Qualitative Versus Quantitative

Qualitative Versus Quantitative?

Qualitative research is used to uncover and understand thoughts and opinions thus providing a basis for further decision making. Quantitative research is used to measure and predict, leading to a final course of action. So basically, qualitative research/data would deals with descriptions, the data which can be observed but not measure, for example, feeling about a product or of a specific people(just like the interview about friendship that we just listened to in the seminar). Quantitative research/data would deals with number and the data can be measured. For example, temperature is something that we can measure.
Quantitative research methods provide numerical information which enables us/experimenter to analyse the data so much easier than Qualitative. And it is often associated with empirical researches, and it is thought to be unsuitable for social science/psychology research, while social science/psychology is more focusing on in-depth information (info/data got form qualitative research) and which is more suitable for social science/psychology research. The very main difference between quantitative and qualitative methods is the flexibility.

Although information obtained from qualitative research is so much harder to analyse or to summaries than quantitative research, it does not mean it is not as scientific. Another main difference is that researchers in quantitative research are looking for statistical validity and hopefully reliability as well while qualitative researchers are seeking for saturation of one/some specific area/topic.

In conclusion, there is no such way to say which research method would be better than the other, it is all depending on one situation and what the researcher is looking for.

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Homework for TA (due 28th Oct)

These are the comments I have made for this week…






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Why is reliability important?

Blog (4th )

Why is reliability important?

What is reliability? (In statistics) Reliability is the consistency of a measure, often used to describe a test. A test is considered to be reliable if the researchers get the same/very similar results repeatedly. It is not possible to calculate reliability exactly, however there are different ways that we could estimate and/or increase the reliability of one test.

Reliability is very important to psychological tests, so does to our general life. Businesses would need to get reliable supplier, would need to ensure the reliability of their products…etc.  To attract new consumers and maintain the loyal consumers so their businesses could keep running and growing.

Here is a video talking and explaining why is reliability so import? (To the businesses and their logistics department)

In the video,The heads of Logistics of Tetra Pak, Addidas, M&S stated they really need everything is to reliable and they could not/do not want to afford the cost of something being not reliable.
And to the businesses, reliability is really important because they need to maintain/grow their Reputation, Customer Satisfaction, to reduce the Warranty Costs to the lowest, to meet Customer Requirements that are increasing by time, and to keep a Competitive Advantage.  In order to test the reliability of the product many businesses would hire a group of people to analysis it, for example RAL (stands for Reliability Analysis Laboratory). https://www.reliabilityanalysislab.com/homepage.asp

Back to Reliability in psychology/statistics, if your data from the test is not reliable, there is no point to publish it; it means the test must have something wrong, and for sure it cannot generalise to the whole or other populations. Reliability usually linked with Validity, Validity is the extent to which a test measures what it is supposed to measure. For the test to be valid it must be reliable; but reliability does not guarantee validity. All measurement procedures would have some potential errors (random error and/or systematic error), the aim of reliability is to reduce and minimise it. The most common types of reliability are test-retest reliability, inter-rater Reliability parallel-form reliability, and internal consistency reliability. Test-Retest Reliability is used to assess the consistency of a test/measure arcoss time. Inter-Rater Reliability is used to assess the degree to which two or more different raters give consistent estimates of the same test, the scores would then be compared to determine the consistency of the raters estimates. Parallel-Forms Reliability is used to assess the consistency of the results of two different tests created in the same content domain. Internal Consistency Reliability is used to measure the consistency of results across items within a test. http://changingminds.org/explanations/research/design/types_reliability.htm
This is a website explaining different types of reliability in a simple way.

However, at some extent reliability might become not as important as I have mentioned above, considering Freud’s theory about personality, it is not quite reliability and valid(at least to me). He suggested there are three elements of personality–known as the id, the ego and the superego–work together to create complex human behaviors. Basically, Id is the evil side of yourself,  Ego is the balance, and superego is the angel side. Whereas I do not think this would applied to me and some people, however this theory has been used a lot from the the 1920s till now. Therefore, there are always some special or different cases.


To explain why there are a lot of different ways to estimate/increase reliability of one test, it is because reliability is really important to a test and researchers would definitely love to get their research to the maximum reliability and validity.

