Qualitative vs. Quantitative Research Designs

Qualitative research explores phenomena that are challenging to measure with numbers. It focuses on understanding qualities, characteristics, and interpretations, such as:

• Beliefs

• Meanings

• Attributes

• Symbols

Quantitative research, conversely, investigates phenomena that can be measured and expressed numerically. It deals with quantities and amounts. For instance:

• Studies that involve experiments are typically quantitative in their approach.

Aspect Comparison:

Experimental Study Design:

Experimental study designs are used to examine cause-and-effect relationships. They involve introducing a specific intervention (considered the potential cause) and observing if it produces a change after a certain period. Key experimental designs include:

i) After-Only Design:

• In this design, it’s understood that a population has already received an intervention.

• The study aims to evaluate the impact of this intervention.

• Information about the situation before the intervention is usually obtained through participant recall or from pre-existing records.

• Frequently used for impact assessments where a baseline wasn’t established beforehand.

ii) Before-and-After Design:

• This design improves upon the after-only design by establishing a baseline measurement before the intervention is introduced.

• An initial observation is conducted to assess the situation before the intervention.

• After the intervention is implemented and expected to have an effect, a subsequent “after” observation is made.

• The difference between the “before” and “after” measurements is used to determine the intervention’s impact.

iii) Control Group Design:

• This design involves two groups: an experimental group and a control group.

• The groups are selected to be as similar as possible at the start, except for the intervention.

• The experimental group receives the intervention.

• The control group does not receive the intervention and serves as a comparison.

• A “before” observation is taken for both groups simultaneously.

• Then, the experimental group is exposed to the intervention.

• After sufficient time for the intervention to have an effect, an “after” observation is conducted on both groups.

• Any significant difference in the “after” observation between the experimental and control groups, beyond what was present in the “before” observation, is attributed to the intervention.

General Characteristics of Experimental Designs:

• Direct Intervention on Variables: Researchers actively change or manipulate the independent variables within a carefully controlled setting.

• Examining Variable Impact: The research investigates how these changes in independent variable(s) affect one or more dependent variables.

• Hypothesis Driven: The core purpose is to test specific research hypotheses.

• Extraneous Variable Control: Effort is made to manage and eliminate any outside variables that could influence the results.

• Empirical and Valid Findings: Aims to produce findings based on observation and measurement, ensuring both internal validity (study accuracy) and external validity (generalizability).

• Control and Experimental Groups: Some designs use distinct control and experimental groups to compare outcomes (true experimental design).

Advantages of Experimental Design:

i) Establishes Causation: Allows researchers to control the research environment and explore “why” something happens.
ii) Cause vs. Placebo: Helps distinguish genuine cause-and-effect relationships from effects that might occur simply due to expectation (placebo effects).
iii) Limits Alternative Explanations: Strengthens the ability to rule out other factors and confidently infer direct causal links.
iv) High Evidence Level: Provides a robust level of evidence within individual studies.

Disadvantages of Experimental Design:

i) Artificiality: Experimental settings can be unnatural, potentially limiting how well results apply to real-world situations.
ii) Participant Behavior Change: The controlled environment of experiments might alter how participants behave or respond.
iii) Costly Resources: Experiments can be expensive, particularly if specialized tools or locations are needed.
iv) Ethical/Practical Limitations: Certain research questions cannot be ethically or practically addressed through experiments.
v) Qualitative Method Integration Challenges: Integrating ethnographic or other qualitative approaches within experimental designs can be difficult.

Cross-Sectional Study Design

Cross-sectional studies are a research approach that looks at different groups of individuals who vary in the characteristic being studied but are similar in other aspects like socioeconomic status, education level, and background. For example, researchers might choose groups similar in most ways, but different in age. This way, any differences found between these groups can be more likely linked to age rather than other factors. These studies are usually observational and often used for descriptive research purposes.

Characteristics of Cross-Sectional Studies:

• Existing Data Collection: Researchers record information as it naturally exists in a population, without intervening or changing anything.

• Single Time Point Data: Data is gathered at just one specific moment in time.

