3 Types of study designs

You have learnt how to ask a RQ. In this chapter, you will learn to:

  • identify and describe the types of quantitative research studies.
  • compare and contrast experimental and observational studies.
  • describe and identify the directionality in observational studies.
  • describe and identify true experimental and quasi-experimental studies.
  • explain external and internal validity.

3.1 Introduction

Chapter 2 introduced four types of research questions: descriptive, relational, repeated-measures and correlational. This chapter discusses the types of research studies needed to answer descriptive, relational and repeated-measures RQs, while Chaps. 4 to 10 discuss the details of designing these studies and collecting the data. Many of the ideas also apply to correlational studies.

3.2 Three types of study designs

The RQ implies what data must be collected from the individuals in the study (the response and explanatory variables)... but the data can be collected in many different ways. Different types of studies are used to answer different types of RQs:

  • descriptive studies (Sect. 3.3) answer descriptive RQs;
  • observational studies (Sect. 3.4) answer RQs with a comparison, that do not have an intervention; and
  • experimental studies (Sect. 3.5) answer RQs with a comparison, that do have an intervention.

Observational and experimental studies are sometimes collectively called analytical studies.

3.3 Descriptive studies

Descriptive studies answer descriptive RQs (Fig. 3.1).

Definition 3.1 (Descriptive study) Descriptive studies answer descriptive research questions, and do not study relationships between variables.

A descriptive study, used to answer a descriptive RQ

FIGURE 3.1: A descriptive study, used to answer a descriptive RQ

Example 3.1 (Descriptive study) A study in Hong Kong determined the percentage of people wearing face-masks under different circumstances (L. Y. Lee et al. 2020). This is a descriptive study, where the population is 'residents of Hong Kong', and the outcome is (for example) 'the percentage who wear face masks when taking care of family members with fever'.

We do not explicitly discuss descriptive studies further, as the necessary ideas are present in the discussion of observational and experimental studies.

3.4 Observational studies

Observational studies (Fig. 3.2) answer relational and repeated-measures RQs without an intervention. They are commonly-used, and sometimes are the only study design possible.

Definition 3.2 (Observational study) Observational studies answer research questions with a comparison, but without an intervention.

An observational study

FIGURE 3.2: An observational study

Definition 3.3 (Condition) Conditions: The conditions are the values of the comparison that those in the observational study experience, but are not imposed by the researchers.

Example 3.2 (Observational study) Consider again this one-tailed, decision-making RQ (see Sect. 2.8):

Among Australian teens with a common cold, is the average duration of cold symptoms shorter for teens taking a daily dose of echinacea compared to teens taking no medication?

This RQ has a between-individuals comparison, so is a relational RQ. If the researchers do not impose the taking of echinacea (that is, the individuals make this decision themselves), the study is observational. The two conditions are 'taking echinacea', and 'not taking echinacea' (Fig. 3.3).

Observational studies with a between-individuals comparison. The dashed lines indicate steps not under the control of the researchers

FIGURE 3.3: Observational studies with a between-individuals comparison. The dashed lines indicate steps not under the control of the researchers

Example 3.3 (Within-individuals relational study) D. A. Levitsky, Halbmaier, and Mrdjenovic (2004) conducted a study where the weights of university students were recorded both at the beginning university, and then after \(12\) weeks. The comparison is within individuals, and the study is a repeated measures (paired) study. Since the researchers do not impose anything on the students (they just measure their weight at two time points), there is no intervention (Fig. 3.4). The outcome is the average weight. The response variable is the weight of individuals. The within-individuals comparison is the number of weeks after university started (\(0\) and \(12\)).

Observational studies with a within-individuals comparison

FIGURE 3.4: Observational studies with a within-individuals comparison

3.5 Experimental studies

Experimental studies (Fig. 3.5), or experiments, are used to study relationships with an intervention, and are commonly-used. Well-designed experimental studies can establish a cause-and-effect relationship between the response and explanatory variables. However, using experimental studies is not always possible.

Definition 3.4 (Experiment) Experimental studies (or experiments) answer RQs answer research questions with a comparison with an intervention.

Definition 3.5 (Treatments) The treatments are the values of the comparison that the researchers impose upon the individuals in the experimental study.

In an experimental study, the unit of analysis (Def. 2.14) is the smallest collection of units of observations that can be randomly allocated to separate treatments.

An experimental study

FIGURE 3.5: An experimental study

Example 3.4 (Within-individuals experimental study) Consider this estimation RQ:

For obese men over \(60\), what is the average increase in heart rate after walking \(400\) m?

