ASSIGNMENT No. 2
Course: Methods of Social Research: Tools of Data Collection (4688)
Level: M.Sc Sociology
Semester: Autumn, 2019
What are steps involved in questionnaire construction? Discuss in detail the different types of interview.
Questionnaires are frequently used in quantitative marketing research and social research. They are a valuable method of collecting a wide range of information from a large number of individuals, often referred to as respondents; it can be students, workers or any person whom you require information from.Adequate questionnaire construction is critical to the success of a survey. Inappropriate questions, incorrect ordering of questions, incorrect scaling, or bad questionnaire format can make the survey valueless, as it may not accurately reflect the views and opinions of the participants.
Different methods can be useful for checking a questionnaire and making sure it is accurately capturing the intended information.
Initial advice may include consulting subject-matter experts
using questionnaire construction guidelines to inform drafts, such as the Tailored Design Method, or those produced by National Statistical Organizations.
Empirical tests also provide insight into the quality of the questionnaire. This can be done by:
conducting cognitive interviewing. By asking a sample of potential-respondents about their interpretation of the questions and use of the questionnaire, a researcher can
carrying out a small pretest of the questionnaire, using a small subset of target respondents. Results can Inform a researcher of errors such as missing questions, or logical and procedural errors.
Questionnaire design is one of the
most critical stages in the survey research process. While common sense and
good grammar are important in question writing, more is required in the art of
questionnaire design. To assume that people will understand the questions is
common error. People may not simply know what is being asked. They may be
unaware of topic of interest, they may confuse thee subject with something
else, or the question may not mean the same thing to every respondent.
Respondents may simply refuse to answer personal questions. Further, properly
wording the questionnaire Is crucial, as some problems may be minimized or
avoided altogether if a skilled researcher composes the questions.
Questionnaire is a series of questions asked to individuals to obtain statistically useful information about a given topic. When properly constructed and responsibly administered, questionnaires become a vital instrument by which statements can be made about specific groups or people or entire populations. Questionnaires are frequently used in quantitative marketing research and social research. They are a valuable method of collecting a wide range of information from a large number of individuals, often referred to as respondents. Adequate questionnaire construction is critical to the success of a survey. Inappropriate questions, incorrect ordering of questions, incorrect scaling, or bad questionnaire format can make the survey valueless, as it may not accurately reflect the views and opinions of the participants. A useful method for checking a questionnaire and making sure it is accurately capturing the intended information is to pretest among a smaller subset of target Respondents Know how (and whether) you will use the results of your research before you start. If, for example, the results won’t influence your decision or you can’t afford to implement the findings or the cost of the research outweighs its usefulness, then save your time and money; don’t bother doing the research.
The research objectives and frame of reference should be defined beforehand, including the questionnaire’s context of time, budget, manpower, intrusion and privacy.
How (randomly or not) and from where (your sampling frame) you select the respondents will determine whether you will be able to generalize your findings to the larger population.
The nature of the expected responses should be defined and retained for interpretation of the responses, be it preferences (of products or services), facts, beliefs, feelings, descriptions of past behavior, or standards of action.
Unneeded questions are an expense to the researcher and an unwelcome imposition on the respondents. All questions should contribute to the objective(s) of the research.
The type of scale, index, or typology to be used shall be determined.
The level of measurement you use will determine what you can do with and conclude from the data. If the response option is yes/no then you will only know how many or what percent of your sample answered yes/no. You cannot, however, conclude what the average respondent answered.
The types of questions (closed, multiple-choice, open) should fit the statistical data analysis techniques available and your goals.
For example, if you just ask “What is your income?” The respondent doesn’t know whether you mean weekly or monthly or annual, pretax or aftertax, household or Individual, this year or last year, from salary only or including dividends, Interest, etc.
> For example, avoid questions like “What should be done about. Murderous terrorists who threaten the freedom of good citizens and the safety of our children?”
> The qualities of a good questionnaire
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).
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 ‘minimize’ these problems.
