32 Quantitative Study Designs
32.1 Introduction
The term ‘study design’ is used to describe the type of format, or structure, that is used to conduct a specific research study. A range of options are available, and it is important to understand the purposes and limitations of each format.
32.2 Descriptive studies
Purpose
Descriptive studies play an integral role in research by systematically identifying patterns and trends within a dataset.
These studies are designed to provide a comprehensive depiction of the subject in question without the manipulation of environmental variables. The primary purpose is to offer a snapshot of a phenomenon at a specific point in time.
This approach can be particularly beneficial in understanding the current state of a system or process. By doing so, they lay the groundwork for future research, offering a platform from which hypotheses can be formulated and subsequently explored in experimental settings.
Descriptive research seeks to establish a foundation that can guide subsequent scientific inquiry.
Method
Descriptive studies are characterised by a range of techniques tailored to the nature of the research question.
Surveys and questionnaires are commonly employed to collect quantitative data through structured or semi-structured formats, allowing for a broad assessment of characteristics, attitudes, or opinions across a sample.
Observational studies are characterised by their non-intrusive nature. Researchers systematically observe and record behavior or conditions in their natural setting, preserving the authenticity of the data.
Case studies dive deeper into a particular subject, offering a rich, contextual analysis of a single case, group, or event, and often uncovering insights that broader studies may overlook.
Examples
The scope of descriptive studies is broad and varied:
Demographic studies, for instance, elucidate patterns related to population distribution, age, gender, and other sociological variables, which are crucial for policy-making and planning.
Market research applies descriptive techniques to analyse consumer behaviour, preferences, and market trends, thereby informing business strategies.
In healthcare, clinical studies focus on recording and analysing patient symptoms and medical histories to improve diagnostic and treatment approaches.
Strengths and Limitations
The strengths of descriptive studies lie in their ability to provide a comprehensive overview of the data at hand.
They are typically easier and less costly to conduct compared to experimental studies
They are particularly adept at hypothesis generation, serving as a crucial step in the research process.
However, they come with limitations:
The absence of experimental control means they cannot establish causal relationships, and the findings may be subject to bias if the sample is not representative of the broader population.
Results can be influenced by external variables, which can compromise the accuracy and reliability of the findings.
It is therefore essential that the limitations of descriptive studies are acknowledged and accounted for in the interpretation of their outcomes.
32.3 Correlational studies
Purpose
Correlational studies are a foundation of behavioral science, focusing on the discovery and interpretation of relationships between variables. They are useful in predicting the behavior of one variable based on the changes in another.
They are particularly useful for testing and refining theories, as they allow us to examine theoretical predictions about the nature of the relationships between variables under investigation. They don’t manipulate variables but rather assess the naturally occurring associations between them.
Methods
The methodologies employed in correlational studies are chiefly statistical.
Pearson’s r is the most common statistical method used to quantify the strength and direction of a linear relationship between two continuous variables.
When data are ordinal or don’t meet the assumptions necessary for Pearson’s r, Spearman’s Rank Correlation offers an alternative that can accommodate non-parametric data.
Regression analysis extends beyond correlation by attempting to explain the variance in a dependent variable based on one or more independent variables, allowing for more sophisticated predictive modeling.
Examples
The applications of correlational studies are diverse.
Researchers may explore the relationship between exercise and heart health to determine if regular physical activity correlates with cardiovascular benefits.
They may examine the link between educational attainment and income level, potentially influencing policy and educational funding.
They may investigate the relationship between age and technology use, to inform product development and marketing strategies targeted at different age demographics.
Strengths and Limitations
Correlational studies offer a rapid and cost-effective means to analyse complex datasets and are especially useful for preliminary investigations into potential relationships between variables.
However, their findings are limited by the inherent inability to infer causation from correlation.
They’re also restricted by their focus on linear relationships, potentially overlooking more complex, non-linear associations.
