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 interview: Questionnaire Designing

The design of a questionnaire will depend on whether the researcher wishes to collect exploratory information (i.e. qualitative information for the purposes of better understanding or the generation of hypotheses on a subject) or quantitative information (to test specific hypotheses that have previously been generated).

Exploratory questionnaires: If the data to be collected is qualitative or is not to be statistically evaluated, it may be that no formal questionnaire is needed. For example, in interviewing the female head of the household to find out how decisions are made within the family when purchasing breakfast foodstuffs, a formal questionnaire may restrict the discussion and prevent a full exploration of the woman’s views and processes. Instead, one might prepare a brief guide, listing perhaps ten major open-ended questions, with appropriate probes/prompts listed under each.

Formal standardised questionnaires: If the researcher is looking to test and quantify hypotheses and the data is to be analysed statistically, a formal standardised questionnaire is designed. Such questionnaires are generally characterised by:

  • Prescribed wording and order of questions, to ensure that each respondent receives the same stimuli
  • Prescribed definitions or explanations for each question, to ensure interviewers handle questions consistently and can answer respondents’ requests for clarification if they occur
  • Prescribed response format, to enable rapid completion of the questionnaire during the interviewing process.

Given the same task and the same hypotheses, six different people will probably come up with six different questionnaires that differ widely in their choice of questions, line of questioning, use of open-ended questions and length. There are no hard-and-fast rules about how to design a questionnaire, but there are a number of points that can be borne in mind:

  1. A well-designed questionnaire should meet the research objectives. This may seem obvious, but many research surveys omit important aspects due to inadequate preparatory work, and do not adequately probe particular issues due to poor understanding. To a certain degree some of this is inevitable. Every survey is bound to leave some questions unanswered and provide a need for further research but the objective of good questionnaire design is to ‘minimise’ these problems.
  2. It should obtain the most complete and accurate information possible. The questionnaire designer needs to ensure that respondents fully understand the questions and are not likely to refuse to answer, lie to the interviewer or try to conceal their attitudes. A good questionnaire is organised and worded to encourage respondents to provide accurate, unbiased and complete information.
  3. A well-designed questionnaire should make it easy for respondents to give the necessary information and for the interviewer to record the answer, and it should be arranged so that sound analysis and interpretation are possible.
  4. It would keep the interview brief and to the point and be so arranged that the respondent(s) remain interested throughout the interview.

Preliminary decisions in questionnaire design

There are nine steps involved in the development of a questionnaire:

  1. Decide the information required.
  2. Define the target respondents.
  3. Choose the method(s) of reaching your target respondents.
  4. Decide on question content.
  5. Develop the question wording.
  6. Put questions into a meaningful order and format.
  7. Check the length of the questionnaire.
  8. Pre-test the questionnaire.
  9. Develop the final survey form.

Deciding on the information required

It should be noted that one does not start by writing questions. The first step is to decide ‘what are the things one needs to know from the respondent in order to meet the survey’s objectives?’ These, as has been indicated in the opening chapter of this textbook, should appear in the research brief and the research proposal.

One may already have an idea about the kind of information to be collected, but additional help can be obtained from secondary data, previous rapid rural appraisals and exploratory research. In respect of secondary data, the researcher should be aware of what work has been done on the same or similar problems in the past, what factors have not yet been examined, and how the present survey questionnaire can build on what has already been discovered. Further, a small number of preliminary informal interviews with target respondents will give a glimpse of reality that may help clarify ideas about what information is required.

Define the target respondents

At the outset, the researcher must define the population about which he/she wishes to generalise from the sample data to be collected. For example, in marketing research, researchers often have to decide whether they should cover only existing users of the generic product type or whether to also include non-users. Secondly, researchers have to draw up a sampling frame. Thirdly, in designing the questionnaire we must take into account factors such as the age, education, etc. of the target respondents.

Choose the methods of reaching target respondents

It may seem strange to be suggesting that the method of reaching the intended respondents should constitute part of the questionnaire design process. However, a moment’s reflection is sufficient to conclude that the method of contact will influence not only the questions the researcher is able to ask but the phrasing of those questions. The main methods available in survey research are:

  • Personal interviews
  • Group or focus interviews
  • Mailed questionnaires
  • Telephone interviews.

Within this region the first two mentioned are used much more extensively than the second pair. However, each has its advantages and disadvantages. A general rule is that the more sensitive or personal the information, the more personal the form of data collection should be.

Decide on question content

Researchers must always be prepared to ask, “Is this question really needed?” The temptation to include questions without critically evaluating their contribution towards the achievement of the research objectives, as they are specified in the research proposal, is surprisingly strong. No question should be included unless the data it gives rise to is directly of use in testing one or more of the hypotheses established during the research design.

There are only two occasions when seemingly “redundant” questions might be included:

  • Opening questions that are easy to answer and which are not perceived as being “threatening”, and/or are perceived as being interesting, can greatly assist in gaining the respondent’s involvement in the survey and help to establish a rapport.

This, however, should not be an approach that should be overly used. It is almost always the case that questions which are of use in testing hypotheses can also serve the same functions.

  • “Dummy” questions can disguise the purpose of the survey and/or the sponsorship of a study. For example, if a manufacturer wanted to find out whether its distributors were giving the consumers or end-users of its products a reasonable level of service, the researcher would want to disguise the fact that the distributors’ service level was being investigated. If he/she did not, then rumours would abound that there was something wrong with the distributor.

