What are the key differences between primary and secondary sources?

Research is compilation of both primary as well as secondary sources. If researchers lack the knowledge of these two sources and their distinction, they would surely get stuck in knowing how to use them properly.  There are some basic and some intricate differences between the two which should be clear in the mind of the … Continue reading “What are the key differences between primary and secondary sources?”

Research is compilation of both primary as well as secondary sources. If researchers lack the knowledge of these two sources and their distinction, they would surely get stuck in knowing how to use them properly.  There are some basic and some intricate differences between the two which should be clear in the mind of the researcher without any ambiguity.

 Primary Sources:

As the name itself indicates, primary research is that research which is conducted at the time when the concerned study is being undertaken and the person who does the primary research is witness to the situation directly. Some of the key sources for primary research can be:

  1. Personal documents of the researcher, such as diaries, novels, email etc.
  2. Various types of documents that come from research studies such as thesis, experiments, data, reports and so on
  3. Original manuscripts or any kind of original documents, maps photographs or newspapers
  4. Movies, work of art or music pieces etc.

They are mostly in use when the researcher is studying a subject related to the past such as history. They bring up the opinion of the people who have a direct link from the past. Though, they come from first hand sources and hence can be called as genuine sources, they have one major concern of biased review as they aren’t anything more than the personal opinion of the author. Researchers who target to use primary sources, must ensure that they first analyse the information thoroughly, to identify and remove any biases if there in the study.

 

Secondary Sources:

 It is the other end of the primary source and has information that can be generalised because it is an evaluation and synthesis of information already existing. Usually the sources for secondary research that we see as getting used are:

  1. Books, textbooks, magazines, encyclopaedias etc.
  2. Documents /pictorials and videos based on history
  3. Bok reviews or peer reviews

Secondary sources have the privilege of making the document easier to understand for the reader as it is usually the generalisation of the content based on the analysis of some primary sources.  But, similar how there is a major drawback that is associated with primary sources, in secondary sources as well, the flip side is that the creator of these secondary sources is not an expert in the field of study and consequently because of narrow exposure to the topic may not be able to create a generalisation that can be trusted completely.

Whenever scholars use secondary sources, they must try to validate the information from other reliable sources before actually taking it as a generalisation

 

 

 

 

Hazards in Data Collection

It’s an uphill climb so it has to has its share of pitfalls. The journey of research is such. However, it is better to take it step by step, one thing at a time. Let me take the chance today to explain to you the hazards and pitfalls of data collection. The most common mistakes … Continue reading “Hazards in Data Collection”

It’s an uphill climb so it has to has its share of pitfalls. The journey of research is such. However, it is better to take it step by step, one thing at a time. Let me take the chance today to explain to you the hazards and pitfalls of data collection. The most common mistakes that are made by researchers are:

No impetus in the questionnaire: At the time of choosing the problem, as a researcher do not limit your vision to yourself. Broaden your vision and look towards whether the problem you wish to research upon is going to generate new insight in the phenomenon. The answers that you would seek from your research should be targeting more real rather than hypothetical problems.

Pursuing Fads: There are certain topics of research that remain popular in the market for a short duration. These are called as fads. Abstain as a researcher to get carried away towards these fads as your area of research. They have a short lived shelf life and they may die their own death before you even get to complete your research. Spend some time over choosing topics that have more time worthiness and can be of value over years to come.

Visionless data mining: Though the collection of data is a very minute step in the entire research process. But before commencing data collection, you have to ensure to undertake proper planning so as to avoid getting into a soup. All the data collection that is done without any proper planning may lead to imperfect, irrelevant and imperfect data which is wastage of time and effort. Knot up the key that abundance in data is not a substitute for quality in data.

Snowball Sampling: A Non-Probability Sampling Technique

We can explain the snowball sampling technique as a technique that is used by the researchers to identify the potential subjects where the possibility of locating the subjects is difficult. Researchers mostly use this kind of a technique when the sample for study is not very common or is confined to a relatively smaller subgroup … Continue reading “Snowball Sampling: A Non-Probability Sampling Technique”

We can explain the snowball sampling technique as a technique that is used by the researchers to identify the potential subjects where the possibility of locating the subjects is difficult.

Researchers mostly use this kind of a technique when the sample for study is not very common or is confined to a relatively smaller subgroup of the population. It is more like a chain referral In this kind of sampling, after having finished his observation on one sample, he asks for assistance from the observed sample to  help in  identifying people who have similar interest. It is pretty much like asking the one subject to nominate the next subject. The process continues like a chain reaction until the target of the sufficient number of subjects is not achieved.

 When the subjects want to observe a rare disease, the snowball technique may be a good option by the researcher to identify the sample and get recommendations from the subjects already under study for the next set of referrals. Observing one sample may lead to the next set of samples.

  There are different types of snowball sampling. The different types are:

  • Linear Snowball Sampling
  •  Exponential Non-Discriminative Snowball Sampling
  • Exponential Discriminative Snowball Sampling

Snowball Sampling comes with its own advantages and disadvantages. The main advantages are  that it allows the researcher to reach those populations that are difficult to sample when other sampling methods are being used. To add to it, it is a cheap and cost efficient method as  it does not require too much of prior planning and and workforce requirement as compared to other sampling techniques.

Snowball  sampling has its own set of disadvantages as well. There is very little or negligible control of the researcher over this kind of method as the new subjects of the study have the dependance on the previous subjects that have been observed. Secondly a generalised representation of the population cannot be assured in this case as the researcher cannot b e sure of the  correct distribution of the sample. Another disadvantage is that of the scope of sampling biasad when the existing subjects nominate new subjects thry go on the basis of familiarity and some preconceived thought process. It creates a great possiblity that  they would share similar traits and the researcher would only be able to obtain a very small group of the population.

Blunders in Data collection approach taken up by researchers

The process of research is flooded with a lot of problems and hazards that need to be considered. The novice or amateur researchers find it very challenging to take care of the problem areas of research, when they have out in so much energy and effort into the process. Some of the common errors that … Continue reading “Blunders in Data collection approach taken up by researchers”

The process of research is flooded with a lot of problems and hazards that need to be considered. The novice or amateur researchers find it very challenging to take care of the problem areas of research, when they have out in so much energy and effort into the process. Some of the common errors that the researchers make are:

Lack of challenge and interest generating content in the data collection tool/questionnaire: Sometimes it does happen that the researcher chooses a problem with a biased perspective but it lies beyond the interest of the scientific community. In that case it fails to generate new knowledge or insight for the investigated phenomenon. A whole lot of effort is invested in the research process from beginning till the end and hence it should be ensured that the research process does not go futile and real problems are dealt with instead of the hypothetical ones.

Following short lived trends: A very common mistake is this one, where often a researcher is seen undertaking studies that have more popularity and a very short span of shelf life and in the end the complete effort of the study becomes a waste as the topic does not hold much relevance after some duration. So many times it is seen that the fads die down much before the completion of  the research. Timeless topics are the best choice and researchers should be careful and aspire to choose such topics that stand strong in the test of time.

Unsighted data mining: Most of the novice researchers are seen going ahead with the process of data collection to begin with and keep the planning process for later. It is dangerous and hazardous for research to not know the means in which the data will be used before collecting it. A chain of other steps are necessary before the data collection is done and if that is not done, the data comes out to be irrelevant, not perfect and completely useless.  The researcher must imperatively understand this that huge quantity of data is not a compensation for unplanned technique and poor execution.