They can be compromised by confounding variables - other factors that may influence the observed relationships. As such, while correlational studies are a powerful tool for exploring associations, they must be interpreted with an understanding of their inherent limitations.
32.4 Experimental studies
Purpose
The purpose of experimental studies is to carefully explore the mechanics of causation, offering concrete evidence of how one variable influences another.
They’re indispensable for testing hypotheses with precision and rigor, often through manipulation of variables and observing the resultant effects.
They’re also important in assessing the impacts of various interventions or treatments in fields such as medicine, psychology, and education.
Methods
Experimental studies utilise a variety of methods to ensure reliability and validity in their findings.
Randomised Controlled Trials (RCTs) represent the gold standard in experimental research, where participants are randomly assigned to either control or treatment groups to evaluate the impact of an intervention in an unbiased manner.
Field experiments try to bring the rigour of the laboratory to natural settings, providing a balance between experimental control and real-world relevance.
Laboratory experiments are conducted in highly controlled environments, allowing researchers to manipulate and measure variables with great precision.
Strengths and Limitations
Experimental studies are robust due to their ability to establish causal relationships and their high level of control over the variables in question. The rigorous testing of hypotheses under controlled conditions is a key strength of this approach.
However, these strengths are counterbalanced by limitations, such as the potential lack of external validity, meaning that results from a controlled, experimental environment may not always generalise to real-world scenarios.
Experimental research can be both time-consuming and costly, with complex designs requiring significant resources.
Ethical considerations also play a critical role, especially in experiments that could potentially harm participants, necessitating a thorough review and adherence to ethical standards.
32.5 Quasi-experimental studies
Purpose
Quasi-experimental studies are instrumental in exploring causal relationships, particularly in situations where random assignment is not feasible due to ethical, practical, or logistical constraints.
They’re valuable for evaluating the real-world effects of programs, policies, and interventions, and are often applied to test theoretical models and hypotheses within naturalistic settings.
By bridging the gap between strict experimental control and the need to study phenomena in their authentic contexts, quasi-experimental designs expand the reach of causal inquiry.
Methods
These studies employ non-randomised designs, relying on alternative methods to assign participants to different conditions.
Pretest-posttest designs are commonly used, which involve taking measurements before and after an intervention to assess its impact.
Matched groups designs enhance comparability by pairing participants across conditions based on certain characteristics, thereby aiming to reduce variability and potential confounding due to these characteristics.
Examples
Quasi-experimental designs are common in educational research, such as examining the outcomes of new educational policies on student performance.
In organisational settings, they might be used to determine the efficacy of a new training program. Public health officials may also use quasi-experimental studies to evaluate the impact of community health programs on population health metrics.
Strengths and Limitations
The strength of quasi-experimental studies lies in their applicability in environments where randomized controlled trials (RCTs) are not possible, making them highly practical for assessing existing programs. They are particularly useful for investigating causal relationships when ethical or practical limitations preclude random assignment.
However, the limitations of these studies are significant.
The lack of randomisation can introduce selection bias, where the groups being compared may differ in systematic ways that affect the outcome. This can lead to lower internal validity, meaning the degree to which one can confidently attribute the outcomes to the intervention is reduced.
Additionally, the potential for confounding variables - factors that the researcher has not measured or controlled for - can compromise the study’s conclusions.
Despite these limitations, quasi-experimental studies remain a crucial tool in research, especially in applied settings where the ideal conditions for RCTs are not present.
32.6 Reflect
Consider the following questions:
How do the methodological approaches and potential biases of descriptive, correlational, experimental, and quasi-experimental studies influence the interpretation of their results, and what are the implications for their application in real-world sport settings?
Reflect on the ethical considerations that must be taken into account when designing experimental and quasi-experimental studies, especially when random assignment is not feasible. How do these considerations impact the validity and reliability of the research findings?
Given the strengths and limitations of each study type, think about a sports scenario where you might choose one research method over another. What factors would influence your decision, and how would you address the potential challenges inherent in the chosen research design?