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Cat Cost Accouting

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1 views4 pages

Cat Cost Accouting

cat for cost accounting

Uploaded by

salsawriters
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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PRESBYTERIAN UNIVERSITY OF EAST AFRICA

P.O. Box 387-00902, Thogoto, Kikuyu Kenya. Phone 0723 799 904

website. www.puea.ac.ke Email. info@puea.ac.ke

email. kivindac@gmail.com Tel .0742295841

RESEARCH METHODS

Purpose: To enable the learner come up with researchable problem, collect data, analyze and
write research project report.

Expected Learning Outcomes

By the end of this course, the learner should be able to:

a) Identify research topic that is relevant and in line with the current issues in
their fields of study.

b) Develop a quality research proposal in line with the set standards

c) Explain on the different methods and techniques of data collection and


analysis and application of the one suitable for their study.

d) Present their proposal and research report

Course outline

Duration: 10 Weeks

Week 1: Introduction to Research

 History and importance of research

 Types of research (qualitative, quantitative, mixed methods)

 Research ethics and integrity

 Formulating research questions and hypotheses

Week 2: Research Design and Measurement

 Research design (exploratory, descriptive, experimental)

 Variables: Independent, dependent, control


 Measurement scales (nominal, ordinal, interval, ratio)

 Reliability and validity in research

Week 3: Sampling and Data Collection

 Sampling techniques (random, stratified, cluster, convenience)

 Sample size determination

 Primary vs. secondary data sources

 Survey design (questionnaires, interviews, observational methods)

Week 4: Scaling Techniques and Experiments

 Attitude scaling (Likert, Semantic Differential, Guttman)

 Experimental vs. non-experimental research

 Simulation in research

 Case study research

Week 5: Data Processing and Statistical Software

 Introduction to statistical tools (SPSS, R, Excel, Python)

 Data coding, cleaning, and transformation

 Descriptive statistics (mean, median, mode, SD, variance)

 Data visualization (graphs, charts, tables)

Week 6: Statistical Inference and Hypothesis Testing

 Probability distributions (normal, binomial)

 Confidence intervals and significance levels

 Parametric tests (t-test, ANOVA)

 Non-parametric tests (chi-square, Mann-Whitney U)

Week 7: Correlation and Regression Analysis

 Pearson and Spearman correlation

 Simple and multiple linear regression

 Interpretation of regression outputs

 Applications in different fields (business, social sciences, health)

Week 8: Advanced Analytical Techniques

 Time series and trend analysis


 Cluster analysis (grouping data)

 Factor analysis (dimension reduction)

 Brief introduction to predictive modeling

Week 9: Model Building and Simulation

 Basics of stochastic models

 Monte Carlo simulation

 Decision-making models

 Applications in finance, operations, and social sciences

Week 10: Research Communication and Reporting

 Structure of a research report (abstract, literature review, methodology, results,


discussion)

 Writing for academic vs. industry audiences

 Effective presentation of findings (visuals, executive summaries)

 Final project presentations and peer feedback

REFERENCES

1. Research Design & Methodology

o Creswell, J. W., & Creswell, J. D. (2023). Research Design: Qualitative,


Quantitative, and Mixed Methods Approaches (6th ed.). Sage.

 Covers all research paradigms, ethics, and writing proposals.

2. Statistics & Data Analysis

o Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). Sage.

 Hands-on guide to statistical tests with software examples.

o Moore, D. S., Notz, W. I., & Fligner, M. A. (2021). The Basic Practice of
Statistics (9th ed.). W.H. Freeman.

 Foundational stats for beginners; avoids heavy math.

3. Survey Design & Sampling

o Fowler, F. J. (2013). Survey Research Methods (5th ed.). Sage.

 Best practices for questionnaires, interviews, and sampling.

4. Advanced Analytics & Modeling


o Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.).
Pearson.

 Covers regression, factor analysis, cluster analysis, etc.

o Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and


Practice (3rd ed.). OTexts.

 Free online book on time series and forecasting


(https://otexts.com/fpp3/).

5. Data Visualization & Reporting

o Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.).


Graphics Press.

 Classic text on effective data visualization.

o Booth, W. C., Colomb, G. G., & Williams, J. M. (2016). The Craft of Research (4th
ed.). University of Chicago Press.

 Step-by-step guide to academic writing and structure.

Bonus: Free Online Resources

 OpenIntro Statistics (https://www.openintro.org/book/os/)

o Introductory stats with R examples.

 R for Data Science (https://r4ds.had.co.nz/)

o Free book on data analysis using R (Wickham & Grolemund, 2017).

Why These Books?

 Comprehensive: Cover all topics from Week 1 (research basics) to Week 10 (reporting).

 Interdisciplinary: Applicable to social sciences, business, health, and STEM.

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