Develop the question wording

Survey questions can be classified into three forms, i.e. closed, open-ended and open response-option questions. So far only the first of these, i.e. closed questions has been discussed. This type of questioning has a number of important advantages;

  • It provides the respondent with an easy method of indicating his answer – he does not have to think about how to articulate his answer.
  • It ‘prompts’ the respondent so that the respondent has to rely less on memory in answering a question.
  • Responses can be easily classified, making analysis very straightforward.
  • It permits the respondent to specify the answer categories most suitable for their purposes.

Putting questions into a meaningful order and format

Opening questions: Opening questions should be easy to answer and not in any way threatening to THE respondents. The first question is crucial because it is the respondent’s first exposure to the interview and sets the tone for the nature of the task to be performed. If they find the first question difficult to understand, or beyond their knowledge and experience, or embarrassing in some way, they are likely to break off immediately. If, on the other hand, they find the opening question easy and pleasant to answer, they are encouraged to continue.

Question flow: Questions should flow in some kind of psychological order, so that one leads easily and naturally to the next. Questions on one subject, or one particular aspect of a subject, should be grouped together. Respondents may feel it disconcerting to keep shifting from one topic to another, or to be asked to return to some subject they thought they gave their opinions about earlier.

Question variety:. Respondents become bored quickly and restless when asked similar questions for half an hour or so. It usually improves response, therefore, to vary the respondent’s task from time to time. An open-ended question here and there (even if it is not analysed) may provide much-needed relief from a long series of questions in which respondents have been forced to limit their replies to pre-coded categories. Questions involving showing cards/pictures to respondents can help vary the pace and increase interest.

Closing questions

It is natural for a respondent to become increasingly indifferent to the questionnaire as it nears the end. Because of impatience or fatigue, he may give careless answers to the later questions. Those questions, therefore, that are of special importance should, if possible, be included in the earlier part of the questionnaire. Potentially sensitive questions should be left to the end, to avoid respondents cutting off the interview before important information is collected.

In developing the questionnaire the researcher should pay particular attention to the presentation and layout of the interview form itself. The interviewer’s task needs to be made as straight-forward as possible.

  • Questions should be clearly worded and response options clearly identified.
  • Prescribed definitions and explanations should be provided. This ensures that the questions are handled consistently by all interviewers and that during the interview process the interviewer can answer/clarify respondents’ queries.

Ample writing space should be allowed to record open-ended answers, and to cater for differences in handwriting between interviewers.

Physical appearance of the questionnaire

The physical appearance of a questionnaire can have a significant effect upon both the quantity and quality of marketing data obtained. The quantity of data is a function of the response rate. Ill-designed questionnaires can give an impression of complexity, medium and too big a time commitment. Data quality can also be affected by the physical appearance of the questionnaire with unnecessarily confusing layouts making it more difficult for interviewers, or respondents in the case of self-completion questionnaires, to complete this task accurately. Attention to just a few basic details can have a disproportionately advantageous impact on the data obtained through a questionnaire.

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.

Types of question: Structured/Close-end, Unstructured/open-end

Structured questions take many forms and include:

  • Single response with nominal or ordinal categories (e.g. From the following list please select the category which includes your household income)
  • Multiple responses (e.g. From the following list of pizza toppings please any or all that you regularly use)
  • Scaled questions (e.g. The President is doing a good job: Strongly Agree to Strongly Disagree), and
  • Numerous variations on these primary types.

Unstructured questions are a bit more qualitative in feel. They do not require pre-defined categories and they allow the respondent to express their views openly. This is their blessing and their curse. Open-ended questions, as they are also known, produce a higher cognitive load in the sense that the respondent has to think harder to come to an answer. This can create a lower response rate and sometimes lesser quality data. On the other hand, they can produce rich insights that provide depth and color to the black and white of structured questions.

Open-ended questions require additional time on the part of the researcher to analyze and code the responses although text-mining software is making this easier. For best results on a survey, keep open-ended questions to a minimum and use them as sub-questions driven by critical responses to a structured question. For example, if someone selects a high or low response to the Net Promoter Score, you can follow up with unstructured questions asking the respondent to elaborate on their score.

Open-ended questions

Open-ended questions are exploratory in nature, and offer the researchers rich, qualitative data. In essence, they provide the researcher with an opportunity to gain insight on all the opinions on a topic they are not familiar with. However, being qualitative in nature makes these types of questions lack the statistical significance needed for conclusive research.

Nevertheless, open-ended questions are incredibly useful in several different ways:

  • Expert interviews
  • Small population studies
  • Preliminary research
  • A respondent outlet
  1. Expert interviews

Since questions that are open-ended ask for the critical thinking and uncut opinion of the respondent, they are perfect for gaining information from specialists in a field that the researcher is less qualified in. Example: If I wanted to learn the history of Ancient China (something I know very little about), I could create my survey for a selected group of historians whose focus is Ancient China. My survey would then be filled with broad open-ended questions that are designed to receive large amounts of content and provide the freedom for the expert to demonstrate their knowledge.

  1. Small population studies

Open-ended questions can be useful for surveys that are targeting a small group of people because there is no need for complex statistical analysis and the qualitative nature of the questions will give you more valuable input from each respondent. The rule here is the group must be small enough for the surveyor to be able to read each unique response and reflect on the information provided. Example: A supervisor who is looking for performance feedback from his/her team of six employees. The supervisor would benefit more from questions that allow the respondents to freely answer rather than forcing them into closed-ended questions that will limit their responses.

  1. Preliminary research

As stated in the closed-ended questions section, conclusive research usually requires preliminary research to be conducted in order to design the appropriate research objects, survey structure and questions. Open-ended questions can reveal to the surveyor a variety of opinions and behaviours among the population that they never realized. It is therefore, incredibly useful to use open-ended questions to gain information for further quantitative research.

