How to build a theoretical model

My note in March 2008 (from my research diary)

A model is a simplified (often mathematical) description of a system etc., to assist calculations and predictions.

Steps in building a theoretical model:-

1.  The variables to be used in characterising and understanding the process must be specified.
2. The forms of the relationships connecting these variables must be specified.
3. Ignorance and the need for simplicity will ensure that all relationships other than identities are subject to error and so, for purposes of efficient statistical estimation, these error terms must be specified.
4. The parameters of the model must be estimated and the extent of its identification ascertained; if this is inadequate, the model must be reformulated.
5. Finally, the model must be kept up to date and used, so that an impression can be formed of its robustness and reliability.

Theoretical models are of many kinds: static or dynamic; partial or complete; aggregated or disaggregated; deterministic or stochastic; descriptive or optimising.

如何把偶然扭转成必然？

偶然，可以是好的，也可以是不好的。

好的偶然英文叫serendipity，一般上被翻译为正面的意外。
不好的偶然在英语世界里和意外同义，叫accident。

要把偶然扭转成必然，这里头的前提是把偶然当作是件坏事，不然就不需要“扭转”。因为“扭转”一般上是把不好的事物变成好的。以此推演的话，“如何把偶然扭转成必然？”就是：How to turn accidentality into inevitability?

如果所谓的偶然实际上是正面的事物，那么“扭转”这个负面的动词就得由中性的“变”替代。
这样，“如何把偶然变成必然？”就是：How to turn serendipity into inevitability?

——————————
“如何把偶然扭转成必然？”这句话是我在看介绍日本著名男声优松岗祯丞的优管视频时听到的。松岗祯丞患有社交恐惧症、女性恐惧症和摄像头恐惧症，但是却成功地把自己面对的问题扭转成可以让他成名的声优事业。

我听到这句话时，想到的是怎么把Robest的偶然性发明变成有一定规律话必然性的发明。

Congratulations! Dr Shahrel Nizar Baharom

After slightly more than three years of hard working days and sleepless nights, my PhD student, Mr. Shahrel Nizar, a lecturer from UiTM is now Dr. Shahrel Nizar. On 14 July 2017, he passed his viva voce with minor correction.

Throughout his doctoral study, he went through numerous difficulties and challenges at his research work and personal life, but he overcame all of them, successfully. Well done Shahrel!

His success taught me a lesson:

Pursuing a PhD is not only about our IQ, it involves high EQ and consistent positive attitude as well.

I became a PhD supervisor in 2012. The first PhD student assigned to me chose to withdraw from her study after the orientation day. Thus, there was more failure than success stories. In fact, not all registered doctoral students can make to the end of the journey, but I think this low success rate would make those who are now successful more appreciated.

At heart, I am also learning as a supervisor. As my knowledge, skills and experience grow over time, I become more particular and selective when potential postgraduate students approach me, telling me they wish to be supervised by me. Like my late mentor, Assoc Prof Dr Stanley Richardson always told me in the past:

The first principle of war is the selection and maintenance of the aim. And the aim in research is the one indispensable results plus constraints. (Richardson, 2005). 

To set the right aim, we do need IQ. However, to maintain the aim over several years with a lot of unpredictable challenges at work and in life, we really need high EQ.

Now I understand why Stanley would spend quite some time with a potential PhD student, understanding his cognitive strengths and weaknesses, and more importantly, observing his attitudes and behaviours towards academic research, before accepting or rejecting the student. It was not about how well or how thick their PhD research proposals were written, it was really about whether they would bear the temptation of giving up their research at any point of a research timeline.

Shahrel's success is also an outcome of his disciplined behaviour and consistent attitude towards the research plan and timeline we both set and adjusted along his doctoral research journey. His success motivates me to supervise more dedicated students like him to achieve their success. At heart, this reminds me of my typical answer to "how to research":

Do the right thing at the right time and use the right method. 

I wish all my other postgraduate students can refer to Shahrel as a role model, especially his respect and commitment on his own research plan and timeline. Brovo Dr Shahrel Nizar!!

Reading Notes: Statistics 101 (Part 4)

Data Analysis

Two ways:
- Looking at the data graphically to see what the general trends in the data are, and - Fitting statistical models to the data.

Frequency distribution: a graph plotting values of observations on the horizontal axis, and the frequency with which each value occurs in the data set on the vertical axis (a.k.a. histogram).

Reading Notes: Statistics 101 (Part 3)

Data collection methods in experiment research
1. to manipulate the independent variable using different entities.
== a between-groups, between-subjects, or independent design.
2. to manipulate the independent variable using the same entities.
== this means that giving a group of students positive reinforcement for a few weeks and test their statistical abilities and then begin to give this same group punishment for a few weeks before testing them again, and then finally give them no motivator and test them for a third time.
== a within-subject or repeated-measures design.

Data collection method determines the type of test that is used to analyse the data.

Andy Field:
The reason why some people think that certain statistical tests allow causal inferences is that historically certain tests (e.g., ANOVA, t-tests, etc.) have been used to analyse experimental research, whereas others (e.g., regression, correlation) have been used to analyse correlational research (Cronbach, 1957)...these statistical procedures are, in fact, mathematically identical.

