Sunday, December 2, 2018
Tuesday, March 20, 2018
Acta Diurna
Roman Emperor, Julius Ceaser is said to have started the first newspaper (something written on metal, stone, leather or cloth) in 59 BC to inform his people about the latest epidemic, war of the country, recruitment in military etc. It was called Acta Diurna, Acta Populi or Acta Senatus as well.
Such information were put on a cross road so that people moving from there could read them and know the latest happenings around the country. The Emperor had also managed a person near it to read out the information for the people who could not read and write themselves.
Acta Diurna, the Latin term means "the activities of the day". So major activities concerned to the public would be published and put them on the crossroad for everyone's access. It is taken as the first newspaper or mass media in the history of communication.
Wednesday, March 14, 2018
Wednesday, January 24, 2018
Sampling in Research
1. Probability Sampling
Probability samples are selected in such a way as to be representative of the population. They provide the most valid or credible results because they reflect the characteristics of the population from which they are selected (e.g., residents of a particular community, students at an elementary school, etc.
सम्भाव्य नमुना छनोट भन्नाले त्यस्तो नमुना छनोटलाई जनाऊँछ अनुसन्धान गरिनु पर्ने सम्पूर्ण जनसंख्यामा प्रत्येक एकाइको नमुनाका रुपमा छनोट हुने सम्भावना बराबर रहन्छ l
Types of Probability sampling
सामान्य नमुना छनोट मा अनुसन्धान कर्ताले सम्पूर्ण एकाईहरुलाई शुरुमा अंकित (Marking) गर्दछ र त्यस पछि गोला प्रथा द्वारा आफुलाई चाहिने संख्यामा नमुना चयन गर्दछ l अंकित (Marking) गरिने हुनाले यसरि छानिने नमुनाहरु एकपटक छानिएपछि दोहोरिने सम्भावना रहँदैन l
सामान्य नमुना छनोटमा अनुसन्धानकर्ताले सम्पूर्ण एकाई हरुलाई शुरुमा अंकित (Marking) गर्दछ र त्यस पछि पहिलो अंक आफैंले छानेर अंक गणितीय हिसाबले निश्चित अन्तराल को अंक हरु चाह्निन्छ, यसरि गणितीय हिसाबले छानिने भएकोले यसलाई भनिएको हो
2. Non Probability sampling
As they are not truly representative, non-probability samples are less desirable than probability samples. However, a researcher may not be able to obtain a random or stratified sample, or it may be too expensive. A researcher may not care about generalizing to a larger population. The validity of non-probability samples can be increased by trying to approximate random selection, and by eliminating as many sources of bias as possible.
Types of Non-Probability sampling
1. Quota sample
The defining characteristic of a quota sample is that the researcher deliberately sets the proportions of levels or strata within the sample. This is generally done to insure the inclusion of a particular segment of the population. The proportions may or may not differ dramatically from the actual proportion in the population. The researcher sets a quota, independent of population characteristics.
2. Purposive Sample
A purposive sample is a non-representative subset of some larger population and is constructed to serve a very specific need or purpose. A researcher may have a specific group in mind, such as high-level business executives. It may not be possible to specify the population — they would not all be known, and access will be difficult. The researcher will attempt to zero in on the target group, interviewing whomsoever is available.
A subset of a purposive sample is a snowball sample — so named because one picks up the sample along the way, analogous to a snowball accumulating snow. A snowball sample is achieved by asking a participant to suggest someone else who might be willing or appropriate for the study. Snowball samples are particularly useful in hard-to-track populations, such as truants, drug users, etc.
Convenience sample
A convenience sample is a matter of taking what you can get. It is an accidental sample. Although selection may be unguided, it probably is not random, using the correct definition of everyone in the population having an equal chance of being selected. Volunteers would constitute a convenience sample. Non-probability samples are limited with regard to generalization. Because they do not truly represent a population, we cannot make valid inferences about the larger group from which they are drawn. Validity can be increased by approximating random selection as much as possible and making every attempt to avoid introducing bias into sample selection.
Probability samples are selected in such a way as to be representative of the population. They provide the most valid or credible results because they reflect the characteristics of the population from which they are selected (e.g., residents of a particular community, students at an elementary school, etc.
सम्भाव्य नमुना छनोट भन्नाले त्यस्तो नमुना छनोटलाई जनाऊँछ अनुसन्धान गरिनु पर्ने सम्पूर्ण जनसंख्यामा प्रत्येक एकाइको नमुनाका रुपमा छनोट हुने सम्भावना बराबर रहन्छ l
Types of Probability sampling
- Simple Random sample-सामान्य नमुना छनोट
सामान्य नमुना छनोट मा अनुसन्धान कर्ताले सम्पूर्ण एकाईहरुलाई शुरुमा अंकित (Marking) गर्दछ र त्यस पछि गोला प्रथा द्वारा आफुलाई चाहिने संख्यामा नमुना चयन गर्दछ l अंकित (Marking) गरिने हुनाले यसरि छानिने नमुनाहरु एकपटक छानिएपछि दोहोरिने सम्भावना रहँदैन l
- Systematic Random sample-व्यवस्थित नमुना छनोट
सामान्य नमुना छनोटमा अनुसन्धानकर्ताले सम्पूर्ण एकाई हरुलाई शुरुमा अंकित (Marking) गर्दछ र त्यस पछि पहिलो अंक आफैंले छानेर अंक गणितीय हिसाबले निश्चित अन्तराल को अंक हरु चाह्निन्छ, यसरि गणितीय हिसाबले छानिने भएकोले यसलाई भनिएको हो
- Stratified sample
- Cluster Sampling
2. Non Probability sampling
As they are not truly representative, non-probability samples are less desirable than probability samples. However, a researcher may not be able to obtain a random or stratified sample, or it may be too expensive. A researcher may not care about generalizing to a larger population. The validity of non-probability samples can be increased by trying to approximate random selection, and by eliminating as many sources of bias as possible.
Types of Non-Probability sampling
1. Quota sample
The defining characteristic of a quota sample is that the researcher deliberately sets the proportions of levels or strata within the sample. This is generally done to insure the inclusion of a particular segment of the population. The proportions may or may not differ dramatically from the actual proportion in the population. The researcher sets a quota, independent of population characteristics.
2. Purposive Sample
A purposive sample is a non-representative subset of some larger population and is constructed to serve a very specific need or purpose. A researcher may have a specific group in mind, such as high-level business executives. It may not be possible to specify the population — they would not all be known, and access will be difficult. The researcher will attempt to zero in on the target group, interviewing whomsoever is available.
A subset of a purposive sample is a snowball sample — so named because one picks up the sample along the way, analogous to a snowball accumulating snow. A snowball sample is achieved by asking a participant to suggest someone else who might be willing or appropriate for the study. Snowball samples are particularly useful in hard-to-track populations, such as truants, drug users, etc.
Convenience sample
A convenience sample is a matter of taking what you can get. It is an accidental sample. Although selection may be unguided, it probably is not random, using the correct definition of everyone in the population having an equal chance of being selected. Volunteers would constitute a convenience sample. Non-probability samples are limited with regard to generalization. Because they do not truly represent a population, we cannot make valid inferences about the larger group from which they are drawn. Validity can be increased by approximating random selection as much as possible and making every attempt to avoid introducing bias into sample selection.
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