry into situation and make sure that all members of the group are willing to accept him. Since usually the observer is conspicuously engaged in recording behaviour, using timing device and other technical aids, it is barely possible to disguise the fact that he is doing research.

Hence, it is all the more important that he obtains the group’s full agreement to the inquiry.

The entry of an observer into the group, however unobtrusive, may introduce a new variable into the situation and this may change the behaviour being observed. For example, in a children’s group, the presence of adult observer may have a great distorting influence.

It is important that some thought is given to ways in which the observer’s presence may influence the outcome of research and to develop the techniques that would reduce this possibility. On the whole, people seem to get used to observers if the behaviour of the observer convinces the subjects that he means no ill.

The participant and the non-participant types of observation. This conceptual typology was introduced to social sciences by Prof. Edward Lindeman. Lindeman was very critical of studies based upon schedules of questions for which the investigator found answers by making inquiries of persons.

Lindeman considered as absurd any attempt to avoid bias by posing questions requiring a simple ‘yes’ or ‘no’ reply in a study dealing not only with the ‘what’ of life but also with the ‘why’ and ‘how’ of life. Lindeman was of the opinion that if one wished to know what the subject was really doing one should watch him and not ask him.

Nels Anderson was a intimate participant in the life of ‘Hobos’, on the road, in lodging houses and in their various activities. The tremendous insight which Anderson developed through such an exercise is amply evidenced in his study entitled ‘The Hobo.’

Participant observation has a reference to the observer sharing to a greater or lesser degree the life of the group he is observing. This sharing may be intermittent but active contacts at close proximity do afford an intimate study of persons.

W.F. Whyte in the course of his study published as ‘The Street Corner Society’ was intimately associated with the various aspects of the activities of members in Cornerville. Paul Cressey in his study entitled ‘Taxi Dance Hall’ employed the technique of participant observation and his investigators became part of the social world of the Taxi Dance Hall to the extent it was possible.

The non-participant observation, in contradistinction, is characterized by a relative lack of participation by the observer in the life of the group that he is observing. In sum, to quote John Madge, “When the heart of the observer is made to beat as the heart of any other member of the group under observation, rather than as that of detached emissary from some distant laboratory, then he has earned the title of participant observer.”

In other words, the participant observation is an attempt to put both observer and observed on the same side by making the observer a member of the group so that he can experience what they experience and work within their frame of reference.

On the contrary, the non-participant observation involves the espousal by the observer of a detached role of the observer and recorder without any attempt on his part to experience through participation that which the observed experience.

Unstructured Observation:

The unstructured observation is diametrically opposed to the structured observation in its ideal-typical formulation. The structured observation is characterized by a careful definition of the units to be observed, information to be recorded, the selection of pertinent data for observation and standardization of conditions of observation.

The unstructured observation represents ideally a contrasting situation in respect of all these.

(a) What should be observed? In highly-structured studies, the well-formulated research-problem or hypotheses clearly point to what data will be most relevant.

But in exploratory studies the observer does not know in advance which aspects of the situation will prove relevant. Since unstructured observation is mostly used as an exploratory technique the observer’s understanding of the situation is likely to change as he goes along.

This, in turn, may call for changes in what he observes. It should be noted that such changes called for in the foci of observation are often desirable. Such shifts in focus according to the exigencies of the situation is a characteristic of unstructured observation.

That is, the unstructured observation is flexible, it allows for changes in focus from time to time if and when reasonable clues or doubts warrant such changes with a view to facilitate taking stock of the new observational items that appear to be pertinent or important at different points in time. The observer is always prepared to draw his clues from unanticipated events in an attitude of alert receptivity.

While no stringent criteria or hard and fast rules can be laid down as to how the observer will go about observing a particular situation it would be helpful, however, to indicate some of the significant aspects that the observer can overlook only at his peril.

(1) The observer should see who the participants are, how many they are and how they are related to one another.

(2) The observer should understand the ‘setting.’ He should know in addition to its overt appearance, the kinds of behaviour it encourages, discourages or prevents and its social characteristics.

(3) The observer should also understand the purpose which has brought the subject-participants together, the nature of the purpose and how the goals of participants are related.

(4) The observer must also understand what the participants do, how, with whom and with what they do it. For example, the observer should know what stimulus initiated the behaviour, what the goal is towards which the behaviour is directed, what are the qualities of the behaviour (duration, intensity, etc.) and what are it consequences?