It should obtain the most complete
and accurate information possible Thy questionnaire designer needs to ensure
that respondents fully u the questions and are not likely to refuse to answer,
lie to the interview 4′ or try to conceal their attitudes. A good questionnaire
is organized awrrr r
worded to encourage respondents to provide accurate, unbiased a complete information.
Steps preceding questionnaire design
Even after the exploratory phase, two key steps remain to be completed before the task of designing the questionnaire should commence. The first of these is to articulate the questions that research is intended to address. The second step is to determine the hypotheses around which the questionnaire is to be designed.
It is possible for the piloting exercise to be used to make necessary adjustments to administrative aspects of the study. This would include, for example, an assessment of the length of time an interview actually takes, in comparison to the planned length of the interview; or, in the same way, the time needed to complete questionnaires. Moreover, checks can be made on the appropriateness of the timing of the study in relation to contemporary events such as avoiding farm visits during busy harvesting periods.
Preliminary decisions in questionnaire design
There are nine steps involved in the development of a questionnaire:
- Decide the information required.
- Define the target respondents.
- Choose the method(s) of reaching your target respondents.
- Decide on question content.
- Develop the question wording.
- Put questions into a meaningful order and format.
- Check the length of the questionnaire.
- Pre-test the questionnaire.
- 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.
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 method(s) 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.
general rule is that the more
sensitive or personal the information, the mor personal the form of data
collection should be.
Decide on question content
Researchers must always be prepared to ask, Is this question really needed?’ a The temptation to include questions without critically evaluating their st contribution towards the achievement of the research objectives, as they 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 twc rf occasions when seemingly “redundant” questions might be included:. Opening questions that are easy to answer and which are not perceived as being ri “threatening”, and/or are perceived as being interesting, can greatly assist in iT gaining the respondent’s involvement in the survey and help to establish a
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 rumors 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 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.
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.
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 analyzed) 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.
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.
Aiou 4688 Solved 2 Assignment Autumn 2019
what are the four scales of measurements? Explain with examples.
In measuring one devises some form of scale and then transfers the observation of property indicants onto this scale several types of scales are possible the appropriate choice depends on what you assume about the mapping rules each scale has its own set of underlying assumptions about how the numerals correspond to real word observations.
Scales classifications employ the real numbers system the most accepted basis for scaling has three characteristics.
Numbers are ordered one number is greater than less than or equal to another number
Differences between numbers are ordered the difference between any pair of numbers is greater than less than or equal to the difference between any other pair of numbers
The number series ahs a unique origin indicated by the number zero.
Ordinal scales include the characteristics of the nominal scale plus of order ordinal scales are possible if the transitivity postulate postulate states if a is greater than b and b is greater than c. than a is great
The use of an ordinal scale implies a statement of greater than or less than equality statement is also acceptable without stating how much greater or le like a rubber yardstick it can stretch varying amounts a different places aloe its length thus the real difference between ranks one and two may be more less than the difference between ranks two and three.
Ration scales incorporate all of the powers of the previous ones plus ti provision for absolute zero or origin the ration scale represents the actu amounts of a variable measures of physical dimensions such as weight heig distance and area are examples in the behavioral sciences few situatioi satisfy the requirements of the ration scale the area of psychophysics offerir some exceptions in business research we find ration scales in many areas.
Interval scales have the power of nominal and ordinal scales plus or additional strength: they incorporate the concept of equality of interval (ti distance between 1 and 2 equals the distance between 2 and 3). For exampl the elapsed time between 3 and 6 A. M. equals the time between 4 and 7 A. I One cannot say, however, 6 A.M. is twice as late as 3 A.M. because “zero time is an arbitrary origin. In the consumer price index, if the base year is 1983, d price level during 1983 will be set arbitrarily as
100. Although this is an equal interval measurement scale, the zero Point arbitrary.
The table below will help clarify the fundamental differences between the four scales of measurement.