  1. A respondent outlet

It is usually a good idea in any survey, no matter how large, to leave an open-ended comments question at the end. This is especially in the case of a survey asking closed-ended questions on attitudes, opinions, or behaviours. Forcing respondents to answer closed-ended questions asks them to fit in your box of options and can leave them with extra information or concerns that they want to share with you. Providing respondents with the outlet of a comment box is showing them the respect they deserve for taking the time to fill out your survey.

There are a few drawbacks to open-ended questions as well. Though respondent answers are almost always richer in quality, the amount of effort it takes to digest the information provided can sometimes be overwhelming. That is why open-ended questions work best in studies with smaller populations. Furthermore, if your survey sample is a fraction of the population you are studying, you will be looking to find data which can be inferred on the overall population as statistically significant. Unfortunately, open-ended questions cannot be used in this manner, as each response should be seen as a unique opinion.

Closed-ended questions

Closed-ended questions come in a multitude of forms, including: multiple choice, drop down, checkboxes, and ranking questions. Each question type doesn’t allow the respondent to provide unique or unanticipated answers, but rather, choose from a list of pre-selected options. It’s like being offered spaghetti or hamburgers for dinner, instead of being asked “What would you like for dinner?”

Use closed-ended questions for the following:

  • When your audience isn’t particularly interested in your survey topic
  • When you need quantifiable data
  • To categorize respondents
  1. When your audience isn’t particularly interested in your survey topic

Closed-ended questions are easier to complete than open-ended questions. Why? Because closed-ended questions lay out all of the possible answers, removing respondents’ task of coming up with their own responses.

So, when you find yourself surveying an audience who may not be excited about what you’re asking them, air on the side of using closed-ended questions. It’ll give them an easier survey-taking experience and, in the process, provide you with a higher completion rate.

  1. When you need quantifiable data

If you’re looking for statistically significant stats, closed-ended questions are the way to go. Going back to our earlier example, using a closed-ended question can help us arrive at stats like: 70% of respondents want to eat spaghetti for dinner versus 30% who prefer hamburgers.

Questions that are closed-ended are conclusive in nature as they are designed to create data that is easily quantifiable. The fact that questions of this type are easy to code makes them particularly useful when trying to prove the statistical significance of a survey’s results. Furthermore, the information gained by closed-ended questions allows researchers to categorize respondents into groups based on the options they have selected.

  1. To categorize respondents

In other words, they allow you to conduct demographic studies. Why is this valuable?

Imagine that the manager of a designer clothing store believes that certain types of people are more likely to visit their store and purchase their clothing than others. To decipher which segment groups are most likely to be their customers, the manager could design a survey for anyone who has been a visitor. This survey could include closed-ended questions on gender, age, employment status, and any other demographic information they’d like to know. Then, it would be followed by questions on how often they visit the store and the amount of money they spend annually. Since all the questions are closed-ended, the store manager could easily quantify the responses and determine the profile of their typical customer. In this case, the manager may learn that her most frequent customers are female students, ages 18-25. This knowledge would allow her to move forward with an action plan on how to cater to this niche better or break into other target demographics.

The major drawback to closed-ended questions is that a researcher must already have a clear understanding of the topic of his/her questions and how they tie into the overall research problem before they are created. Without this, closed-ended questions will lead to insufficient options for respondents to select from, questions that do not properly reflect the research’s purpose, and limited or erroneous information.

For example, if I asked the question, “do you get to work by driving, busing, or walking?” I would have accidentally omitted carpooling, biking, cartwheeling or any other form of transportation I am unaware of. Instead, it would have been better for me to ask the open-ended question of “how do you get to work?” to learn all the different types of answer before forcing the selection based on a list of several options.

Types of question: Structured/Close-end, Unstructured/open-end

Structured questions take many forms and include:

  • Single response with nominal or ordinal categories (e.g. From the following list please select the category which includes your household income)
  • Multiple responses (e.g. From the following list of pizza toppings please any or all that you regularly use)
  • Scaled questions (e.g. The President is doing a good job: Strongly Agree to Strongly Disagree), and
  • Numerous variations on these primary types.

Unstructured questions are a bit more qualitative in feel. They do not require pre-defined categories and they allow the respondent to express their views openly. This is their blessing and their curse. Open-ended questions, as they are also known, produce a higher cognitive load in the sense that the respondent has to think harder to come to an answer. This can create a lower response rate and sometimes lesser quality data. On the other hand, they can produce rich insights that provide depth and color to the black and white of structured questions.

Open-ended questions require additional time on the part of the researcher to analyze and code the responses although text-mining software is making this easier. For best results on a survey, keep open-ended questions to a minimum and use them as sub-questions driven by critical responses to a structured question. For example, if someone selects a high or low response to the Net Promoter Score, you can follow up with unstructured questions asking the respondent to elaborate on their score.

Open-ended questions

Open-ended questions are exploratory in nature, and offer the researchers rich, qualitative data. In essence, they provide the researcher with an opportunity to gain insight on all the opinions on a topic they are not familiar with. However, being qualitative in nature makes these types of questions lack the statistical significance needed for conclusive research.

Nevertheless, open-ended questions are incredibly useful in several different ways:

  • Expert interviews
  • Small population studies
  • Preliminary research
  • A respondent outlet
  1. Expert interviews

Since questions that are open-ended ask for the critical thinking and uncut opinion of the respondent, they are perfect for gaining information from specialists in a field that the researcher is less qualified in. Example: If I wanted to learn the history of Ancient China (something I know very little about), I could create my survey for a selected group of historians whose focus is Ancient China. My survey would then be filled with broad open-ended questions that are designed to receive large amounts of content and provide the freedom for the expert to demonstrate their knowledge.