It should be noted that in a practical situation, it is often not possible to obtain enough clues to allow such a comprehensive description. It may also be that the course of events is too fluid to permit consideration of all dimensions of a social situation or that a certain aspect of an occurrence may be so important as to need the entire attention of the observer.

(b) Recording an observation involves two major considerations, viz:

(i) When should the notes be taken, and

(ii) How the notes should be kept.

The best time for recording is on the spot and during the event. This results in minimizing selective bias and distortions of memory. There are, however, many situations in which note taking on the spot is not feasible because this is likely to affect the naturalness of the situation and create suspicions in minds of the persons being observed.

Constant note taking may also affect the quality of observation, as the observer has to divide his attention between observing and writing. In consequence, during the process, the relevant aspects of the situation may be lost to the eye.

In a situation where on the spot detailed note taking is not possible, the memory of the observer may be too heavily taxed if recording is postponed to the expiry of an observational period. In certain situations, it may also help if the observer retires from an on-going situation for a few minutes every hour to make more detailed notes. It is important that the observer should pen down as soon as possible, after the period of observation, a complete account of everything important in the situation. The facility of recording improves if the observer evolved some kind of indexing system.

(c) Ensuring the accuracy of observation is another important concern of the observer. In situations where for some reasons, immediate recording is not possible, he is likely to find that by the time he sits down to write his observations; his memory does not accurately feed in the relevant details.

In order to check the accuracy and completeness of the record, the observer should, if feasible, compare it with a record made by a tape recording equipment. Of course, this is not always feasible; besides, tape recording captures only the auditory stimuli in the situation.

The next best solution is to have two or more people observe the same event. They can later compare their notes and check bias. This is an excellent way to discover one’s blind spots. Two observations may be qualitatively different; against this, two observers from different backgrounds may be employed to observe the same situation. This is understandably a limited remedy.

It happens quite often that the observer injects an overdose of interpretation into his records. This may adversely affect the validity and reliability of his conclusions. One way out of this is to have two observers record the same vent using the same system. A subsequent comparison, between their records may go some way in detecting the intrusion of interpretation.

The participant observer, by virtue of his typical position, faces formidable difficulties in maintaining baselessness. Such an observer may get involved emotionally with some of the people he is studying. This affects his objectivity.

To gain access to intimate data, the observer may allow himself to be absorbed into particular situation he is studying. But this very factor may make him to accept uncritically the behaviour that he should be trying to explain. This problem can be met mainly by the observer becoming aware of his proneness or tendency to take things for granted. An outsider serving as a check may bring home to the observer his blind spot.

It is also possible to detect blind spots by breaking up or dissecting the perceptual field so the factors that lead it to be seen in a particular way lose much of their force. In other words, by approaching the situation in an analytical way the observer may be able to lessen the distorting influence of certain factors that are likely to lead to bias.

The natural way of seeing the situation is to see the action as one centred around the principal actors. But an inconspicuous person, seemingly very insignificant in the situation, or sometimes even a dead person, may be the real center of the situation (e.g., in ceremonies dealing with the propitiation of the soul of a dead person).

An effective screw to control accuracy in observation and interpretations is for the investigator to establish a sort of relationship with the subjects which makes it possible for him to take them into his confidence about the research.

A participant observer’s situation is likely to create inner conflicts within the investigator. This, in turn, may interfere with objectivity. Should the group being observed be undergoing an emergency of some kind, there is indeed a strong pressure on the observer to become an active participant.

He may have to abandon at least temporarily, his detached position as an observer. But if he does enter into the center of activities of the group, he risks the danger of losing his identity as a scientist. Thus, the participant observer is in a dilemma; resulting either way, in the loss of objectivity.

Rosenfeld suggests that bias arising from inner conflicts may be minimized if one is aware of the conflicts and of the nature of one’s defence.

The final issue relates to the relationship between the observer and the observed. In field observation faulty approach vis-a-vis the subjects may have dire consequences for the inquiry. Since the method is applied in the actual life-sphere of the persons, the observer’s mistakes cannot remain insulated incidents.

The observer must decide before he approaches the potential subjects, whether to reveal the facts that he is a researcher or to enter the situation under some other guise. There are advantages as also disadvantages in both these approaches.