You will notice in the above table that only the ratio scale meets the criteria for all four properties of scales of measurement. Interval and Ratio data are sometimes referred to as parametric and Nominal and Ordinal data are referred to as nonparametric. Parametric means that it meets certain requirements with respect to parameters of the population (for example, the data will be normal – the distribution parallels the normal or bell curve). In addition, it means that numbers can be added, subtracted, multiplied, and divided. Parametric data are analyzed using statistical techniques identified as Parametric Statistics. As a rule, there are more statistical technique options for the analysis of parametric data and parametric statistics are considered more powerful than nonparametric statistics. Nonparametric data are lacking those same parameters and can not be added, subtracted, multiplied, and divided. For example, it does not make sense to add Social Security numbers to get a third person. Nonparametric data are analyzed by using Nonparametric Statistics. As a rule, ordinal data is considered nonparametric and can not be added, etc.. Again, it does not make sense to add together first and second place in a race – one does not get third place. However, many assessment devices within the behavioral and social sciences (for example, intelligence scales) as well as Likert-type scales represent ordinal data but are often treated as if they are interval data. For example, the “average” amount of pain that a person reports on a Likert-type scale over the course of a day would be computed by adding the reported pain levels taken over the course of the day and dividing by the number of times the question was answered. Theoretically, as this represents ordinal data, this computation should not be done. As stated above, many measures (ie. personality, intelligence, psycho-social, etc.) within the behavioral and social sciences represent ordinal data. IQ scores may be computed for a group of individuals. They will represent differences between individuals and the direction of those differences but they lack the property of indicating the amount of the differences. Psychologists have no way of truly measuring and quantifying intelligence. An individual with an IQ of 70 does not have exactly half of the intelligence of an individual with an IQ of 140. Therefore, IQ scales should theoretically be treated as ordinal data. In both of the above illustrations, the statement is make that they should be theoretically treated as ordinal data. In practice, however, they are usually treated as if they represent parametric (interval or ratio) data. This opens up the possibility for use of parametric statistical techniques with these data and the benefits associated with the use of techniques.
Aiou 4688 Solved 2 Assignment Autumn 2019
Elaborate the importance of content analysis. Also discuss the advantages and disadvantages of document study.
Content analysis is a systematic, quantitative process of analyzing communication messages by determining the frequency of message characteristics. Content analysis as a research method has advantages and disadvantages. Content analysis is useful in describing communicative messages, the research process is relatively unobtrusive, and content analysis provides a relatively safe process for examining communicative messages, but it can be time-consuming and presents several methodological challenges. This entry identifies several advantages and disadvantages of content analysis related to the scope, data, and process of content analysis.
Three different definition of content analysis are provided below.
- Definition 1: “Any technique for making inferences by systematically and objectively identifying special characteristics of messages.” (from Holsti, 1968)
- Definition 2: “An interpretive and naturalistic approach. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research (reliability, validity and generalizability) (from Ethnography, Observational Research, and Narrative Inquiry, 1994-2012).
- Definition 3: “A research technique for the objective, systematic and quantitative description of the manifest content of communication.” (from Berelson, 1952)
Uses of Content Analysis
- Identify the intentions, focus or communication trends of an individual, group or institution
- Describe attitudinal and behavioral responses to communications
- Determine psychological or emotional state of persons or groups
- Reveal international differences in communication content
- Reveal patterns in communication content
- Pre-test and improve an intervention or survey prior to launch
- Analyze focus group interviews and open-ended questions to complement quantitative data
Types of Content Analysis
There are two general types of content analysis: conceptual analysis and relational analysis. Conceptual analysis determines the existence and frequency of concepts in a text. Relational analysis develops the conceptual analysis further by examining the relationships among concepts in a text. Each type of analysis may lead to different results, conclusions, interpretations and meanings.