  1. Small population studies

Open-ended questions can be useful for surveys that are targeting a small group of people because there is no need for complex statistical analysis and the qualitative nature of the questions will give you more valuable input from each respondent. The rule here is the group must be small enough for the surveyor to be able to read each unique response and reflect on the information provided. Example: A supervisor who is looking for performance feedback from his/her team of six employees. The supervisor would benefit more from questions that allow the respondents to freely answer rather than forcing them into closed-ended questions that will limit their responses.

  1. Preliminary research

As stated in the closed-ended questions section, conclusive research usually requires preliminary research to be conducted in order to design the appropriate research objects, survey structure and questions. Open-ended questions can reveal to the surveyor a variety of opinions and behaviours among the population that they never realized. It is therefore, incredibly useful to use open-ended questions to gain information for further quantitative research.

  1. A respondent outlet

It is usually a good idea in any survey, no matter how large, to leave an open-ended comments question at the end. This is especially in the case of a survey asking closed-ended questions on attitudes, opinions, or behaviours. Forcing respondents to answer closed-ended questions asks them to fit in your box of options and can leave them with extra information or concerns that they want to share with you. Providing respondents with the outlet of a comment box is showing them the respect they deserve for taking the time to fill out your survey.

There are a few drawbacks to open-ended questions as well. Though respondent answers are almost always richer in quality, the amount of effort it takes to digest the information provided can sometimes be overwhelming. That is why open-ended questions work best in studies with smaller populations. Furthermore, if your survey sample is a fraction of the population you are studying, you will be looking to find data which can be inferred on the overall population as statistically significant. Unfortunately, open-ended questions cannot be used in this manner, as each response should be seen as a unique opinion.

Closed-ended questions

Closed-ended questions come in a multitude of forms, including: multiple choice, drop down, checkboxes, and ranking questions. Each question type doesn’t allow the respondent to provide unique or unanticipated answers, but rather, choose from a list of pre-selected options. It’s like being offered spaghetti or hamburgers for dinner, instead of being asked “What would you like for dinner?”

Use closed-ended questions for the following:

  • When your audience isn’t particularly interested in your survey topic
  • When you need quantifiable data
  • To categorize respondents
  1. When your audience isn’t particularly interested in your survey topic

Closed-ended questions are easier to complete than open-ended questions. Why? Because closed-ended questions lay out all of the possible answers, removing respondents’ task of coming up with their own responses.

So, when you find yourself surveying an audience who may not be excited about what you’re asking them, air on the side of using closed-ended questions. It’ll give them an easier survey-taking experience and, in the process, provide you with a higher completion rate.

  1. When you need quantifiable data

If you’re looking for statistically significant stats, closed-ended questions are the way to go. Going back to our earlier example, using a closed-ended question can help us arrive at stats like: 70% of respondents want to eat spaghetti for dinner versus 30% who prefer hamburgers.

Questions that are closed-ended are conclusive in nature as they are designed to create data that is easily quantifiable. The fact that questions of this type are easy to code makes them particularly useful when trying to prove the statistical significance of a survey’s results. Furthermore, the information gained by closed-ended questions allows researchers to categorize respondents into groups based on the options they have selected.

  1. To categorize respondents

In other words, they allow you to conduct demographic studies. Why is this valuable?

Imagine that the manager of a designer clothing store believes that certain types of people are more likely to visit their store and purchase their clothing than others. To decipher which segment groups are most likely to be their customers, the manager could design a survey for anyone who has been a visitor. This survey could include closed-ended questions on gender, age, employment status, and any other demographic information they’d like to know. Then, it would be followed by questions on how often they visit the store and the amount of money they spend annually. Since all the questions are closed-ended, the store manager could easily quantify the responses and determine the profile of their typical customer. In this case, the manager may learn that her most frequent customers are female students, ages 18-25. This knowledge would allow her to move forward with an action plan on how to cater to this niche better or break into other target demographics.

The major drawback to closed-ended questions is that a researcher must already have a clear understanding of the topic of his/her questions and how they tie into the overall research problem before they are created. Without this, closed-ended questions will lead to insufficient options for respondents to select from, questions that do not properly reflect the research’s purpose, and limited or erroneous information.

For example, if I asked the question, “do you get to work by driving, busing, or walking?” I would have accidentally omitted carpooling, biking, cartwheeling or any other form of transportation I am unaware of. Instead, it would have been better for me to ask the open-ended question of “how do you get to work?” to learn all the different types of answer before forcing the selection based on a list of several options.

Types of Hypothesis, Sources

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of effect.

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example, If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, reduces the risk of many diseases such as heart disease, high blood pressure and some cancers.

Null Hypothesis

It provides the statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “HO”.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, causal hypothesis proposes a cause and effect interaction between two or more variables.

Sources of Hypothesis

  • The resemblance between the phenomenon.
  • Observations from past studies, present-day experiences and from the competitors.
  • Scientific theories.
  • General patterns that influence the thinking process of people.
  1. General Culture in which a Science Develops:

A cultural pattern influences the thinking process of the people and the hypothesis may be formulated to test one or more of these ideas. Cultural values serve to direct research interests. The function of culture has been responsible for developing today’s science to a great dimension. In the words of Goode and Hatt, “to say that the hypotheses are the product of the cultural values does not make them scientifically less important than others, but it does at least indicate that attention has been called to them by the culture itself.

For example, in the Western society race is thought to be an important determinant of human behaviour. Such a proposition can be used to formulate a hypothesis. We may also cite metaphysical bias and metaphysical ideas of Indian culture to have been responsible for the formulation of certain types of hypotheses. It implies that cultural elements of common cultural pattern may form a source of the formulation of hypotheses.

