STM1001 Topic 1B Readings

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# STM1001 [Topic 3B](https://bookdown.org/a_shaker/STM1001_Topic_3B_Sci_S/) Readings
## Designing a Study Part I
### La Trobe University
This presentation complements the [Topic 3B readings](https://bookdown.org/a_shaker/STM1001_Topic_3B_Sci_S/)

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# Topic 3B: Designing a Study Part I

## In this week's readings:

* we will begin by learning about different types of study designs,
* we will discuss sampling considerations.

You will learn to:

* design scientifically sound studies to answer simple quantitative research questions,
* design ethical studies,
* describe and identify retrospective, prospective and cross-sectional observational studies,
* describe and identify true experimental and quasi-experimental studies.

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# Three types of study designs

Three broad methods for obtaining data are to use:
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* *Descriptive* studies for answering Descriptive RQs;
* *Observational* studies for answering Relational RQs; or
* *Experimental* studies for answering Interventional RQs.

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**Example (Research design)** Suppose we wish to compare the effects of echinacea on the symptoms of the common cold.

How would we design such a study to collect the necessary data? What decisions would you need to make?]

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# Descriptive studies

Descriptive studies are used to answer descriptive RQs.

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**Definition (Descriptive study)** In a descriptive study, researchers only focus on collecting, measuring, assessing or describing an outcome in the population.]

<img src="data:image/png;base64,#images/desc.jpg" width="25%" height="15%" style="display: block; margin: auto;" />
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**Example (Descriptive study)** Consider this RQ:

For overweight men over 60, what is the average increase in heart rate after walking 400 metres?
The outcome is the average increase in heart rate. The response variable is the increase in heart rate for the individual men. No comparison being made between the participants: every man in the study is treated in the same way. This is a descriptive RQ, which can be answered by a descriptive study.
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# Observational studies

**Definition (Observational study)** In an observational study, researchers do not impose the comparison or connection upon those in the study to (potentially) change the response of the participants.

Broadly speaking, three types of observational studies exist:

* *Retrospective*: look into the past for the comparison;
* *Prospective*: look into the future for the outcome;
* *Cross-sectional*: obtain the outcome in the present.

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# Observational studies
In **retrospective studies**, the Outcome (and response variable) is observed now, and the researchers look back to see what Comparison/Connection group was in the past.

**Example** A study examined patients with and without sporadic motor neurone disease (SMND), and asked about past exposure to metals. The response (whether or not the respondent had SMND) is assessed now, and whether or not they had exposure to metals (explanatory) is assessed from the past.

In **prospective** studies, the Comparison/Connection (or explanatory variable) is determined now, and researchers look ahead to assess or measure the Outcome (or response) (e.g., Prospective cohort studies).

**Example** A study measures the softdrink consumption of men, and determined who experienced gout over the following 12 years. The response (whether or not the individuals experience gout) is determined in the future. The explanatory variable (the amount of softdrink consumed) is measured now.

In **cross-sectional** studies, both the Outcome (response) and Comparison/Connection (explanatory variable) are gathered now.

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# Experimental studies

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**Definition (Experiment)** In an *experimental* study (or an experiment), the researchers intervene to control the values of the explanatory variables (C) that are applied to the individuals. The researchers allocate treatments (i.e., apply the intervention).]
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**Definition (Treatments)** Treatments are the conditions of interest that those in the study can be exposed to (in the comparison/connection). In experiments, treatments are imposed by researchers.]

Two types of experimental studies are: *True experiments*,  *Quasi-experiments*.
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# Experimental studies

**Definition (True experiment)** In a true experiment, the researchers:

* allocate treatments to groups of individuals (i.e., decide the values of the Comparison/Connection used on the individuals), and
* determine who or what individuals are in those groups.
While these may not actually happen explicitly, they can happen conceptually.

The echinacea study could be designed as a *true experiment*. The researchers would allocate individuals to one of two groups, and then decide which group took echinacea and which group did not.

**Definition (Quasi-experiment)** In a quasi-experiment, the researchers:

* allocate treatments to groups of individuals (i.e., decide the values of the Comparison/Connection used on the individuals), but
* do not determine who or what individuals are in those groups.