It may for certain reasons seem preferable to make known to the subjects his real role as the researcher. This approach is relatively simple compared to disguised observation. Secondly, it increases substantially one’s opportunity to get information which he would get only very indirectly were he to approach them in disguise.

Thirdly, the open declaration approach does not hold the possibility that his activity will harm any of the people in the situation whereas the disguised observer must consider this possibility seriously.

The obvious disadvantage of a direct approach is that this may make the subjects conscious only to the detriment of naturalness of behaviour the observer wants to observe. The researcher therefore has to weigh carefully the relative gains and losses of these two approaches before employing any one.

Entrance into a community requires a very careful staging. If there are many more than two sides to be approached simultaneously, the issue becomes all the trickier. The observer must be prepared to provide a convincing reason for his presence in the community.

It may sometimes be advisable to let influential persons in the community handle the explanation of the investigator’s work. The observer then must decide upon the degree of his participation in the community, ranging from the bare minimum of answering when addressed, to engaging in some major activity concerning the community life.

Survey: Telephonic Survey, Mail, E-mail, Internet Survey, Social Media and Media Listening

Telephonic Survey

A telephone survey is one of the survey methods used in collecting data either from the general population or from a specific target population. Telephone numbers are utilized by trained interviewers to contact and gather information from possible respondents.

The telephone survey approach is usually utilized when there is a need to collection information via public opinion polling. In other words, phone surveys are ideal for data gathering which takes anyone from the general population as potential.

Advantages of phone-based interviewing

There are several reasons why researchers choose CATI interview methodology over other survey methodologies. Here are just a few:

  • Research can be gathered quickly because phone interviews are immediate and skilled interviewers can complete a lot of surveys in a day of work.
  • Most people have telephones, so you have an ample audience for gathering a representative sample to complete the survey.
  • A telephone interview has a personal touch, so it can lead to valuable brand-building benefits if the interviewer surveys in a professional and skilled way.
  • Telephone interviews can be cost-effective as you can have a higher response rate than web surveys, for example.

Disadvantages of a phone survey

  • Sometimes telephone calls are perceived as telemarketing and thus negatively received by potential respondents. This might influence your response rate.
  • It can be challenging to design an effective phone survey because the questions need to be short and precise for easy comprehension.
  • Timing must be carefully considered. The administrators and supervisors should monitor both the time of the call and the length of the actual interview.

Mail

A mail survey is one in which the postal service, or another mail delivery service, is used to mail the survey materials to sampled survey addresses. What is mailed usually consists of a cover letter, the survey questionnaire, and other materials, such as a postage-paid return envelope, an informational brochure to help legitimize the survey organization, detailed instructions about how to participate in the survey, and/or a noncontingent cash incentive.

Applicability of Mail Surveys

  • The survey’s participants are likely to be concerned or interested in the goals of the research, e.g. improving the quality of the brand.
  • You know or have access to the complete name and home address of the members of your target population.
  • Since it is more challenging to complete a written survey than a verbal survey, your respondents must be able to read and write well. It is also ideal if their educational level is above average.
  • The survey does not have time constraints. Sending and receiving a mail survey can be a month-long process.
  • Instructions in the questionnaire can be easily followed and the questions are simple and can be understood without difficulty.

Advantages of a Mail Survey

  • Administration: For those who will administer and supervise the mail survey, not much of an experience are needed. This type of survey does not oblige the authority to make decisions during high-pressure scenarios. For researchers, they are permitted to curtail sampling errors. They also have the jurisdiction of what the respondents can see on the questionnaire, unlike online surveys where software compatibilities and technical issues are factors on how the survey will be displayed.
  • Convenience: Mail surveys provide convenience to respondents for they can answer the questionnaires at their own pace. Survey participants have the liberty to use as much time needed when answering the survey, which will result to more comprehensive and thorough responses. They can also answer the questionnaire anywhere they want to, as long as they have survey instrument.
  • Honesty: Research shows that participants of a survey give more honest answers compared with other data collection methods. This is because respondents are more comfortable giving their views or opinions through writing.
  • Geographical stratification: A mail survey can specifically target different segments of the population.
  • Cost: Mail surveys need not much of manpower. A man alone can administer the entire survey process. Compared with telephone surveys and face-to-face interviews, the cost in conducting a mail survey is relatively cheaper. This type of survey is optimal of there are large sample size involved. Let us say that the participants are around 40,000. Mailing them is cost-effective than calling them one by one. On estimate, a typical medium-scale mail survey can cost at least $5,000. On the contrary, a telephone survey or a face-to-face interview requires double or triple of your budget for a mail survey.