  1. Scientific Theory:

A major source of hypothesis is theory. A theory binds a large body of facts by positing a consistent and lawful relationship among a set of general concepts representing those facts. Further generalizations are formed on the basis of the knowledge of theory. Corollaries are drawn from the theories.

These generalizations or corollaries constitute a part of hypothesis. Since theories deal with abstractions which cannot be directly observed and can only remain in the thought process, a scientific hypothesis which is concerned with observable facts and observable relationship between facts can only be used for the purpose of selecting some of the facts as concrete instances of the concepts and for making a tentative statement about the existence of a relation among the selected facts with the purpose of subjecting the relation to an empirical test.”

A hypothesis emerges as a deduction from theory. Hence, hypotheses become “working instruments of theory” Every worthwhile theory provides for the formulation of additional hypothesis. “The hypothesis is the backbone of all scientific theory construction; without it, confirmation or rejection of theories would be impossible.”

The hypotheses when tested are “either proved or disproved and in turn constitute further tests of the original theory.” Thus the hypothetical type of verbal proposition forms the link between the empirical propositions or facts and the theories. The validity of a theory can be examined only by means of scientific predictions or experimental hypothesis.

  1. Analogies:

Observation of a similarity between two phenomena may be a source of formation of a hypothesis aimed at testing similarity in any other respect. Julian Huxley has pointed out that “casual observation in nature or in the framework of another science may be a fertile source of hypothesis. The success of a system in one discipline can be used in other discipline also. The theory of ecology is based on the observation of certain plants in certain geographical conditions. As such, it remains in the domain of Botany. On the basis of that the hypothesis of human ecology could be conceived.

Hypothesis of social physics is also based on analogy. “When the hypothesis was born out by social observation, the same term was taken into sociology. It has become an important idea in sociological theory”. Although analogy is not always considered, at the time of formulation of hypothesis; it is generally satisfactory when it has some structural analogies to other well established theories. For the systematic simplicity of our knowledge, the analogy of a hypothesis becomes inversely helpful. Formulation of an analogous hypothesis is construed as an achievement because by doing so its interpretation is made easy.

  1. Consequences of Personal, Idiosyncratic Experience as the Sources of Hypothesis:

Not only culture, scientific theory and analogies provide the sources of hypothesis, but also the way in which the individual reacts to each of these is also a factor in the statement of hypotheses. Certain facts are present, but every one of us is not able to observe them and formulate a hypothesis.

Referring to Fleming’s discovery of penicillin, Backrach has maintained that such discovery is possible only when the scientist is prepared to be impressed by the ‘unusual’. An unusual event struck Fleming when he noted that the dish containing bacteria had a green mould and the bacteria were dead. Usually he would have washed the dish and have attempted once again to culture the bacteria.

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But normally, he was moved to bring the live bacteria in close contact with the green mould, resulting in the discovery of penicillin. The example of Sir Issac Newton, the discoverer of the theory of Gravitation, is another glaring example of this type of ‘personal experience’. Although prior to Newton’s observation, several persons had witnessed the falling of the apple, he was the right man to formulate the theory of gravitation on the basis of this phenomenon.

Thus, emergence of a hypothesis is a creative manner. To quote Mc Guigan, “to formulate a useful and valuable hypothesis, a scientist needs first sufficient experience in that area, and second the quality of the genius.” In the field of social sciences, an illustration of individual perspective may be visualized in Veblen’s work. Thorstein Veblen’s own community background was replete with negative experiences concerning the functioning of economy and he was a ‘marginal man’, capable of looking at the capitalist system objectively.

Thus, he could be able to attack the fundamental concepts and postulates of classical economics and in real terms Veblen could experience differently to bear upon the economic world, resulting in the making of a penetrating analysis of our society. Such an excellent contribution of Veblen has, no doubt, influenced social science since those days.

Hypothesis Meaning, Nature, Significance, Null Hypothesis & Alternative Hypothesis

Hypothesis is a proposed explanation or assumption made on the basis of limited evidence, serving as a starting point for further investigation. In research, it acts as a predictive statement that can be tested through study and experimentation. A good hypothesis clearly defines the relationship between variables and provides direction to the research process. It can be formulated as a positive assertion, a negative assertion, or a question. Hypotheses help researchers focus their study, collect relevant data, and analyze outcomes systematically. If supported by evidence, a hypothesis strengthens theories; if rejected, it helps refine or redirect the research.

Nature of Hypothesis:

  • Predictive Nature

A hypothesis predicts the possible outcome of a research study. It forecasts the relationship between two or more variables based on prior knowledge, observations, or theories. Through prediction, the researcher sets a direction for investigation and frames experiments accordingly. The predictive nature helps in formulating tests and procedures that validate or invalidate the assumptions. By predicting outcomes, a hypothesis serves as a guiding tool for collecting and analyzing data systematically in the research process.

  • Testable and Verifiable

A fundamental nature of a hypothesis is that it must be testable and verifiable. Researchers should be able to design experiments or collect data to prove or disprove the hypothesis objectively. If a hypothesis cannot be tested or verified with empirical evidence, it has no scientific value. Testability ensures that the hypothesis remains grounded in reality and allows researchers to apply statistical tools, experiments, or observations to validate the proposed relationships or statements.

  • Simple and Clear

A good hypothesis must be simple, clear, and understandable. It should not be complex or vague, as this makes testing and interpretation difficult. The clarity of a hypothesis allows researchers and readers to grasp its meaning without confusion. It should specifically state the expected relationship between variables and avoid unnecessary technical jargon. A simple hypothesis makes the research process more organized and structured, leading to more reliable and meaningful results during analysis.