The echinacea study could be designed as a quasi-experiment. The researchers would need to find (not create) two existing groups of people (say, from two different suburbs) then decide which group took echinacea and which group did not.

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# External validity
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**Definition (External validity)** Externally validity refers to the ability to generalise the results to other groups in the population, apart from the sample studied.]

**Example.** Suppose the population in a study is Queensland university students. The sample would be the students studied. The study is externally valid if the sample is a random sample from the population of students.

The results will not necessarily apply to Queensland residents, but this has nothing to do with externally validity. External validity concerns how the sample represents the intended population in the RQ, which is Queensland university students. The study is not concerned with all Queensland residents.

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#  The idea of sampling

A RQ implies that every member of the population should be studied (the P in POCI stand for 'population'). However, being able to do so is very rare because of cost, time, ethics, logistics or practicality. Hence, a subset of the population (a sample) is almost always studied.

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In research, the goal is to learn about the *population*, but only a *sample* can be studied. This subject is essentially about how to learn about a population based on an imperfect sample.]

**Example** A study (based on Lipton et al. (1998)) of the effect of aspirin in treating headaches cannot possibly use every single human alive who might one day wish to take aspirin.

Having seen that using a sample is necessary, other issues are raised:

* *How* can we learn something useful about the *whole* population if only *some* of that population is studied?
* *Which* individuals should be included in the sample?
* *How many* individuals should be included in the sample be?
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# Random sampling methods
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**Definition (Random)** In research and statistics, random means "determined completely by chance".]

The results obtained from a random sample probably generalise to the population from which the sample is drawn; that is, *random samples* are likely to produce *externally valid* studies. See the [readings](https://bookdown.org/a_shaker/STM1001_Topic_3B_Sci/2-3-types-of-sampling.html) to learn more about the four types of random sampling:

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# Non-random sampling methods

A *non-random sample* requires some kind personal input. Examples of non-random samples include:

* *Judgement sample*: Individuals are selected, based on the researchers' judgement, depending on whether the researcher thinks they are likely to be agreeable or helpful. For example, researchers may decided to survey people who are not in a hurry.
* *Convenience sample*: Individuals are selected because they are convenient for the researcher. For example, researchers may gather data from their family and friends.
* *Voluntary response* (self-selecting) sample: Individuals participate if they wish to. For example, a voluntary response survey, or a TV station call-in survey.

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Using a non-random sample means that the results may not generalise to the intended population: they probably do not produce externally valid studies.]

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# Internal validity
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**Definition (Internal validity)** Internally valid refers to the strength of the association between the outcome and the comparison/connection.]

**Example** In a study of treating depression in adults (Danielsson et al. 2014), three treatments were compared: exercise, basic body awareness therapy, or advice.

If any differences between the treatments were found, the researchers need to be confident that the differences were due to the treatment.

For this reason, the three groups were compared to ensure the groups were similar in terms of average ages, percentage of women, taking of anti-depressants, and many other aspects.

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# The explanatory variable

The explanatory variable may be associated with changes in the values of the response variable. However, it may not; after all, determining this is the purpose of the study.

If nothing else influenced the values of the response variable, life would be easy: Any change of a given size in the value of the explanatory variable would always result in a change of the same size in the value of the response variable.

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**Example (Explanatory variable)** In the typing-speed study (Example 2.4), the explanatory variable is the sex of the person. If nothing else influenced typing speed, all females would record the same typing speed every time, and all males would record the same typing speed every time. This is clearly unreasonable.]

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# Extraneous variables 
**Definition (Extranaeous variable)** An extraneous variable is any variable that is (potentially) associated with the response variable, but is not the explanatory variable.

**Definition (Confounding variable)** A confounding variable (or a confounder) is an extraneous variable associated with the response and explanatory variables.

**Definition (Confounding)** Confounding is when a third variable influences the relationship between the response and explanatory variable.

**Definition (Lurking variable)** A lurking variable is an extraneous variable associated with the response and explanatory variables (that is, is a confounding variable), but whose values are not measured, assessed, described or recorded in the study.
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# Have questions?

## Ask your computer lab demonstrator

## or discuss with your peers

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These notes have been prepared by Illia Donhauzer. They are based on material written by Peter K. Dunn. Unless otherwise stated, material within this work 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>
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