This RQ uses a within-individuals comparison (before and after walking \(400\) m) so is a repeated-measures (and paired) RQ. There is an intervention if researchers impose the \(400\) m walk on the subjects. The outcome is the average heart rate. The response variable is the heart rate for each individual man.

Experimental studies with a within-individuals comparison

FIGURE 3.6: Experimental studies with a within-individuals comparison

Between-individuals experimental studies can be either true experiments (Sect. 3.5.1 or quasi-experiments (Sect. 3.5.2); see Table 3.1.

TABLE 3.1: Comparing analytical designs (descriptive studies do not have any comparison (C))
Study type Individuals allocated to receive the comparison? Individuals allocated to treatments Reference
Observational No No Sect. 3.4
True experiment Yes Yes Sect. 3.5.1
Quasi-experiment No Yes Sect. 3.5.2

3.5.1 True experimental studies

True experiments are commonly used to answer relational RQs (with a between-individuals comparison), but are not always possible. An example of a true experiment is a randomised controlled trial, often used in drug trials.

Definition 3.6 (True experiment) In a true experiment, the researchers:

  1. allocate treatments to groups of individuals (i.e., determine the values of the explanatory for the individuals), and
  2. determine who or what individuals are in those groups.

While these may not happen in these explicit steps, they can happen conceptually.

Example 3.5 (True experiment) The echinacea study (Sect. 2.7) 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 (Fig. 3.7).

These steps may happen implicitly: researchers may allocate each person at random to one of the two groups (echinacea; no echinacea). This is still a true experiment, since the researchers could decide to switch which group receives echinacea; ultimately, the decision is still made by the researchers.

True experimental studies: researchers allocate individuals to groups, and treatments to groups

FIGURE 3.7: True experimental studies: researchers allocate individuals to groups, and treatments to groups

3.5.2 Quasi-experimental studies

Quasi-experiments are similar to true experiments (i.e., answer relational RQs) where treatments are allocated to groups that already exist (e.g., may be naturally occurring).

Definition 3.7 (Quasi-experiment) In a quasi-experiment, the researchers:

  1. allocate treatments to groups of individuals (i.e., allocate the values of the explanatory variable to the individuals), but
  2. do not determine who or what individuals are in those groups.

Example 3.6 (Quasi-experiments) The echinacea study (Sect. 2.7) could be designed as a quasi-experiment. The researchers could find (not create) two existing groups of people (say, from Suburbs A and B), then decide to allocate people in Suburb A to take echinacea, and people in Suburb B to not take echinacea (Fig. 3.8).

Quasi-experimental studies: researchers do not allocate individuals to groups, but do allocate treatments to groups. The dashed lines indicate steps not under the control of the researchers.

FIGURE 3.8: Quasi-experimental studies: researchers do not allocate individuals to groups, but do allocate treatments to groups. The dashed lines indicate steps not under the control of the researchers.

Example 3.7 (Quasi-experiments) A researcher wants to examine the effect of an alcohol awareness program (M. MacDonald 2008) on the average amount of alcohol consumed per student in a university Orientation Week. She runs the program at University A only, then compares the average amount of alcohol consumed per person at two universities (A and B).

This study is a quasi-experiment since the researcher did not (and can not) determine the groups: the students (not the researcher) would have chosen University A or University B for many reasons. However, the researcher did decide whether to allocate the program to University A or University B.

3.6 Comparing study types

Different RQs require different study designs. In experimental studies, researchers create differences in the values of the explanatory variable through allocation, and then note the effect this has on the values of the response variable. In observational studies, researchers observe differences in the values of the explanatory variable, and observe the values of the response variable.

Importantly, only well-designed true experiments can show cause-and-effect. Nonetheless, well-designed observational and quasi-experimental studies can provide evidence to support cause-and-effect conclusions, especially when supported by other evidence. Although only experimental studies can show cause-and-effect, experimental studies are often not possible for ethical, financial, practical or logistical reasons.

The advantages and disadvantages of each study type are discussed later (Sect. 9.2), after these study types are discussed in greater detail in the following chapters.

Example 3.8 (Cause and effect) Many studies report that the bacteria in the gut of people on the autism spectrum is different than the bacteria in the gut of people not on the autism spectrum (Kang et al. (2019), Ho et al. (2020)), and suggest the bacteria may contribute whether a person is autistic. These studies were observational, so the suggestion of a cause-and-effect relationship may be inaccurate.

Other studies (Yap et al. 2021) suggest that people on the autism spectrum are more likely to be 'picky eaters', which contributes to the differences in gut bacteria.

The animation below compares observational, quasi-experimental and true experimental designs.