  • Specific and Focused

The nature of a hypothesis demands that it be specific and focused on a particular issue or problem. It should not be broad or cover unrelated aspects, which can dilute the research findings. Specificity helps researchers concentrate their efforts on one clear objective, design relevant research methods, and gather precise data. A focused hypothesis reduces ambiguity, minimizes errors, and improves the validity of the research results by maintaining a sharp direction throughout the study.

  • Consistent with Existing Knowledge

A hypothesis should align with the existing body of knowledge and theories unless it aims to challenge or expand them. It should logically fit into the current understanding of the subject to make sense scientifically. When a hypothesis is consistent with known facts, it gains credibility and relevance. Even when proposing something new, a hypothesis should acknowledge previous research and build upon it, rather than ignoring established evidence or scientific frameworks.

  • Objective and Neutral

A hypothesis must be objective and free from personal bias, emotions, or preconceived notions. It should be based on observable facts and logical reasoning rather than personal beliefs. Researchers must frame their hypotheses with neutrality to ensure that the research process remains fair and unbiased. Objectivity enhances the scientific value of the study and ensures that conclusions are drawn based on evidence rather than assumptions, preferences, or subjective interpretations.

  • Tentative and Provisional

A hypothesis is not a confirmed truth but a tentative statement awaiting validation through research. It is subject to change, modification, or rejection based on the findings. Researchers must remain open-minded and willing to revise the hypothesis if new evidence contradicts it. This provisional nature is crucial for the progress of scientific inquiry, as it encourages continuous testing, exploration, and refinement of ideas instead of blindly accepting assumptions.

  • Relational Nature

Hypotheses often establish relationships between two or more variables. They state how one variable may affect, influence, or be associated with another. This relational nature forms the backbone of experimental and correlational research designs. Understanding these relationships helps researchers explain causes, predict effects, and identify patterns within their study areas. Clearly stated relationships in hypotheses also facilitate the application of statistical tests and the interpretation of research findings effectively.

Significance of Hypothesis:

  • Guides the Research Process

The hypothesis acts as a roadmap for the researcher, providing clear direction and focus. It helps define what needs to be studied, which variables to observe, and what methods to apply. Without a hypothesis, research would be unguided and scattered. By offering a structured path, it ensures that the research efforts are purposeful and systematically organized toward achieving meaningful outcomes.

  • Defines the Focus of Study

A hypothesis narrows the scope of the study by specifying exactly what the researcher aims to investigate. It identifies key variables and their expected relationships, preventing unnecessary data collection. This concentration saves time and resources while allowing for more detailed analysis. A focused study helps in maintaining clarity throughout the research process and results in stronger, more convincing conclusions based on targeted inquiry.

  • Establishes Relationships Between Variables

A hypothesis highlights the potential relationships between two or more variables. It outlines whether variables move together, influence each other, or remain independent. Establishing these relationships is essential for explaining complex phenomena. Through hypothesis testing, researchers can confirm or reject assumed connections, leading to deeper understanding, better theories, and stronger predictive capabilities in both scientific and business research contexts.

  • Helps in Developing Theories

Hypotheses contribute significantly to theory building. When a hypothesis is repeatedly tested and supported by empirical evidence, it can help form new theories or refine existing ones. Theories built on tested hypotheses have greater scientific value and can guide future research and practice. Thus, hypotheses are not just for individual studies; they play a critical role in expanding the broader knowledge base of a discipline.

  • Facilitates the Testing of Concepts

Concepts and assumptions need validation before they can be widely accepted. A hypothesis facilitates this validation by providing a mechanism for empirical testing. It helps researchers design experiments or surveys specifically aimed at confirming or disproving a particular idea. This ensures that concepts do not remain speculative but are subjected to rigorous scientific scrutiny, enhancing the reliability and acceptance of research findings.

  • Enhances Objectivity in Research

Having a well-defined hypothesis enhances objectivity by setting specific criteria that research must meet. Researchers approach data collection and analysis with a neutral mindset focused on proving or disproving the hypothesis. This objectivity minimizes the influence of personal biases or preconceived notions, promoting fair and unbiased research results. In this way, hypotheses help maintain the scientific integrity of research projects.

  • Assists in Decision Making

In applied fields like business and healthcare, hypotheses help decision-makers by providing data-driven insights. By testing hypotheses about consumer behavior, product performance, or treatment outcomes, organizations and professionals can make informed decisions. This reduces risks and improves strategic planning. A hypothesis, therefore, transforms vague assumptions into evidence-based conclusions that directly impact policies, operations, and practices.

  • Saves Time and Resources

By clearly defining what needs to be studied, a hypothesis prevents researchers from wasting time and resources on irrelevant data. It limits the research to specific objectives and focuses efforts on gathering meaningful, actionable information. Efficient use of resources is critical in both academic and professional research settings, making a well-structured hypothesis an essential tool for maximizing productivity and effectiveness.

Null Hypothesis:

The null hypothesis (H₀) is a fundamental concept in statistical testing that proposes no significant relationship or difference exists between variables being studied. It serves as the default position that researchers aim to test against, representing the assumption that any observed effects are due to random chance rather than systematic influences.

In experimental design, the null hypothesis typically states there is:

  • No difference between groups

  • No association between variables

  • No effect of a treatment/intervention

For example, in testing a new drug’s efficacy, H₀ would state “the drug has no effect on symptom reduction compared to placebo.” Researchers then collect data to determine whether sufficient evidence exists to reject this null position in favor of the alternative hypothesis (H₁), which proposes an actual effect exists.

Statistical tests calculate the probability (p-value) of obtaining the observed results if H₀ were true. When this probability falls below a predetermined significance level (usually p < 0.05), researchers reject H₀. Importantly, failing to reject H₀ doesn’t prove its truth – it simply indicates insufficient evidence against it. The null hypothesis framework provides objective criteria for making inferences while controlling for Type I errors (false positives).