Typically people think of conceptual analysis when they think of content analysis. In conceptual analysis, a concept is chosen for examination and the analysis involves quantifying and counting its presence. The main goal is to examine the occurrence of selected terms in the data. Terms may be explicit or implicit. Explicit terms are easy to identify. Coding of implicit terms is more complicated: you need to decide the level of implication and base judgments on subjectivity (issue for reliability and validity). Therefore, coding of implicit terms involves using a dictionary or contextual translation rules or both.
To begin a conceptual content analysis, first identify the research question and choose a sample or samples for analysis. Next, the text must be coded into manageable content categories. This is basically a process of selective reduction. By reducing the text to categories, the researcher can focus on and code for specific words or patterns that inform the research question.
General steps for conducting a conceptual content analysis:
1. Decide the level of analysis: word, word sense, phrase, sentence, themes
2. Decide how many concepts to code for: develop pre-defined or interactive set of categories or concepts. Decide either: A. to allow flexibility to add categories through the coding process, or B. to stick with the pre-defined set of categories.
- Option A allows for the introduction and analysis of new and important material that could have significant implications to one’s research question.
- Option B allows the researcher to stay focused and examine the data for specific concepts.
3. Decide whether to code for existence or frequency of a concept. The decision changes the coding process.
- When coding for the existence of a concept, the researcher would count a concept only once if it appeared at least once in the data and no matter how many times it appeared.
- When coding for the frequency of a concept, the researcher would count the number of times a concept appears in a text.
4. Decide on how you will distinguish among concepts:
- Should text be coded exactly as they appear or coded as the same when they appear in different forms? For example, “dangerous” vs. “dangerousness”. The point here is to create coding rules so that these word segments are transparently categorized in a logical fashion. The rules could make all of these word segments fall into the same category, or perhaps the rules can be formulated so that the researcher can distinguish these word segments into separate codes.
- What level of implication is to be allowed? Words that imply the concept or words that explicitly state the concept? For example, “dangerous” vs. “the person is scary” vs. “that person could cause harm to me”. These word segments may not merit separate categories, due the implicit meaning of “dangerous”.
5. Develop rules for coding your texts. After decisions of steps 1-4 are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. The researcher can code for exactly what he/she wants to code. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules. In content analysis, obeying by the translation rules is equivalent to validity.
6. Decide what to do with irrelevant information: should this be ignored (e.g. common English words like “the” and “and”), or used to reexamine the coding scheme in the case that it would add to the outcome of coding?
7. Code the text: This can be done by hand or by using software. By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize error far more easily (e.g. typos, misspelling). If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs. computer coding is most relevant for implicit information where category preparation is essential for accurate coding.
8. Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information. Typically, general trends and patterns can be identified.
Relational analysis begins like conceptual analysis, where a concept is chosen for examination. However, the analysis involves exploring the relationships between concepts. Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts.
To begin a relational content analysis, first identify a research question and choose a sample or samples for analysis. The research question must be focused so the concept types are not open to interpretation and can be summarized. Next, select text for analysis. Select text for analysis carefully by balancing having enough information for a thorough analysis so results are not limited with having information that is too extensive so that the coding process becomes too arduous and heavy to supply meaningful and worthwhile results.
There are three subcategories of relational analysis to choose from prior to going on to the general steps.
- Affect extraction: an emotional evaluation of concepts explicit in a text. A challenge to this method is that emotions can vary across time, populations, and space. However, it could be effective at capturing the emotional and psychological state of the speaker or writer of the text.
- Proximity analysis: an evaluation of the co-occurrence of explicit concepts in the text. Text is defined as a string of words called a “window” that is scanned for the co-occurrence of concepts. The result is the creation of a “concept matrix”, or a group of interrelated co-occurring concepts that would suggest an overall meaning.
- Cognitive mapping: a visualization technique for either affect extraction or proximity analysis. Cognitive mapping attempts to create a model of the overall meaning of the text such as a graphic map that represents the relationships between concepts.
General steps for conducting a relational content analysis:
1. Determine the type of analysis:
Once the sample has been selected, the researcher needs to determine what types
of relationships to examine and the level of analysis: word, word sense,
phrase, sentence, themes.
