Quantitative Research Flashcards
a. What are the advantages and disadvantages of open questions in a survey?
Disadvantages of Open
• different respondents give different degrees of detail in answers
• responses may be irrelevant or buried in useless detail
• comparisons and statistical analysis become difficult
• coding responses is difficult· articulate and highly literate respondents have an advantage
• questions may be too general for respondents who lose direction
• a greater amount of respondent time, thought, and effort is necessary
• respondents can be intimidated by questions
• answers take up a lot of space in the questionare.
Advantages of Open
• they permit an unlimited number of possible answers.
• respondents can answer in detail and can qualify and clarify responses
• unanticipated findings can be discovered
• they permit adequate answers to complex issues
• they permit creativity, self-expression, and richness of detail
• they reveal a respondent�s logic, thinking process, and frame of reference
b. Elaborate upon possible errors in survey based research and what you can do in order to avoid these errors when you design a questionnaire. In order to exemplify, you can use the case of MEASUREALL trying to understand their company’s innovation culture and the team climate (or any other case).
Errors
do people know or do they believe?
memory issues, e.g. do people recall the right amount or other facts?
social desirability
cultural values and norms (e.g. our study of
organizations’ internal change strategies)
When filling in the questionnaire - respondents don’t read instructions,
respond in a “repetitive” way etc. (e.g. US presidential ballot - election 2000)
Do you ask the right persons?
Respondents do not represent the desired sample (e.g.US presidential poll 1930’s and the use of internet surveys)
you should never split up a question so that it appears on two separate pages. A common error is to have some space left at the bottom of a page into which the question can be slotted but for the closed answers to appear on the next page.
Tips and skills
Common sources of error in survey research
There are many sources of error in survey research, in addition to those associated with sampling. This is a list of
the principal sources of error:
1. a poorly worded question;
2. the way the question is asked by the interviewer;
3. misunderstanding on the part of the interviewee;
4. memory problems on the part of the interviewee;
5. the way the information is recorded by the interviewer;
6. the way the information is processed, either when answers are coded or when data are entered into the
computer.
What is Operationalization?
Defining what you mean with an expression that you use in your problem definition, e.g. you would like to study “human capital”
You can use other words…(human capital is … and… but not…)
…or you can use a specific measurement technique (human capital is defined with the instrument YY that measures human capital)
When operationalizing the research questions as survey items, we are forced to be concrete, to specify exactly what information we expect from respondents
c. What is the meaning of generalization in the context of quantitative research (surveys)?
Generalization, which is an act of reasoning that involves drawing broad inferences from particular observations, is widely-acknowledged as a quality standard in quantitative research, but is more controversial in qualitative research. The goal of most qualitative studies is not to generalize but rather to provide a rich, contextualized understanding of some aspect of human experience through the intensive study of particular cases.
Generalization is an essential component of the wider scientific process.
In an ideal world, to test a hypothesis, you would sample an entire population.
You would use every possible variation of an independent variable. In the vast majority of cases, this is not feasible, so a representative group is chosen to reflect the whole population.
For any experiment, you may be criticized for your generalizations about sample, time and size.
• You must ensure that the sample group is as truly representative of the whole population as possible.
• For many experiments, time is critical as the behaviors can change yearly, monthly or even by the hour.
• The size of the group must allow the statistics to be safely extrapolated to an entire population
a. Do the steps in quantitative research suggest a deductive or inductive approach to the relationship between theory and research?
The roots of the quantitative studies suggest a deductive approach since the purpose is to test a theory or hypotesis. it comes from the epistemological orientation of the natural sciences and the positivism and the Objectivism as well.
The main steps in a quantitative research include the following which is rarely found in it pure form but help to give a starting point in getting grips of the ingredients and linkages between these steps.
