STM1001 Lecture 1

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# STM1001 Lecture 1
## Science/Health stream
### La Trobe University

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# Lecture 1 (Science/Health stream)

## In this lecture, we will cover:

* An overview of the Science/Health stream

* jamovi: what you need to know and do before this week's core computer lab

* Summary of Topic 1B in preparation for this week's Sci/Health computer lab

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# Overview of the Science/Health stream

* While all students do core material in this subject, stream material is tailored towards your chosen course / discipline

* The core and stream topics covered are listed in the STM1001 Overview pdf document located on the LMS

* The main software program you will be learning how to use in this stream is [jamovi](https://www.jamovi.org).

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# Overview of the Science/Health stream

* In this subject, you will be expected to know how to ***carry out your own*** statistical analyses using **[jamovi](https://www.jamovi.org)**

* In addition, you will be expected to be able to ***interpret*** statistical analyses from **other software packages**

* For example, the core readings and some assessments will contain output produced from the statistical sofware package called [R](https://www.r-project.org/)

* For the most part, you will not be required to use R, but you will need to know how to interpret output from R

* Most packages provide the same or similar information in the output, and lots of examples are provided in the readings

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# Why jamovi?

Some of jamovi's advantages include:

* **It is free and open-source**, meaning you can install it on your computer for free and use it anywhere. Also, this means you can take the skills you learn in this subject anywhere in the future, as you will always be able to access jamovi for free

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* **It is user-friendly and intuitive to use.** With just a few clicks, you can carry out highly complex statistical analyses and produce professionally formatted results

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* **We make use of the computing power available to us**, meaning the use of mathematical formulae and doing statistics by hand with pen and paper are minimal in this subject

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* **It is much more powerful, and can carry out more sophisticated analyses, than spreadsheets**. And once you have carried out your analysis, it is reproducible - no need to repeat point-and-click work over and over again

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* **It uses arguably the best statistical programming language, R, in the background**. It also has an option to automatically generate R code for you as you go, as well as an option to write R code within the jamovi interface (we will have an opportunity to do this later in the semester). For those of you who go on to do more stats and/or data analysis in the future, familiarity with R will be very useful.

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# Why jamovi?

And perhaps the best reason of all: we make use of the computing power available to us.

Compare this...

![caption](data:image/png;base64,#Formulas.png)

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# Why jamovi?

To this...

![caption](data:image/png;base64,#jamovi.png)

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# Installing jamovi

### The following will assist:

* jamovi homepage: [jamovi.org](https://www.jamovi.org)
* If you encounter issues installing: [Troubleshooting (user guide)](https://www.jamovi.org/user-manual.html)
* Use jamovi via internet browser (including on iPads): [jamovi cloud](https://www.jamovi.org/cloud.html)
* Use jamovi via the virtual desktop (this will allow for more functionality than jamovi cloud): details on LMS

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# Kahoot!

### These are online quizzes that will pop up from 
### time to time during lectures.

### They are NOT Assessable - just for a bit of fun and 
### to help our learning.

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# Kahoot!

## Go to [kahoot.it](https://kahoot.it/) and use

## the code provided

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# Topic 1B: Research: An introduction

## In this week's readings:

* identify quantitative and qualitative research,
* identify the steps in the quantitative research process.

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# The purpose of research

Everyone in science-based disciplines must know the language, tools, concepts and ideas of research: research is the foundation of science.
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***Research seeks to confirm, refute or extend previous findings, and potentially reveal new findings***
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Scientific research formally answers questions that arise by observing the world using data; that is, science requires evidence-based answers.

While analysis of the data is often viewed as the hardest part of research, sometimes the hardest part is knowing what data to collect, and how to collect it (that is, the study design).

We study both the study design and the analysis of data in this subject.

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# Evidence-based research

'Evidence-based research' refers to research conclusions based on *evidence*, rather than researchers' hunches, feelings, intuition, hopes, or traditional practice.