The three main study designs

FIGURE 3.9: The three main study designs

3.7 Directionality

Analytical research studies (observational; experimental) can be classified by their directionality (Table 3.2):

  • Forward direction (Sect. 3.7.1): The values of the explanatory variable are obtained, and the study determines what values of the response variable occur in the future. All experimental studies have a forward direction.
  • Backward direction (Sect. 3.7.2): The values of the response variable are obtained, then the study determines what values of the explanatory variable occurred in the past.
  • No direction (Sect. 3.7.3): The values of the response and explanatory variables are obtained at the same time.

Directionality is important for understanding cause-and-effect relationships. If the comparison occurs before the outcome is observed, a cause-and-effect relationship may be possible. That is, studies with a forward direction are more likely to provide evidence of causality.

TABLE 3.2: Classifying observational studies. (All experimental studies have a forward direction.)
Type Explanatory variable Response variable
Forward direction When study begins Determined in the future
Backward direction Determined from the past When study begins
No direction When study begins When study begins

Example 3.9 (Directionality) In South Australia in 1988--1989, \(25\) cases of legionella infections (an unusually high number) were investigated (O’Connor et al. 2007). All \(25\) cases were gardeners.

Researchers compared \(25\) people with legionella infections with \(75\) similar people without the infection. The use of potting mix in the previous four weeks was associated with an increase in the risk of contracting illness of about \(4.7\) times.

This study has a backward direction: people were identified with an infection, and then the researchers looked back at past activities.

Research studies are sometimes described as 'prospective' or 'retrospective', but these terms can be misleading (Ranganathan and Aggarwal 2018) and their use not recommended (Vandenbroucke et al. 2014).

Experimental studies always have a forward direction. Observational studies may have any directionality, and are sometimes given different names accordingly.

3.7.1 Forward-directional studies

All experimental studies have a forward direction, and include randomised controlled trials (RCTs) and clinical trials.

Observational studies with a forward direction are often called cohort studies. Both experimental studies and cohort studies can be expensive and tricky: tracking a group of individuals (a cohort) into the future is not always easy, and the ability to track some individuals into the future may be lost (drop outs) as (for example) plants or animals die, or people move or decide to no longer participate, etc. Forward-directional observational studies:

  • may add support to cause-and-effect conclusions, since the comparison occurs before the outcome (only well-designed experimental studies can establish cause-and-effect).
  • can examine many different outcomes in one study, since the outcome(s) occur in the future.
  • can be problematic for rare outcomes, as the outcome of interest may not appear (or may appear rarely) in the future.

Example 3.10 (Forward study) Chih et al. (2018) studied dogs and cats who had been recommended to receive intermittent nasogastric tube (NGT) aspiration for up to \(36\) hrs. Some pet owners did not give permission for NGT, while some did; thus, whether the animal received NGT was not determined by the researchers (so this study is observational). The researchers then observed whether the animals developed hypochloremic metabolic alkalosis (HCMA) in the next \(36\) hrs.

Since the explanatory variable (whether NGT was used or not) was recorded at the start of the study, and the response variable (whether HCMA was observed or not) was determined within the following \(36\) hrs, this study has a forward direction.

3.7.2 Backward-directional studies

Observational studies with a backward direction are often called case-control studies. Researchers find individuals with specific values of the response variable (the cases and the controls), and determine values of the explanatory variable from the past. Case-control studies:

  • only allow one outcome to be studied, since individuals are chosen to be in the study based on the value of the response variable of interest.
  • are useful for rare outcomes: the researchers can purposely select large numbers with the rare outcome of interest.
  • do not effectively eliminate other explanations for the relationship between the response and explanatory variables (called confounding; Def. 6.5).
  • may suffer from selection bias (Sect. 5.10), as researchers try to locate individuals with a rare outcome.
  • may suffer from recall bias (Sect. 10.2.2) when the individuals are people: accurately recalling the past can be unreliable.

Example 3.11 (Backwards study) A study (Pamphlett 2012) examined patients with and without sporadic motor neurone disease (SMND), and asked about past exposure to metals.

The response variable (whether or not the respondent had SMND) is assessed when the study begins, and whether or not they had exposure to metals (explanatory variable) is determined from the past. This observational study has a backward direction.

3.7.3 Non-directional studies

Non-directional observational studies are called cross-sectional studies. Cross-sectional studies:

  • are good for findings associations between variables (which may or may not be causation).
  • are generally quicker and cheaper than other types of studies.
  • are not useful for studying rare outcomes.
  • do not effectively eliminate other explanations for the relationship between the response and explanatory variables (called confounding; Def. 6.5).