2. Reduce the text to categories and code for words or patterns. A researcher can code for existence of meanings or words.
3. Explore the relationship between concepts: once the words are coded, the text can be analyzed for the following:
- Strength of relationship: degree to which two or more concepts are related.
- Sign of relationship: are concepts positively or negatively related to each other?
- Direction of relationship: the types of relationship that categories exhibit. For example, “X implies Y” or “X occurs before Y” or “if X then Y” or if X is the primary motivator of Y.
4. Code the relationships: a difference
between conceptual and relational analysis is that the statements or
relationships between concepts are coded.
5. Perform statistical analyses: explore differences or look for relationships among the identified variables during coding.
6. Map out representations: such as decision mapping and mental models.
Reliability and Validity
Reliability: Because of the human nature of researchers, coding errors can never be eliminated but only minimized. Generally, 80% is an acceptable margin for reliability. Three criteria comprise the reliability of a content analysis:
- Stability: the tendency for coders to consistently re-code the same data in the same way over a period of time.
- Reproducibility: tendency for a group of coders to classify categories membership in the same way.
- Accuracy: extent to which the classification of text corresponds to a standard or norm statistically.
Validity: Three criteria comprise the validity of a content analysis:
- Closeness of categories: this can be achieved by utilizing multiple classifiers to arrive at an agreed upon definition of each specific category. Using multiple classifiers, a concept category that may be an explicit variable can be broadened to include synonyms or implicit variables.
- Conclusions: What level of implication is allowable? Do conclusions correctly follow the data? Are results explainable by other phenomena? This becomes especially problematic when using computer software for analysis and distinguishing between synonyms. For example, the word “mine,” variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. Software can obtain an accurate count of that word’s occurrence and frequency, but not be able to produce an accurate accounting of the meaning inherent in each particular usage. This problem could throw off one’s results and make any conclusion invalid.
- Generalizability of the results to a theory: dependent on the clear definitions of concept categories, how they are determined and how reliable they are at measuring the idea one is seeking to measure. Generalizability parallels reliability as much of it depends on the three criteria for reliability.
Advantages of Content Analysis
- Directly examines communication using text
- Allows for both qualitative and quantitative analysis
- Provides valuable historical and cultural insights over time
- Allows a closeness to data
- Coded form of the text can be statistically analyzed
- Unobtrusive means of analyzing interactions
- Provides insight into complex models of human thought and language use
- When done well, is considered a relatively “exact” research method
- Content analysis is a readily-understood and an inexpensive research method
- A more powerful tool when combined with other research methods such as interviews, observation, and use of archival records. It is very useful for analyzing historical material, especially for documenting trends over time.
Disadvantages of Content Analysis
- Can be extremely time consuming
- Is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation
- Is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study
- Is inherently reductive, particularly when dealing with complex texts
- Tends too often to simply consist of word counts
- Often disregards the context that produced the text, as well as the state of things after the text is produced
- Can be difficult to automate or computerize
Aiou 4688 Solved 2 Assignment Autumn 2019
Q.4 Write comprehensive notes on the following: (25)
i) Report Writing
ii) Hypothesis Testing & Drawing Inference
Report Writing: Report writing is the last step in a research study but it is a very significant step because the results or research have very little value if they are not communicated effectively to the right person, at the right time and in the right manner.
contents of the research report
The lay out of the report must be appropriate. A comprehensive report will include.
•The Preliminary Pages
•The Main Text
•The End Matter
(A)The Preliminary Pages
These should include
(i)The title page :- The title page should carry the title of the research study, the organization for which the research is being carried out and the name of the researcher.
(ii)Acknowledgement – The research should acknowledge those people or organizations/ institutions who have significantly contributed in completing the research.
(iii)Any certification required: If any certificate/s regarding the research are necessary this may be attached at this point.
(iv)Preface: The preface should be a brief general introduction to the topic of research answering why the researcher took up the research.