In conclusion, reliability is important. Reliability is one of the most important elements of test quality. It has to do with the consistency, or reproducibility, of an examinee’s performance on the test. It would be representative if the test were reliable.

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Homework for TA ..

These are the comments I’ve made for this week:






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Validity – A process of which to ensure your findings.

(“Free” Topic this week…… even harder :S )

Validity – A process of which to ensure your findings.

Valid is…

  1. having some foundation; based on truth; legally acceptable; logic
  2. having legal efficacy or force; especially : executed with the proper legal authority and formalities <a valid contract>
  3. well-grounded or justifiable : being at once relevant and meaningful <a valid theory> logically correct <a valid argument> <valid inference>

(From http://www.merriam-webster.com/dictionary/valid and http://dictionary.reference.com/browse/valid)

It is very important for any study or research to have validity.  A test is valid when it actually measures what it intended to measure. Validity is the key to interpret the result, because without validity, the result would not be able to generalize and not representable.

Validity is usually assessed with reliability, however they are different.  If the result of a test is reliable, it means the result is consistent and the test can be replicated and will obtain similar results.

For a finding to be valid, it must be accurate and appropriate. [Using the appropriate statistical test to analyze the data]. Results can be reliable, but surprisingly, it might not be valid, this is due to factor A might not truly effected by factor B for example, but it just happens that either the it is a positive skewed or negative skewed graph, but yet, if results are not reliable, results are definitely Not valid, and of course, if results are not reliable, validity does not exist in this case.

Once, if your results are valid (tested it over and over again), it is reliable, representable enough to generalize the population, then you can be confident to draw our your findings as conclusion.

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Do you need statistics to understand your data?

(This is the redo version, guess it would be so much worse 😦  )

Here we go, another question, ”Do you need statistics to understand your data?”

What is Data?
Data is……

-individual facts, statistics, or items of information. Example: These data represent the results of our analyses. Data are entered by terminal for immediate processing by the computer.
-a body of facts; information.Example: Additional data is available from the president of the firm.
Data also refers to quantitative or qualitative attributes of a variable/set of variables.
Back to the topic…

Do you need statistics to understand your data?
With the first-hand data of your research/study, it is possible that you could still understand roughly what is the actual outcome.For instance, a product satisfaction survey received 130 replies that 82 participants out of 130 participants were satisfy with the product, so from this stage we could still tell that quite a lot(over a half) of participants were happy with the product,without doing any further statistical test or find out the % rate. 

However, when it comes to psychology data-analysis, without the ‘help’ of statistics it would become hard to analysis and interpret the results. WHY? Many psychology studies usually would have testing hypothesis (enable both experimenters/viewers to understand what is the study about and testing on.) For example, we have done a mini psychology project during A-Level, we have a jar of marbles and went around the college to ask student to guess how many marbles were there, with the condition of we ‘made-up’ some random numbers on the record sheet and the real participants could easily see the ‘previous answers’. We were testing whatever participants would affected by the ‘made-up’ answer. The testing hypothesis was H1 = the participants were affected by the ‘made-up’ answer. And the null hypothesis was H0 = the participants were not affected by it. We got two set of data, one group was done with the ‘made-up’ answer and the other group was done in normal condition. Then we used a statistical test(ANOVA test)[I cannot remember t clearly but should be ANOVA 🙂 ]to analyze it, the p was set p = 0.05(Critical Value = 5 %). It was significant that participants were affected by the ‘previous answer’ at the level of 5%.(The participants were affected by the ‘previous answer’ and it was significant too.) Without the statistical test, we might still be able to tell that participants were affected in the ‘made-up’ condition however it was not reliable, not presentable and it would not be significant.

In conclusion, we do need statistics to understand the data more in depth. Without the statistical data we could not be able draw out a reliable result. Having a valid statistical data would be supportive to the research.

(Please feel free to comment, discuss or debate again. It is a re-do version and I could not recall many important parts that I wrote in the first one, it is really depressing:'( I could feel its so much worse than the original one and its a hard topic to me too. But I tried my best to do the best I could in the 1/2 hours or less to finish it off :/ )

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I am so ….!”£$%^&*()_+ angry……and really upset.

THAT I JUST FINISHED MY BLOG but then its been clear or something….. so 500+ words gone.

I spent hours on it,

I can’t type again so yeah 0 marks:’D

The worst day ever.

End of story.

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