Example 3.12 (Non-directional study) A study (J. Russell et al. 2014) asked older Australian their opinions of their own food security, and recorded their living arrangements. Individuals' responses to both both the response variable and explanatory variable were gathered at the same time. This observational study is non-directional.

3.8 Internal validity

Well-designed studies allow the researchers to focus on the relationship of interest, and to eliminate other possible explanations for changes in the value of the response variable apart from this relationship. A study which does this is said to have good internally validity.

Definition 3.8 (Internal validity) Internally validity refers to the extent to which a cause-and-effect relationship can be established in a study, that cannot be otherwise explained.

A study with high internal validity shows that the changes in the response variable can be attributed to changes in the explanatory variables; other explanations have been ruled out.

Studies with high internal validity show that changes in the response variable can confidently be related to changes in the explanatory variable in the group that was studied; the possibility of other explanations has been minimised.

In contrast, studies with low internal validity leave open other possibilities, apart from changes in value of the explanatory variable, to explain changes in the value of the response variable. Experimental studies usually have higher internal validity than observational studies.

Ideally, all studies should be designed to be internally valid as far as possible. For this reason, internal validity is studied in more detail in Chaps. 7 (for exerimental studies) and 8 (for observational studies).

Example 3.13 (Low internal validity) In a review of studies that used double-fortified salt to manage iodine and iron deficiencies (L. M. Larson et al. 2021), one conclusion was (p. 265):

Internal validity of the efficacy trials was generally weak [...] because of issues around selection bias, unaccounted confounders, and participant withdrawals.

One of many potential threats to internal validity is that the groups being compared are initially different; for example, the group receiving echinacea is younger (on average) than the group receiving no medication. This is a form of confounding (Def. 6.5).

The comparison groups are often compared at the start of the study: the groups being compared should be as similar as possible, so that any differences in the outcome cannot be attributed to pre-existing difference in the groups.

Example 3.14 (Baseline characteristics) 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 in the outcomes of patients receiving the different 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.

An internally valid study requires studies to be carefully designed, as discussed in Chaps. 7 and 8. In general, well-designed experimental studies are more likely to be internally valid than observational studies.

3.9 External validity

Apart from being internally valid, the conclusions from the study of a sample should apply to the intended population. This is called external validity.

A study is externally valid if the results of the study are likely to generalise to the rest of the population, beyond just those studied in the sample. To be externally valid, a study first needs to be internally valid, since the results must at least be sound for the group under study before being extended to other members of the population.

Using a random sample helps ensure external validity. In addition, the use of inclusion and exclusion criteria (Sect. 2.2.1) helps clarify to whom or what the results may apply.

Definition 3.9 (External validity) External validity refers to the ability to generalise the results to the rest of the population, beyond just those in the sample studied. For a study to be truly externally valid, the sample must be a random sample (Chap. 5) from the population.

External validity does not mean that the results apply more widely than the intended population.

Example 3.15 (External validity) Suppose the population in a study is Californian university students. The sample comprises the Californian university students actually studied by the researchers. The study is externally valid if the sample is a random sample from the population of all Californian university students.

The results will not necessarily apply to university students outside of Californian (though they may), or all Californian residents. However, this is irrelevant for external validity. External validity concerns how the sample represents the intended population in the RQ, which is Californian university students. The study is not concerned with all Californian residents, or with non-Californian university students.

3.10 The importance of design

Choosing the type of study is only one part of research design. Planning the data collection process, and actually collecting the data, is still required. Sometimes, data may be already available (called secondary data), and sometimes it may need collecting (called primary data).

Either way, how the data are obtained is important. The design phase is concerned with planning the best approach to obtaining the data, to ensure the study is internally and externally valid, as far as possible.

Designing a study to maximise internal validity means:

  • identifying what else might influence the values of the response variable, apart from the explanatory variable (Chap. 6); and
  • designing the study to be effective (Chaps. 7 and 8).

Designing a study to maximise external validity means:

  • identifying who or what to study, since the whole population cannot be studied (Chap. 5); and
  • determining how many individuals to study. (We need to learn more before we can answer this critical question in Chap. 30.)

The details of how the data will be collected (Chap. 10) and ethical issues (Chap. 4) must also be considered. Furthermore, the limitations of the study must be communicated (Chap. 9).

The following short (humourous) video demonstrates the importance of understanding the design!

3.11 Chapter summary

Three types of research studies are: Descriptive studies (for descriptive RQs), observational studies (for relational or repeated-measures RQs without an intervention), and experimental (for relational or repeated-measures RQs with an intervention).