(v)Table of Contents/ Index: The table of contents or index is a vital part of this report and must include the page number of each of the contents.
(vi)List of Table and Illustrations and list of graphs and or charts: These may be given on the following pages as ready reference.
Introduction: This should introduce the research to the reader. The language of the introduction should be so simple that a lay man whom having little knowledge about the study area could also be able to understand the purpose and objectives of the research.
Nature and scope of the study: It explains about why the study was undertaken and what geographical area and time frame it occupies.
Research Methodology: The methodology involves in the study, the research design, data collection techniques, analysis techniques and method of interpretation should be mentioned.
Hypothesis: If the study involves testing of hypothesis. These should be clearly stated.
Supporting theoretical background: Any secondary research or theories that may be relevant for our studies should be discussed.
Results for the study: A detailed presentation of the findings of the study with reporting data in the form of tables and charts should be given.
Analysis of data: The analysis of the data which has been statistically treated should be made and noted.
Interpretation of results: After analyzing the data the researcher should note down his interpretations of results derived from the analysis of data.
Recommendations, suggestions, and conclusions: This step is the most important part of the research report because any decision takes on the basis of research will be based upon the recommendations, suggestions and conclusions.
Summary: The summary helps to present the research briefly and presents all the information about research in a capsule form.
(C) End matter:
Appendices: All technical data such as questionnaire, sample information, mathematical derivation etc. should form a part of the appendix. It should be numbered alphabetically or numerically.
Bibliography and references: The source consulted must be given. Bibliography forms all the relevant subject matter studied by the research for conducting the research while references is specific or particular subject matter or text that not only researcher studied but used for his investigation.
Glossary: This includes a list of special terms used in the research along with their definitions.
Subject Index: Sometimes a subject index given alphabetically works as a guide to the reader for the contents of the report. Points to remember while writing a research report:
(1)The report should be lengthy enough to cover all the points but short and crisp enough to hold the interests of the reader.
(2)The report should provide relevant and important information leaving out undesirable, extra information.
(3)Technical terms used in the report should be explained in the glossary.
(4)Charts, graphs, statistical table and figures should be used for readability, interest and quick grasp of the research.
(5)The layout of the report should be as per the format discussed earlier or as per the outline of the institution.
(6)The report should be free of grammatical mistake and should contain sound sentence construction. Use of quotation, footnotes, etc. should be done in the correct manner and context.
(7)The research report should be on original work.
(8)The analysis of data must be done in a logical manner.
(9)Appendices must be numbered and attached if needed.
(10)The index should be clear and the pagation should be done correctly.
(11)A detailed bibliography and references must be given.
(12)The report must be neat and attractive in appearance.
(13)The summary must be given in the end.
(14)The objective of the study, nature and scope of the study must be mentioned in the beginning of the study and the conclusions, suggestions and recommendations must justify it in the end.
ii) Hypothesis Testing & Drawing Inference
A statistical hypothesis, sometimes called confirmatory data analysis, is a hypothesis that is testable on the basis of observing a process that is modeled via a set of random variables. A statistical hypothesis test is a method of statistical inference. Commonly, two statistical data sets are compared, or a data set obtained by sampling is compared against a synthetic data set from an idealized model. An alternative hypothesis is proposed for the statistical-relationship between the two data-sets, and is compared to an idealized null hypothesis that proposes no relationship between these two data-sets. This comparison is deemed statistically significant if the relationship between the data-sets would be an unlikely realization of the null hypothesis according to a threshold probability—the significance level. Hypothesis tests are used when determining what outcomes of a study would lead to a rejection of the null hypothesis for a pre-specified level of significance.
Null and Alternative Hypothesis
Null hypothesis is a claim that is initially assumed to be true and alternative hypothesis is a statement that contradicts the null hypothesis. The process of distinguishing between the null hypothesis and the alternative hypothesis is aided by considering two conceptual types of errors. The first type of error occurs when the null hypothesis is wrongly rejected. The second type of error occurs when the null hypothesis is wrongly not rejected. (The two types are known as type 1 and type 2 errors.)
