Research projects (A&F) surgical session topics (session 2) Spring 2026

Research projects (A&F) surgical session topics (session 2) Spring 2026

First of all: a checklist to evaluate acceptability of the research proposal and a note on how to refine research objectives in your proposal: an illustration. (also study session 1 teaching plan)

Also take a look at some examples of research objectives.

To begin with: a brief note on positivism. Then study the inductive and deductive approaches. Next, what is inferential statistics? (video)

The main theme is correlation analysis

1. On covariance [video]; demonstration of Excel

a. basic concept of correlation video: understanding correlation.
b. note the difference with causal relations. [also study the video on causal relationships]/ also a video on necessary vs sufficient conditions.

c. a video: types of variables (detailed).

d. on mediators and moderators & control variables in correlation modelling. (further discussion of the mediator under the topic of intervening variables)./ a brief note on the moderating variable.  Also study a video on confounding variables. Also a blog note on the terms used in correlation analysis: about variables./ a note on variable types.

e. Correlation analysis: its limitations.

f. How to include variables into the multiple regression formula?
1.1 scatter diagram
*** basic tutorial video on scatterplot and linear correlation
(also refer to: https://www.excel-easy.com/examples/trendline.html), and coefficient of determination. A related video on the coefficient of determination/ correlation; a video on coefficient of determination and another one on the coefficient of correlation (steps).
*** on Simpson's Paradox [video]; a note on Simpson's paradox.
1.2 Data->data analysis -> regression

2. Videos: 

2.1 * the normal distribution./ * understanding the central limit theorem./ standardized normal distribution./ standard error briefing 1/ standard error : briefing 2.

2.2 on P-value and significance test/ Z-tests (also see p-value and alpha).
Also see slides on P-value.

2.3 * video: one tailed and two tailed tests, and critical values.

3. Correlation hypothesis statements
3.1. Examples

3.2 hypothesis testing with correlation hypothesis statement: part 1

3.3 hypothesis testing with correlation hypothesis statement: part 2

3.4. a brief one on hypothesis testing with correlation hypothesis statement.

For formulation of one-tailed test, some clarification about the null hypothesis related to correlation:

a. for right tailed test, use b ≤ 0 [b less than or equal to 0]

b. for left tailed test, use b ≥ 0 [ b is greater than or equal to 0]

Also study this blog note on one-tailed test and two-tailed test.

3.5. A video on falsification.

3.6. For hypothesis testing (two-tailed test), if the p value is 4%, then on each side of the distribution curve, the p value is 2%.

3.7. For excel (multiple regression analysis), the reported p value is based on a 2-tailed test.

4. On multiple regression analysis

** Introductory video: simple linear regression./ do a linear regression on Excel.

4,1 multiple linear regression. part 1

4.2 multiple linear regression part 2.

4.3 multiple linear regression - evaluating basic models.

4.4 Interpreting Excel regression report. (also take a look at the topic of anova). (now study this online info also).

4.5 For students interested in panel data regression analysis, study this e-resource on panel research design.

5. On literature search to justify choice of variables to be used to formulate hypothesis statements: a practice note.

6. Other issues about writing your assignment

6.1. Write an introduction.

6.2. Paragraphs tips.

6.3. write a conclusion.

7. About population and sample

Defining the population and sample for a correlation analysis in an accounting and finance dissertation involves clearly delineating the scope of financial entities you wish to understand (population) and the specific, manageable data set you will analyze (sample). In this context, population is often not people, but companies, financial events, or time periods. 

1. Defining the Population

The population is the entire set of entities, companies, or data points you are interested in researching. It must be defined by specific, shared characteristics. 

·        Geographical/Market Scope: e.g., "All non-financial companies listed on the London Stock Exchange (LSE)."

·        Time Horizon: e.g., "During the period 2015–2025."

·        Industry Focus: e.g., "Within the technology sector."

·        Data Availability: e.g., "Having available data on Bloomberg or WRDS." 

Example Definition: “The target population for this study consists of all firms in the FTSE 100 Index that have published audited financial statements consecutively between 2018 and 2023.”. 

2. Defining the Sample

The sample is a subset of the population, specifically selected for data collection. In financial research, the sample is often selected using non-probability sampling, such as purposive (judgmental) sampling, because researchers typically focus on companies that meet specific criteria. 

·        Criteria for Selection: Clearly state your exclusion/inclusion criteria.

·        Representativeness: Explain why your sample represents the population. 

Example Definition: “A purposive sampling technique was used to select a final sample of 75 firms from the population based on the following criteria: (1) active trading throughout 2018–2023, (2) no missing data for key variables, and (3) a positive net income in at least four of the six years.”. 

e-resources:

Two videos on study of research validity: internal validity and external validity & internal validity.

Another topic: a collection of notes on literature review of an academic article.