Direct Personal Interviews - Methods of Primary Data Collection

A face to face contact is made with the informants (persons from whom the information is to be obtained) under this method of collecting data.

The interviewer asks them questions pertaining to the survey and collects the desired information. Thus, if a person wants to collect data about the working conditions of the workers of the BHEL, Trichy, he would go to the factory, contact the workers and obtain the desired information. The information collected in this manner is first hand and also original in character.

There are many merits and demerits of this method, which are discussed as under:

Merits:

1. Most often respondents are happy to pass on the information required from them when contacted personally and thus response is encouraging.

2. The information collected through this method is normally more accurate because interviewer can clear doubts of the informants about certain questions and thus obtain correct information. In case the interviewer apprehends that the informant is not giving accurate information, he may cross-examine him and thereby try to obtain the information.

3. This method also provides the scope for getting supplementary information from the informant, because while interviewing it is possible to ask some supplementary questions which may be of greater use later.

4. There might be some questions which the interviewer would find difficult to ask directly, but with some tactfulness, he can mingle such questions with others and get the desired information. He can twist the questions keeping in mind the informant’s reaction. Precisely, a delicate situation can usually he handled more effectively by a personal interview than by other survey techniques.

5. The interviewer can adjust the language according to the status and educational level of the person interviewed, and thereby can avoid inconvenience and misinterpretation on the part of the informant.

Demerits:

1. This method can prove to be expensive if the number of informants is large and the area is widely spread.

2. There is a greater chance of personal bias and prejudice under this method as compared to other methods.

3. The interviewers have to be thoroughly trained and experienced; otherwise they may not be able to obtain the desired information. Untrained or poorly trained interviewers may spoil the entire work.

4. This method is more time taking as compared to others. This is because interviews can be held only at the convenience of the informants. Thus, if information is to be obtained from the working members of households, interviews will have to be held in the evening or on week end. Even during evening only an hour or two can be used for interviews and hence, the work may have to be continued for a long time, or a large number of people may have to be employed which may involve huge expenses.

Though there are some demerits in this method still we cannot say that it is not useful. The matter of fact is that this method is suitable for intensive rather than extensive field surveys. Hence, it should be used only in those cases where intensive study of a limited field is desired.
In the present time of extreme advancement in the communication system, the investigator instead of going personally and conducting a face to face interview may also obtain information over telephone. A good number of surveys are being conducted every day by newspapers and television channels by sending the reply either by e-mail or SMS. This method has become very popular nowadays as it is less expensive and the response is extremely quick. But this method suffers from some serious defects, such as (a) those who own a phone or a television only can be approached by this method, (b) only few questions can be asked over phone or through television, (c) the respondents may give a vague and reckless answers because answers on phone or through SMS would have to be very short.

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Methods of Collecting Primary Data

Primary data refers to original data that has been collected from the original source first hand. Data collected this way is called primary data. It may be obtained by applying any of the following methods:

1. Observation.     
2. Direct Personal Interviews.       
3. Indirect Oral Interviews.      
4. Information from Correspondents.      
5. Mailed Questionnaire Methods.      

6. Schedule Sent Through Enumerators.

Important properties of the Normal distribution

The normal distribution was first described by Abraham Demoivre (1667-1754) as the limiting form of binomial model in 1733. Normal distribution was rediscovered by Gauss in 1809 and by Laplace in 1812. Both Gauss and Laplace were led to the distribution by their work on the theory of errors of observations arising in physical measuring processes particularly in astronomy. Here I will show you some important properties of Normal distribution

1. The normal curve is “bell shaped” and symmetrical in nature. The distribution of the frequencies on either side of the maximum ordinate of the curve is similar with each other.

2. The maximum ordinate of the normal curve is at. Hence the mean, median and mode of the normal distribution coincide.

3. It ranges between to

4. The value of the maximum ordinate is

5. The points where the curve change from convex to concave or vice versa is at

6. The first and third quartiles are equidistant from median.

7. The area under the normal curve distribution are:

a. covers 68.27% area

b. covers 95.45% area

c. covers 99.73% area

8. When μ = 0 and σ = 1, then the normal distribution will be a standard normal curve. The probability function of standard normal curve is

The following table gives the area under the normal probability curve for some important value of Z.

Distance from the mean ordinate in Terms of ± σ	Area under the curve
Z = ± 0.6745	0.50
Z = ± 1.0	0.6826
Z = ± 1.96	0.95
Z = ± 2.00	0.9544
Z = ± 2.58	0.99
Z = ± 3.0	0.9973

9. All odd moments are equal to zero.

10. Skewness = 0 and Kurtosis = 3 in normal distribution.

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International Conference on Advanced Management on February 2018

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Some Key Abbreviations used in Statistics

FNR - False Negative Ratio
FPR - False Positive Ratio
iff  - if an only if
I.I.d. - independent and identically distributed
IRQ - inter-quartile range
pdf - probability density function
LSE - Least Square Error
ML - Maximum Likelihood
MSE - Mean Square Error
PDF – probability distribution function
RMS - Root Mean Square Error
r.v. - Random variable
ROC - Receiver Operating Characteristic
SSB - Between-group Sum of Squares
SSE - Error Sum of Squares
SSLF - Lack of Fit Sum of Squares
SSPE - Pure Error Sum of Squares
SSR - Regression Sum of Squares
xxiv - Symbols and Abbreviations
SST - Total Sum of Squares
SSW - Within-group Sum of Squares
TNR - True Negative Ratio
TPR - True Positive Ratio
VIF - Variance Inflation Factor

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Maulana Tariq Jameel at UMT University Lahore, Pakistan November 2017

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Tariq Jameel, commonly referred to as Maulana Tariq Jameel, is a Pakistani religious and Islamic scholar, preacher, and public speaker from Pakistan. He is listed as a popular speaker in the 2013/2014 edition of the book The Muslim 500. In May 2014, the International Human Rights Commission Chief Ambassador met Tariq Jameel along with Asad Bashir and Veena Malik at Dubai and thanked him for promoting the true picture of Islam.

Multivariate normality Tests with R - Mardia's Test, Henze-Zirkler, Royston

Most multivariate techniques, such as Linear Discriminant Analysis (LDA), Factor Analysis, MANOVA and Multivariate Regression are based on an assumption of multivariate normality. So, In this post, I am going to show you how you can assess the multivariate normality for the variables in your sample. The above test multivariate techniques can be used in a sample only when the variables follow a Multivariate normal distribution.

For this, you need to install a package called MVN Type install.packages("MVN")and then load the package using R command library(“MVN”)

There are 3 different multivariate normality tests available in this package

1.Mardia's Multivariate Normality Test

2.Henze-Zirkler's Multivariate Normality Test

3.Royston's Multivariate Normality Test

Let's discuss these test in brief here, I am using inbuilt trees data here data(“trees”). This data consists of 3 variables I.e Girth, Height and volume.

First, we use Mardia's test to verify the normality for the above data Type mardiaTest(trees) This will return the results of normality test with 3 variables in it. Data is not multivariate normal when the p-value is less than 0.05 .  When you want to check Multivariate normality of selected variables. Create a subset. Let’s create a subset under name trees1 that includes 1st and 3rd variables using the command Trees1<-trees[c(1,3)].

Now let's check normality of trees1 using Henze-Zirkler's Test Type hzTest(trees1) .

To use Royston's Multivariate Normality Test Type roystonTest(trees1). So, That is how you can test the multivariate normality of variables using R. Give your queries and suggestions in comment section below.

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