Data Analysis for Scientific Research, Lecture notes of Literature

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Data Analysis for

Scientific Research

BAE 815

Dr. Zifei Liu

In the old days Modern life

• Very limited data
  • More and more data

is available

How do we make decisions?

• Heavily relied on

experience

• Less and less

experience is needed

Experience Data Statistics Big data

Observations Questions Hypothesis Testing Theory Negative, revise hypothesis Answer Positive Classical scientific research

• Very likely, your research will involve data collection and analysis in order to test your hypothesis.
• Data is a universal language allowing scientists to work together no matter where they are and when do they live.
• Without careful data analysis to back up your conclusions, the results of your scientific research won't be taken seriously by other scientists. Why do I have to analyze data?

• Problem statement
  • What is a researchable question?
• Theory, assumptions, background literature
• Variables and hypotheses
• Research design and methodology
• Instrumentation, sampling
• Data analysis
• Conclusions, interpretations, recommendations, limitations Important components of empirical research

Elements of data analysis Research question Data/information (what?) Scientific reasoning/ argument (how?) Lesson/conclusion (so what?) Be accurate Working with uncertainties Be objective Separate facts and opinion Support argument with data Be careful and be honest about limitations Correlation does not always imply causation.

• Data requirements
  • Forms of data: text, numbers, images, audio, video.
  • Scales of data: nominal, ordinal, numerical.
  • Quantitative, qualitative, or mixed?
• Scope of study: case or sample?
  • What is your population of interest? How do you want to generalize your results?
  • How many data points do we need? Do they represent all the population we want to study? Before you collect your data

• Sampling errors (random, representative, nuisance factors)
• Validity (systematic error/bias), reliability (random error/bias)
• Accuracy, precision, reproducibility
  • Effective digit
• Standard deviation vs. standard error
• Propagation of errors
• Quantitative vs qualitative
• Statistical significance (P value) Working with uncertainties

• Check for completeness and accuracy of data, handle missing values, undetected values, duplicates, outliers, and correct errors
• Code, clean
• Initial data analysis: check and question the assumptions required for the following data analysis and hypothesis testing. - Linearity - Normality - Symmetry - Effect of uncommon observation
• Make transformations of variables as needed. Data processing

• Descriptive: How can the data be summarized?
• Exploratory/Inferential: focuses on discovering new features in the data and suggest new hypotheses. How can we draw inferences from the data?
• Confirmatory: focuses on confirming or falsifying existing hypotheses.
• Predictive: How can we build predictive models using the data available? Levels of data analysis

• Statement 1: A is a human being. B is a gorilla. Between and A and B are many similarities, but A has many superior attributes when compared with B.
• Statement 2: The similarities show that both A and B had a common origin. The superiorities suggest that A evolved from B over millions of years.
• Statement 3: The similarities show that both A and B had a common origin: the creator God. The superior attributes of A show that God chose to create human beings in His own image, and this was not the case with the creation of animals. Data and interpretation: Knowing the difference Credit: Elaine Kennedy

• Data are usually regarded as facts, and are used as a basis for reasoning, discussion, or calculation.
• As technology and science progress, “facts” will be discarded, modified, or replaced with new data
• Numbers do not speak for themselves.
• Interpretation demands fair and careful judgments. Often the same data can be interpreted in different ways. So it is helpful to involve others or take time to hear how different people interpret the same information.
• Much of the controversy that exists in the scientific literature is generated by a common problem: interpretations drawn from limited databases. Data interpretation

• Justifying the methodology; citing agreement with previous studies
• Offer an interpretation/explanation of the results
• Discussing limitations , pointing out discrepancies
• Commenting on the data; state the implications and recommend further research The results section of your paper/presentation There is some merit in indicating what you did not find, or what surprised you.
• Demonstrate your integrity and professionalism
• Chance to get useful feedback

• How to report results?
  • Tables, graphs, drawing, flow charts,

photographs, multimedia presentations …

• Think in terms of harmony, rhythm, flow, balance,

and focus.

• Think creatively to combine these elements

together to convey the essential conclusions to the

audience effectively.

Presentation and visualization of results