Alternative Hypothesis:

The alternative hypothesis represents the researcher’s actual prediction about a relationship between variables, contrasting with the null hypothesis. It states that observed effects are real and not due to random chance, proposing either:

  1. A significant difference between groups

  2. A measurable association between variables

  3. A true effect of an intervention

Unlike the null hypothesis’s conservative stance, the alternative hypothesis embodies the research’s theoretical expectations. In a clinical trial, while H₀ states “Drug X has no effect,” H₁ might claim “Drug X reduces symptoms by at least 20%.”

Alternative hypotheses can be:

  • Directional (one-tailed): Predicting the specific nature of an effect (e.g., “Group A will score higher than Group B”)

  • Non-directional (two-tailed): Simply stating a difference exists without specifying direction

Statistical testing doesn’t directly prove H₁; rather, it assesses whether evidence sufficiently contradicts H₀ to support the alternative. When results show statistical significance (typically p < 0.05), we reject H₀ in favor of H₁.

The alternative hypothesis drives research design by determining appropriate statistical tests, required sample sizes, and measurement precision. It must be formulated before data collection to prevent post-hoc reasoning. Well-constructed alternative hypotheses are testable, falsifiable, and grounded in theoretical frameworks, providing the foundation for meaningful scientific conclusions.

Stages in Research Process

Research Process refers to a systematic sequence of steps followed by researchers to investigate a problem or question. It involves identifying a research problem, reviewing relevant literature, formulating hypotheses, designing a research methodology, collecting data, analyzing the data, interpreting results, and drawing conclusions. This structured approach ensures reliable, valid, and meaningful outcomes in the study.

Stages in Research Process:

  1. Identifying the Research Problem

The first stage in the research process is to identify and define the research problem. This involves recognizing an issue, gap, or question in a particular field of study that requires investigation. Clearly articulating the problem is essential as it sets the foundation for the entire research process. Researchers need to explore existing literature, consult experts, or observe real-world issues to determine the research problem. Defining the problem ensures that the study remains focused and relevant, guiding the researcher in formulating objectives and hypotheses for further investigation.

  1. Reviewing the Literature

Once the research problem is identified, the next stage is reviewing existing literature. This step involves gathering information from books, journal articles, reports, and other scholarly sources related to the research topic. A comprehensive literature review helps researchers understand the current state of knowledge on the subject and identifies gaps in existing studies. It also helps refine the research problem, build hypotheses, and establish a theoretical framework. A well-conducted literature review ensures that the researcher’s work contributes to the existing body of knowledge and avoids duplication of previous studies.

  1. Formulating Hypothesis or Research Questions

In this stage, researchers formulate hypotheses or research questions based on the research problem and literature review. A hypothesis is a testable statement about the relationship between variables, while research questions are open-ended queries that guide the investigation. These hypotheses or questions direct the research design and data collection methods. A well-defined hypothesis or research question helps in focusing the research, making it possible to derive meaningful conclusions. This stage ensures that the study remains on track and allows researchers to clearly communicate the aim and scope of their research.

  1. Research Design and Methodology

The research design is a blueprint for the entire research process. In this stage, researchers select an appropriate methodology to collect and analyze data. They decide whether the research will be qualitative, quantitative, or a mix of both. The design outlines the research approach, methods of data collection, sampling techniques, and analytical tools to be used. A well-defined research design ensures that the study is structured, systematic, and capable of addressing the research questions effectively. This stage also includes setting timelines, budgeting, and ensuring ethical considerations are met.

  1. Data Collection

Data collection is a critical stage where the researcher gathers the necessary information to address the research problem. The data collection method depends on the research design and could involve surveys, interviews, observations, or experiments. Researchers ensure that they collect valid and reliable data, adhering to ethical guidelines such as consent and confidentiality. This stage is vital for providing the empirical evidence needed to test hypotheses or answer research questions. Proper data collection ensures that the research is based on accurate and comprehensive information, forming the basis for analysis and conclusions.

  1. Data Analysis

Once data is collected, the next step is data analysis, where researchers process and interpret the information gathered. The type of analysis depends on the research design—quantitative data might be analyzed using statistical tools, while qualitative data is typically analyzed through thematic analysis or content analysis. Researchers examine patterns, relationships, and trends in the data to draw conclusions or test hypotheses. Effective data analysis helps researchers provide answers to research questions and ensures the results are valid, reliable, and relevant to the research problem. This stage is key to producing meaningful insights.

  1. Interpretation and Presentation of Results

In this stage, researchers interpret the data analysis results, drawing conclusions based on the evidence. The researcher compares the findings to the original hypotheses or research questions and discusses whether the data supports or contradicts expectations. They may also explore the implications of the findings, the limitations of the study, and suggest areas for future research. The results are then presented in a clear, structured format, typically through a research paper, report, or presentation. Effective communication of the results ensures that the research contributes to the body of knowledge and informs decision-making.

  1. Conclusion and Recommendations

The final stage in the research process involves summarizing the key findings and offering recommendations based on the research results. In the conclusion, researchers restate the importance of the research problem, summarize the main findings, and discuss how these findings address the research questions or hypotheses. If applicable, they provide suggestions for practical applications of the research. Researchers may also suggest areas for future research to explore unanswered questions or limitations of the study. This stage ensures that the research has real-world relevance and potential for further exploration.

Applied Research

Applied research is a methodology used to solve a specific, practical issue affecting an individual or group. This scientific method of study and research is used in business, medicine, and education in order to find solutions that may improve health, solve scientific problems or develop new technology. Examples of applied research topics will show you how this method can be used to address everyday problems.