Two sources of variation:
Systematic variation: This variation is due to the experimenter doing something in one condition but not in the other condition.
Unsystematic variation: This variation results from random factors that exist between the experimental conditions (such as natural differences in ability, the time of day, etc.).

In a repeated-measures design, differences between two conditions can be caused by only two things:
(1) the manipulation that was carried out on the participants, or
(2) any other factor that might affect the way in which an entity performs from one time to the next.
== The latter factor is likely to be fairly minor compared to the influence of the experimental manipulation.

In an independent design, differences between the two conditions can also be caused by one of two things:
(1) the manipulation that was carried out on the participants, or
(2) differences between the characteristics of the entities allocated to each of the groups.
== The latter factor in this instance is likely to create considerable random variation both within each condition and between them.

When we look at the effect of our experimental manipulation, it is always against a background of ‘noise’ caused by random, uncontrollable differences between our conditions.

In a repeated-measures design this ‘noise’ is kept to a minimum and so the effect of the experiment is more likely to show up.

This means that, other things being equal, repeated-measures designs have more power to detect effects than independent designs.

The two most important sources of systematic variation in repeated-measures design are:
Practice effects: Participants may perform differently in the second condition because of familiarity with the experimental situation and/or the measures being used.

Boredom effects: Participants may perform differently in the second condition because they are tired or bored from having completed the first condition.

Randomization: the process of doing things in an unsystematic or random way. In the context of experimental research the word usually applies to the random assignment of participants to different treatment conditions.

Reading Notes: Statistics 101 (Part 2)

Source: Andy Field
Correlational or cross-sectional research: observe what naturally goes on in the world without directly interfering with it.
- by either taking a snapshot of many variables at a single point in time, or
- by measuring variables repeatedly at different time points (known as longitudinal research).
== provides a very natural view of the question we’re researching because we are not influencing what happens and the measures of the variables should not be biased by the researcher being there (an important aspect of ecological validity).
== tells us nothing about the causal influence of variables.
- Variables are often measured simultaneously.
- The first problem with doing this is that it provides no information about the contiguity between different variables.
- The second problem with correlational evidence: the tertium quid (‘a third person or thing of indeterminate character’).
== E.g., a correlation has been found between having breast implants and suicide (Koot, Peeters, Granath, Grobbee, & Nyren, 2003).
== However, it is unlikely that having breast implants causes you to commit suicide – presumably, there is an external factor (or factors) that causes both; for example, low self-esteem might lead you to have breast implants and also attempt suicide.
== These extraneous factors are sometimes called confounding variables or confounds for short.

Experimental research: manipulate one variable to see its effect on another.
- Even when the cause–effect relationship is not explicitly stated, most research questions can be broken down into a proposed cause and a proposed outcome.
- Both the cause and the outcome are variables.
- The key to answering the research question is to uncover how the proposed cause and the proposed outcome relate to each other.

David Hume said that to infer cause and effect:
(1) cause and effect must occur close together in time (contiguity);
(2) the cause must occur before an effect does; and
(3) the effect should never occur without the presence of the cause.

- These conditions imply that causality can be inferred through corroborating evidence: cause is equated to high degrees of correlation between contiguous events.

- The shortcomings of Hume’s criteria led John Stuart Mill (1865) to add a further criterion: that all other explanations of the cause–effect relationship be ruled out.
== Mill proposed that, to rule out confounding variables, an effect should be present when the cause is present and that when the cause is absent the effect should be absent also.
== Mill’s ideas can be summed up by saying that the only way to infer causality is through comparison of two controlled situations: one in which the cause is present and one in which the cause is absent.

- This is what experimental methods strive to do: to provide a comparison of situations (usually called treatments or conditions) in which the proposed cause is present or absent.
- Example: the effect of motivators on learning about statistics. Randomly split some students into three different groups in which teaching styles vary in the seminars:
== Group 1 (positive reinforcement): praise participants
== Group 2 (punishment): give verbal punishment
== Group 3 (no motivator): give neither praise or punishment, i.e. give no feedback at all.

Manipulated variable or independent variable: the motivator (positive reinforcement, punishment or no motivator).
Interested outcome or dependent variable: statistical ability, to be measured via a statistics exam after the last seminar.
Assumption: the scores will depend upon the type of teaching method used (the independent variable).
Inclusion of the ‘no motivator’ group: proposed cause (motivator) is absent, and we can compare the outcome in this group against the two situations in which the proposed cause is present.

If the statistics scores are different in each of the motivation groups (cause is present) compared to the group for which no motivator was given (cause is absent) then this difference can be attributed to the type of motivator used.
In other words, the motivator used caused a difference in statistics scores.

Proposal Defence

The key problem faced by my students in Proposal Defence sessions was the lack of defence.

When they were questioned or challenged, they noded their heads, accepting the comments and doubts held by panel examiners, as opposed to argue with the examiners and clarify doubts.

Another problem they normally had was the lack of coherence, esp between the problem statement, research objectives and research questions.

For those who don't have learning experience in foundation research course, i.e. having q Master's degree by completing course work instead of research project, this would make then having serious problem in research.