Observational studies can be classified as having a forward direction (cohort studies), backward direction (case-control studies), or no direction (cross-sectional studies). Experimental studies always have a forward direction. Relational RQs with an intervention can be classified as true experiments or quasi-experiments. Cause-and-effect conclusions can only be made from well-designed true experiments.

Ideally studies should be designed to be internally and externally valid. In general, experimental studies have better internal validity than observational studies.

The following short videos may help explain some of these concepts:

3.12 Quick review questions

  1. A study (Fraboni et al. 2018) examined the 'red-light running behaviour of cyclists in Italy'. This study is most likely to be:
  2. When the results of studying a sample apply to the wider population of interest, the study is called:
  3. In a quasi-experiment, the researchers allocate treatments to groups that they cannot manipulate. True or false?
  4. What is the difference between an true experiment and a quasi-experiment?
  1. Which of the following are true?

    1. True experiments generally have a higher internal validity than observational studies.
    2. Internal validity refers to the strength of the inferences made from the study.
    3. External validity refers to the ability to generalise the results to other groups apart from those studied.
    4. Inclusion and exclusion criteria can be used to clarify the internal validity.
    5. Observational studies have a higher external validity than experimental studies.

3.13 Exercises

Selected answers are available in App. E.

Exercise 3.1 Consider this RQ (McLinn et al. 1994):

In children with acute otitis media, what is the difference in the average duration of symptoms when treated with cefuroxime compared to amoxicillin?

  1. Is the comparison a within- or between-individuals comparison?
  2. Is this RQ relational, repeated-measures or correlational?
  3. Is there likely an intervention?
  4. Is the RQ an estimation or decision-making RQ?
  5. Is the study observational or experimental? If observational, what is the direction? If experiment, is this a quasi-experiment or true experiment?

Exercise 3.2 A study (Khair et al. 2015) studied the time needed for organic waste to turn into compost. For some batches of compost, earthworms were added. In other batches, earthworms were not added to the waste.

One RQs asked whether the composting times for waste with and without earthworms was the same or not.

  1. Is the comparison a within- or between-individuals comparison?
  2. Is this RQ relational, repeated-measures or correlational?
  3. Is there an intervention?
  4. Is the RQ an estimation or decision-making RQ?
  5. Is the study observational or experimental? If observational, what is the direction? If experiment, is this a quasi-experiment or true experiment?

Exercise 3.3 In a study on the shear strength of recycled concrete beams (Gonzalez-Fonteboa and Martinez-Abella 2007), beams were divided into three groups. Different loads were then applied to each group, and the shear strength needed to fracture the beams was measured. Is this a quasi-experiment or a true experiment? Explain.

Exercise 3.4 A research study compared the use of two different education programs to reduce the percentage of patients experiencing ventilator-associated pneumonia (VAP). Paramedics from two cities were chosen to participate. Paramedics in City A were chosen to receive Program 1, and paramedics in the other city to receive Program 2.

  1. Is this RQ relational, repeated-measures or correlational?
  2. Is the comparison a within- or between-individuals comparison?
  3. Is there likely an intervention?
  4. Is the study observational or experimental? If observational, what is the direction? If experiment, is this a quasi-experiment or true experiment?

Exercise 3.5 A nursing study aimed to compare 'the effectiveness of alternating pressure air mattresses vs. overlays, to prevent pressure ulcers' (Manzano et al. (2013), p. 2099). Patients were provided with either alternating pressure air overlays (in 2001) or alternating pressure air mattresses (in 2006). The number of pressure ulcers were recorded.

This study is experimental, because the researchers provided the mattresses. Is this a true experiment or quasi-experiment? Explain.

Exercise 3.6 Consider this initial RQ (based on Friedmann and Thomas (1985)), that clearly requires a lot of refining: 'Are people with pets healthier?' To answer this RQ:

  1. Briefly describe useful and practical definitions for P, O and C.
  2. Briefly describe an experimental study to answer the RQ.
  3. Briefly describe an observational study to answer the RQ.

Exercise 3.7 Consider this journal article extract (Sacks et al. (2009), p. 859):

We randomly assigned \(811\) overweight adults to one of four diets [...] The diets consisted of similar foods and met guidelines for cardiovascular health [...] The primary outcome was the change in body weight after \(2\) years in [...] comparisons of low fat versus high fat and average protein versus high protein and in the comparison of highest and lowest carbohydrate content.

  1. What is the between-individuals comparison?
  2. What is the within-individuals comparison?
  3. Is this study observational or experimental? Why?
  4. Is this study a quasi-experiment or a true experiment? Why?
  5. What are the units of analysis?
  6. What are the units of observation?
  7. What is the response variable?
  8. What is the explanatory variable?