- Theory:
- Hypothesis:
- Selection of research design
- Devise measures of concepts:
- Selection of research site:
- Selection of research respondents/subjects:
- Administration of research instruments/collection of data:
- Process of data:
- Analyze the data:
- Findings/Conclusions:
- Writing up findings/Conclusions:
Deductive or Inductive Approach of the Quantitative Research
A quantitative research shows the presence both of a deductive approach in Step 2 where from the theory a hypothesis is derived and tested and an inductive approach in the feedback loop (Step 11 to step 1) where findings are feedback to the initial theory. This is indicative of the positivist foundations of a quantitative research.
d. What is the meaning of generalization in quantitative research? And what are the implications for research?
In quantitative research the researcher is usually concerned to be able to say that his or her fi ndings can be generalized beyond the confines of the particular context in which the research was conducted. Thus, if a study of motivation to work is carried out by a questionnaire with a number of people who answer the questions, we often want to say that the results can apply to individuals other than those who responded in the study. This concern reveals itself in survey research in the attention that is often given to the question of how one can create a representative sample. Given that it is rarely feasible to send questionnaires to or interview whole populations (such as all members of a town, or the whole population of a country, or all members of an organization), we have to sample. However, we will want the sample to be as representative as possible in order to be able to say that the results are not unique to the particular group upon whom the research was conducted; in other words, we want to be able to generalize the fi ndings beyond the cases (for example, the people) that make up the sample. The preoccupation with generalization means some researchers become focused on developing lawlike principles about human behaviour that can be used to predict what people will do in certain situations. To complicate matters further, this research is sometimes based on studies of animal rather than human behaviour, thus raising the question of whether or not behaviour can be generalized from one species to another (see Research in focus 6.10).
e. Elaborate upon possible errors in the way you collect data, define words, sample, etc., when you design a questionnaire studying “What do consumers want from next generation mobile phones?”
We can think of ‘error’, a term that has been employed on a number of occasions, as being made up of four main factors (see Figure 7.9).
• Sampling error. See Key concept 7.1 for a defi nition.
This kind of error arises because it is extremely unlikely
that one will end up with a truly representative sample, even when probability sampling is employed.
• We can distinguish what might be thought of as sampling related error. This is error that is subsumed under the category non-sampling error (see Key concept 7.1) but that arises from activities or events that are related to the sampling process and are connected with the issue of generalizability or external validity of fi ndings. Examples are an inaccurate sampling frame and non-response.
• There is also error that is connected with the implementation of the research process. We might call this data collection error. This source of error includes such factors as: poor question wording in self-completion questionnaires or structured interviews; poor interviewing techniques; and fl aws in the administration of research instruments.
• Finally, there is data processing error. This arises from
faulty management of data, in particular, errors in the
coding of answers.
The third and fourth sources of error relate to factors
that are not associated with sampling and instead relate much more closely to concerns about the validity of measurement, which was addressed in Chapter 6. An example of the way that non-response can impact upon the external validity and generalizability of fi ndings is shown in Research in focus 7.10. However, the kinds of steps that need to be taken to keep these sources of error to a minimum in the context of social survey research will be addressed in the next three chapters.
a. What is meant by operationalization?
This term operationalization refers to a process of devising measures of concepts in which researchers are interested in carrying out a study. The term derived from physics refers to the operations by which a concept (like temperature or velocity) is measured. Good examples are concepts like motivation to work, TQM, technology, etc. devising a measure for these concepts so as to collect data related to understanding these concepts refers to operationalization. See pg 155, 157-158 for more explanations.
b. Ellaborate upon possible errors in the way you collect data, define words, sample, etc., when you design a questionnaire studying the “impact of six sigma tools on innovation”.
Collect Data
Define Words
Sample
b. Discuss the effects of sample size on a statistical significance test of survey result.
Significance depends on the sample size. The greater the sample size more significant are the results- T- Test Independent Means, Z-test of proportions. Often, an “acceptable” margin of error used by survey researchers falls between 4% and 8% at the 95% confidence level. Often, an “acceptable” margin of error used by survey researchers falls between 4% and 8% at the 95% confidence level
- Outline the main preoccupations of quantitative researchers. What reason can you give for their prominence?
Measurement
Causality
Generalization
Replication
