When to use a questionnaire?

When to use a questionnaire?

There is no all encompassing rule for when to use a questionnaire. The choice will be made based on a variety of factors including the type of information to be gathered and the available resources for the experiment. A questionnaire should be considered in the following circumstances.

a. When resources and money are limited. A Questionnaire can be quite inexpensive to administer. Although preparation may be costly, any data collection scheme will have similar preparation expenses. The administration cost per person of a questionnaire can be as low as postage and a few photocopies. Time is also an important resource that questionnaires can maximize. If a questionnaire is self-administering, such as a e-mail questionnaire, potentially several thousand people could respond in a few days. It would be impossible to get a similar number of usability tests completed in the same short time.

b. When it is necessary to protect the privacy of the participants. Questionnaires are easy to administer confidentially. Often confidentiality is the necessary to ensure participants will respond honestly if at all. Examples of such cases would include studies that need to ask embarrassing questions about private or personal behavior.

c. When corroborating other findings. In studies that have resources to pursue other data collection strategies, questionnaires can be a useful confirmation tools. More costly schemes may turn up interesting trends, but occasionally there will not be resources to run these other tests on large enough participant groups to make the results statistically significant. A follow-up large scale questionnaire may be necessary to corroborate these earlier results

Characteristics of a Good Questionnaire

•         Questions worded simply and clearly, not ambiguous or vague, must be objective

•         Attractive in appearance (questions spaced out, and neatly arranged)

•         Write a descriptive title for the questionnaire

•         Write an introduction to the questionnaire

•         Order questions in logical sequence

•         Keep questionnaire uncluttered and easy to complete

•         Delicate questions last (especially demographic questions)

•         Design for easy tabulation

•         Design to achieve objectives

•         Define terms

•         Avoid double negatives (I haven't no money)

•         Avoid double barreled questions (this AND that)

•         Avoid loaded questions ("Have you stopped beating your wife?")

Explain the various measure of central tendency?

In statistics, the general level, characteristic, or typical value that is representative of the majority of cases. Among several accepted measures of central tendency employed in data reduction, the most common are the arithmetic mean (simple average), the median, and the mode. 

FOR EXAMPLE, one measure of central tendency of a group of high school students is the average (mean) age of the students. Central tendency is a term used in some fields of empirical research to refer to what statisticians sometimes call "location". A "measure of central tendency" is either a location parameter or a statistic used to estimate a location parameter. 
Examples include: Arithmetic mean, the sum of all data divided by the number of observations in the data set.
Median, the value that separates the higher half from the lower half of the data set.
Mode, the most frequent value in the data set. Measures of central tendency, or "location", attempt to quantify what we mean when we think of as the "typical" or "average" score in a data set.
 The concept is extremely important and we encounter it frequently in daily life. For example, we often want to know before purchasing a car its average distance per litre of petrol. Or before accepting a job, you might want to know what a typical salary is for people in that position so you will know whether or not you are going to be paid what you are worth. Or, if you are a smoker, you might often think about how many cigarettes you smoke "on average" per day. Statistics geared toward measuring central tendency all focus on this concept of "typical" or "average." As we will see, we often ask questions in psychological science revolving around how groups differ from each other "on average". Answers to such a question tell us a lot about the phenomenon or process we are studying Arithmetic Mean 
The arithmetic mean is the most common measure of central tendency. It is simply the sum of the numbers divided by the number of numbers. The symbol mm is used for the mean of a population. The symbol MM is used for the mean of a sample. 

Although the arithmetic mean is not the only "mean" (there is also a geometric mean), it is by far the most commonly used. Therefore, if the term "mean" is used without specifying whether it is the arithmetic mean, the geometric mean, or some other mean, it is assumed to refer to the arithmetic mean. Median The median is also a frequently used measure of central tendency. The median is the midpoint of a distribution: the same number of scores are above the median as below it.

Trimmed Mean

A trimmed mean is calculated by discarding a certain percentage of the lowest and the highest scores and then computing the mean of the remaining scores. For example, a mean trimmed 50% is computed by discarding the lower and higher 25% of the scores and taking the mean of the remaining scores. The median is the mean trimmed 100% and the arithmetic mean is the mean trimmed 0%. A trimmed mean is obviously less susceptible to the effects of extreme scores than is the arithmetic mean. It is therefore less susceptible to sampling fluctuation than the mean for extremely skewed distributions. It is less efficient than the mean for normal distributions. Trimmed means are often used in Olympic scoring to minimize the effects of extreme ratings possibly caused by biased judges.