Disadvantages of a Mail Survey

  • Coverage errors and Response Rates: A mail survey usually generates 3-15% response rate. Having said that, it is not the primary drawback of engaging in this type of survey. The real problem is how to obtain a reliable and complete list of participants from the target population. When failed to do so, this will result to coverage errors. Examples are incomplete mailing lists e.g. excluding members of the family that are temporarily away like college students. Biased results and outdated information are also included in coverage errors.
  • Questionnaire design: Since mail surveys do not offer the opportunity for follow-ups, the questionnaire design can make or break the survey. Questions must be brief, straightforward and accurate.
  • Respondents: Mail surveys are unseemly ineffectual for very young children, disabled or sick persons, to those with language barriers, and marginally literate or illiterate.
  • Administration: Researchers have no control as to whether or not the survey has been completely answered or what will happen to the questionnaire after being mailed.

E-mail

Internet Survey

Over the past decade, the use of online and mobile research methods like online surveys has skyrocketed.  Thanks to technological advances, you can now conduct research for a fraction of the cost and time. This makes collecting data easier than ever and better for everyone.

Advantages

Real-time Access

Respondents’ answers store automatically so you get results at your fingertips in no time. This turns analyzing your results into effortless and immediate action.

Increaed Response Rate

The low cost and overall convenience of online surveys bring in a high response. Respondents get to answer questions on their own schedule at a pace they choose.

Design Flexibility

Surveys can be programmed even if they’re very complex.  Intricate skip patterns and logic can be employed seamlessly.  You can create the layout, questions, and answer choices with no hassle.

Low Cost

Collecting data doesn’t have to break the bank anymore.  There are plenty of websites and platforms that make creating your survey fast and affordable.

Convenience

Respondents answer questions on their own schedule and can even have flexibility with completion time.

Rapid deployment and return times are possible with online surveys that don’t use traditional methods.  And, if you have bad contact information for some respondents, you’ll know it almost immediately.

No Interviewer

Since respondents are not disclosing their answers directly to another person, it is easier for them to open up. Interviewers can also influence responses in some cases.

Disadvantages

No Volunteer

The lack of a trained interviewer to clarify and probe can lead to less reliable data.

Possible Cooperation Problems

Online surveys could be deleted and ignored. People hate feeling poked and if they get annoyed, they just have to click delete.

Limited Sampling and Respondent Availability

Certain populations are less likely to have internet access and to respond to online questionnaires. Drawing samples is harder based on email addresses or website visitations.

Fraud

This is the biggest challenge. If your survey is long and/or confusing you might get fake answers. Since there is less accountability, the chances for people just hitting buttons to finish are high. Check the questions you use carefully.

People often take surveys because they’re promised a reward at the end, resulting in them not accurately contributing to your study.

Social Media and Media Listening

Advantages

Unfiltered opinions: Social listening allows you to be the fly on the wall. With social listening, you can gather consumers’ uninfluenced thoughts and opinions. These thoughts and opinions may be less filtered than what they would share in a survey or interview response, making them more authentic.

Travel back in time: Most social mention tools will store and provide access to data for about 24-30 months. Some tools have the ability to go back even further but at an additional cost. This means that if you start a trending program to compare current conversations to past conversations, you can begin analyzing the information right away and don’t have to wait for multiple data collection periods.

Possibilities within other forms of media: Social listening isn’t limited to text. Images, videos, and emojis often help us better understand what consumers are thinking, saying, and doing better than a more traditional research method would allow. That rich media backed up by commentary text allows us to pick up on key terminology and understand how to communicate back to consumers using their own words.

Disadvantages

No guarantees: The nature of social listening is much different from traditional research, where you ask a question to prompt an answer. There are no guarantees with social listening, and you never know what you will (or will not) find. However, if your area of interest is something included in the broad range of topics discussed online, you should be able to uncover useful information, even if it wasn’t the information you initially anticipated finding.

Social listening insights don’t always stand alone. They often work best as a complement to other information or research. However, social listening can add a unique dimension to traditional research, sometimes uncovering the motivation behind behaviors and shedding light on how to move forward.

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