Characteristics of Applied Research in Education

  • It clearly highlights generalizations and hypotheses that inform the research findings.
  • It relies on empirical evidence.
  • It is set at providing solutions to a defined problem.
  • It requires accurate observation and description.

Examples of Applied Research

The following are examples for applied research. You can notice that each of these studies aim to resolve a specific and an immediate problem.

  • A study into the ways of improving the levels of customer retention for D-Mart in India.
  • An investigation into the ways of improving employee motivation in Taj Hotel, Mumbai
  • Development of strategies to introduce change in Starbucks global supply-chain management with the view on cost reduction
  • A study into the ways of fostering creative deviance amongst employees without compromising respect for authority.

Types of Applied Research

There are 3 types of applied research. These are evaluation research, research and development, and action research.

  • Evaluation Research

Evaluation research is a type of applied research that analyses existing information about a research subject to arrive at objective research outcomes or reach informed decisions. This type of applied research is mostly applied in business contexts, for example, an organisation may adopt evaluation research to determine how to cut down overhead costs.

  • Research and Development

Research and development is a type of applied research that is focused on developing new products and services based on the needs of target markets. It focuses on gathering information about marketing needs and finding ways to improve on an existing product or create new products that satisfy the identified needs.

  • Action Research

Action research is a type of applied research that is set on providing practical solutions to specific business problems by pointing the business in the right directions. Typically, action research is a process of reflective inquiry that is limited to specific contexts and situational in nature.

Advantages and Disadvantages of Applied Research

The advantages and disadvantages of applied and fundamental research mirror and contrast each other. On the positive side, applied research can be helpful in solving specific problems in business and other settings.

On the negative side, findings of applied research cannot be usually generalized. In other words, applicability of the new knowledge generated as a result of applied research is limited to the research problem. Moreover, applied studies usually have tight deadlines which are not flexible.

Empirical Research

Empirical research is research using empirical evidence. It is also a way of gaining knowledge by means of direct and indirect observation or experience. Empiricism values some research more than other kinds. Empirical evidence (the record of one’s direct observations or experiences) can be analyzed quantitatively or qualitatively. Quantifying the evidence or making sense of it in qualitative form, a researcher can answer empirical questions, which should be clearly defined and answerable with the evidence collected (usually called data). Research design varies by field and by the question being investigated. Many researchers combine qualitative and quantitative forms of analysis to better answer questions which cannot be studied in laboratory settings, particularly in the social sciences and in education.

In some fields, quantitative research may begin with a research question (e.g., “Does listening to vocal music during the learning of a word list have an effect on later memory for these words?”) which is tested through experimentation. Usually, the researcher has a certain theory regarding the topic under investigation. Based on this theory, statements or hypotheses will be proposed (e.g., “Listening to vocal music has a negative effect on learning a word list.”). From these hypotheses, predictions about specific events are derived (e.g., “People who study a word list while listening to vocal music will remember fewer words on a later memory test than people who study a word list in silence.”). These predictions can then be tested with a suitable experiment. Depending on the outcomes of the experiment, the theory on which the hypotheses and predictions were based will be supported or not, or may need to be modified and then subjected to further testing.

Characteristics

  • A research question, which will determine research objectives.
  • A particular and planned design for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
  • The gathering of primary data, which is then analysed.
  • A particular methodology for collecting and analysing the data, such as an experiment or survey.
  • The limitation of the data to a particular group, area or time scale, known as a sample: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
  • The ability to recreate the study and test the results. This is known as reliability.
  • The ability to generalise from the findings to a larger sample and to other situations.

Usage

The researcher attempts to describe accurately the interaction between the instrument (or the human senses) and the entity being observed. If instrumentation is involved, the researcher is expected to calibrate his/her instrument by applying it to known standard objects and documenting the results before applying it to unknown objects. In other words, it describes the research that has not taken place before and their results.

In practice, the accumulation of evidence for or against any particular theory involves planned research designs for the collection of empirical data, and academic rigor plays a large part of judging the merits of research design. Several typologies for such designs have been suggested, one of the most popular of which comes from Campbell and Stanley. They are responsible for popularizing the widely cited distinction among pre-experimental, experimental, and quasi-experimental designs and are staunch advocates of the central role of randomized experiments in educational research.

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

  • Quantitative research: Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables. These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
  • Qualitative research: Qualitative research methods are used to gather non numerical data. It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Quantitative research methods

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

  • Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.
  • Experimental research: In experimental research, an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.
  • Correlational research: Correlational research is used to find relation between two set of variables. Regression is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.
  • Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.
  • Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause-and-effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.
  • Causal-Comparative research: This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

Qualitative research methods

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research method are used to derive a conclusion to support the theory or hypothesis being studied.

  • Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real-life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.
  • Textual Analysis: This primarily involves the process of describing, interpreting, and understanding textual content. It typically seeks to connect the text to a broader artistic, cultural, political, or social context (Fairclough, 2003).

A relatively new research method, textual analysis is often used nowadays to elaborate on the trends and patterns of media content, especially social media. Data obtained from this approach are primarily used to determine customer buying habits and preferences for product development, and designing marketing campaigns.

  • Focus Groups:

A focus group is a thoroughly planned discussion guided by a moderator and conducted to derive opinions on a designated topic. Essentially a group interview or collective conversation, this method offers a notably meaningful approach to think through particular issues or concerns.

This research method is used when a researcher wants to know the answers to “how,” “what,” and “why” questions. Nowadays, focus groups are among the most widely used methods by consumer product producers for designing and/or improving products that people prefer.

  • Observational method: Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.
  • One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.
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