Hypothesis tests based on statistical significance are another way of expressing confidence intervals (more precisely, confidence sets). In other words, every hypothesis test based on significance can be obtained via a confidence interval, and every confidence interval can be obtained via a hypothesis test based on significance.
Significance-based hypothesis testing is the most common framework for statistical hypothesis testing. An alternative framework for statistical hypothesis testing is to specify a set of statistical models, one for each candidate hypothesis, and then use model selection techniques to choose the most appropriate model. The most common selection techniques are based on either Akaike information criterion or Bayes factor.
The purpose of statistical inference is to draw conclusions about a population on the basis of data obtained from a sample of that population. Hypothesis testing is the process used to evaluate the strength of evidence from the sample and provides a framework for making determinations related to the population, ie, it provides a method for understanding how reliably one can extrapolate observed findings in a sample under study to the larger population from which the sample was drawn. The investigator formulates a specific hypothesis, evaluates data from the sample, and uses these data to decide whether they support the specific hypothesis.
The first step in testing hypotheses is the transformation of the research question into a null hypothesis, H0, and an alternative hypothesis, HA.The null and alternative hypotheses are concise statements, usually in mathematical form, of 2 possible versions of “truth” about the relationship between the predictor of interest and the outcome in the population. These 2 possible versions of truth must be exhaustive (ie, cover all possible truths) and mutually exclusive (ie, not overlapping). The null hypothesis is conventionally used to describe a lack of association between the predictor and the outcome; the alternative hypothesis describes the existence of an association and is typically what the investigator would like to show. The goal of statistical testing is to decide whether there is sufficient evidence from the sample under study to conclude that the alternative hypothesis should be believed.
Hypothesis testing has been likened to a criminal trial, in which a jury must use evidence to decide which of 2 possible truths, innocence (H0) or guilt (HA), is to be believed. Just as a jury is instructed to assume that the defendant is innocent unless proven otherwise, the investigator should assume there is no association unless there is strong evidence to the contrary. A jury’s verdict must be either guilty or not guilty, in which case a not-guilty verdict does not equal innocence. Rather, it indicates that the burden of proof has not been met. Similarly, an investigator can only reject H0 or fail to reject it; failure to reject does not prove that the null H0 is true.
In a criminal trial in the United States, the required burden of proof is “beyond a reasonable doubt.” For hypothesis testing, the investigator sets the burden by selecting the level of significance for the test, which is the probability of rejecting H0 when H0 is true. The standard value chosen for level of significance is 5% (ie, P=0.05), which is a much weaker standard than used in the criminal justice system. This standard means that even if no association between predictor and outcome exists in the population, the investigator is willing to accept a 1 in 20 chance of a false-positive conclusion that an association does exist.
Just as hypothesis testing can reject a true null hypothesis (referred to as a type I error), it can fail to reject H0 when the predictor and outcome are associated (type II error). The probability of such a false-negative conclusion is called β. The quantity (1−β) is called the power of the test and is simply the probability of drawing the correct conclusion (ie, rejecting H0) when an association between predictor and outcome actually does exist.
In most cases, investigators are equally interested in whether a predictor leads to higher or lower levels of the outcome. In this situation, we specify a 2-sided statistical test, in which we accept a combined rate of false-positives (for both the higher and lower level of the outcome) of only 5%. If only 1 direction is of interest, a 1-sided test may be appropriate, but this choice requires strong justification. Because a 1-sided test is less stringent, many readers (and journal editors) appropriately view 1-sided tests with skepticism. Two-sided tests should also be considered the default option because an investigator’s intuition about how a study will come out may be incorrect. If an investigator chooses a 1-sided test but observes results opposite to those expected, the strongest statement that can be made is that the null hypothesis was not rejected. For these reasons, the investigator should always specify the hypotheses, the methods of analysis, and the level of significance before initiating the research.
Aiou 4688 Solved 2 Assignment Autumn 2019