Research conclusions are based on **evidence**, which comes from analysing the collected **data**.
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***Definition 1.1 (Data)*** Data refers to information (observations or measurements) obtained from a study, as numbers, labels, or text.
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***Definition 1.2 (Data set)*** A *data set* refers to a structured collection of data from a study.
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# Using software in research

Many people use spreadsheets (such as Microsoft Excel) for analysis of data in research.

Using spreadsheets requires extreme care; many extremely expensive and dangerous errors have been made due to using spreadsheets, including problems when reporting the [2020 COVID-19 pandemic](https://www.zdnet.com/article/excel-errors-microsofts-spreadsheet-may-be-hazardous-to-your-health/).

* Spreadsheets can *automatically change the entered data*,
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* Spreadsheets may include *formulas with errors*,
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* Spreadsheets *do not leave a record* of how the data have been analysed or prepared,
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* Excel has *bugs* [Keeling and Pavur 2004](https://bookdown.org/a_shaker/STM1001_Topic_1B_Sci/1-4-Software-In-Research.html#ref-keeling2004numerical).
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Statistical software packages such as **R**, **jamovi**, and **SPSS** can help us to avoid such problems:
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* They are designed for large data sets,
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* They allow for [ reproducible research](https://bookdown.org/pkaldunn/Book/academic-integrity.html#ReproducibleResearch),
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* They allow for a high level of precision in formatting and data visualisation,
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* With a little bit of programming, these software packages can be extremely powerful.

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# The components of research

The research process typically follows the process

We will be learning more about each of these steps throughout this subject. 
Go to [Week 1](https://bookdown.org/a_shaker/STM1001_Topic_1B_Sci/1-6-the-components-of-research.html) to learn more about the above steps.
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# Types of research

Research is a formal, evidence-based approach to learning or creating new information. Broadly speaking, the two main types of research are **qualitative** and **quantitative** research.

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# Types of research

**Qualitative research** leads to a deeper understanding of what is being studied, usually about a very narrowly-defined group. Meanings, motivations, opinions or themes often emerge from qualitative research.

**Quantitative research** summarises and analyses data using *numerical methods*, such as *averages* and *percentages*. Typically, information from a subset of the population (a sample) is used to infer information about a larger group (a population) in quantitative research.
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***Definition 1.3 (Quantitative research)***  Quantitative research summarises and analyses data using numerical methods, such as producing averages and percentages.
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# Example  (Types of research)

Suppose we wish to learn about why people do or do not buy electric cars (for example, see [Egbue and Long (2012)](https://bookdown.org/a_shaker/STM1001_Topic_1B_Sci/1-7-TypesOfResearch.html#ref-egbue2012barriers)).

A **qualitative research** study might:

* Interview a small group of people who have bought electric cars,

* Interview another small group of people who have bought non-electric cars.

A **quantitative research** study might survey a large number of buyers of electric and non-electric cars, and ask the buyers' age, sex, and type of car purchased.

The survey responses could be analysed by numerically summarising the ages and sex of car buyers, looking for relationships between age and whether an electric car was purchased, and reporting the percentage of respondents who select each of the five options of concerns about buying electric cars.

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# See you in the computer labs!

Don't forget to submit the **Week 1 checklist** on LMS before your computer labs!

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These notes have been prepared by Amanda Shaker and Illia Donhauzer and are partially based on material written by Peter K. Dunn. Material in these notes based on material written by Peter K. Dunn is licensed under a Creative Commons Attribution-Non Commercial-Share Alike License <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA </a>. The copyright for the remaining material in these notes resides with the authors named above, with the Department of Mathematical and Physical Sciences and with La Trobe University. Copyright in this work is vested in La Trobe University including all La Trobe University branding and naming. Unless otherwise stated, material within this work is licensed under a Creative Commons Attribution-Non Commercial-Non Derivatives License 
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