• Population Description: This design describes characteristics within a population but does not prove cause-and-effect relationships between variables.

• Multi-faceted Investigation: Researchers can examine many different factors at once, such as age, income, and gender, within the same study.

What Cross-Sectional Studies Can Reveal:

1. Snapshot in Time: They offer a picture of results and related traits at a particular moment.

2. Observing Existing Differences: Instead of creating changes like in experiments, these studies analyze and interpret differences that already exist between people, subjects, or phenomena.

3. Data at a Single Point: Data collection is focused on a specific, defined time.

4. Moment-in-Time Relationships: While longitudinal studies track changes over time, cross-sectional research aims to find connections between variables at one point in time.

5. Difference-Based Group Selection: Groups are chosen for the study because they already differ in some way, rather than being randomly selected.

6. Prevalence Estimation: This method can estimate how common a certain outcome is because the sample often represents the larger population.

7. Efficient Data Collection: Often uses surveys for data collection, making it relatively affordable and time-efficient.

Disadvantages and Challenges of Cross-Sectional Design:

1. Finding Similar Groups: It can be hard to find individuals, subjects, or phenomena that are very alike except for the specific variable being studied.

2. Static Results: Findings are fixed in time and don’t show how things change over time or consider historical context.

3. No Causation: Cross-sectional studies cannot establish that one thing causes another.

4. Single Snapshot Limitation: Provides only one view of the analysis, and results might be different if the study was done at another time.

5. Lack of Follow-up: There is no follow-up to see how things change after the initial data collection.

Characteristics of Cross-Sectional Research Design:

• No Time Element: Does not track changes over time.

• Focus on Existing Variation: Examines differences that are already present, not changes after an intervention.

• Difference-Based Grouping: Groups are chosen because they are already different, not through random assignment.

Note: Cross-sectional research design is distinct from longitudinal research, which involves taking multiple measurements across a longer duration.

Longitudinal Research Design:

This study design gathers data from the same subjects or sample repeatedly over a period of time. It follows specific individuals or a group over a set duration, looking at changes in their behavior or characteristics.

Examples:

1. Tracking the academic progress of the same group of children throughout their schooling years.

2. Studying human development stages by following the same child from birth into adulthood.

Longitudinal studies offer deeper understanding of issues. However, they can be costly in terms of time and resources. Study progress can be affected if participants are lost over time due to reasons like death or relocation.

Longitudinal studies are further divided into Panel and Trend studies.

Panel Study: Uses the exact same group or individuals throughout the entire study. A single sample is studied continuously over time.

Trend Study: Uses different but comparable groups or samples at different points in time during the study. Results from these different groups are analyzed and compared to identify patterns of change or trends over time. For example, one could observe the trend in behavior changes between first-year students entering in 2001 and first-year students entering in 2002 using a trend longitudinal study.

What Longitudinal Studies Tell You:

1. Duration Analysis: Longitudinal data helps analyze how long a particular phenomenon lasts.

2. Approaching Causation: Allows researchers to get closer to understanding cause-and-effect relationships, similar to what experiments achieve.

3. Change Measurement Over Time: Enables measuring differences or changes in a variable across different time points, revealing patterns of change over time.

4. Future Prediction: Facilitates making predictions about future outcomes based on earlier factors or trends.

What Longitudinal Studies Don’t Tell You:

1. Methodological Shifts: Data collection methods might need to be adjusted over the course of the study.

2. Sample Integrity Challenges: Maintaining the original sample group intact can be difficult over long periods.

3. Limited Multi-Variable Display: It can be challenging to effectively show the influence of more than one variable at a time.

4. Need for Qualitative Explanation: Often requires qualitative data to interpret unexpected variations or fluctuations in the results.

5. Trend Continuity Assumption: Relies on the assumption that current trends will continue without significant changes.

6. Time-Intensive Results: Gathering results can take a considerable amount of time.

7. Sampling Requirements: Requires a large sample size and precise sampling techniques to ensure the sample is representative of the population.

Case Study/Case Report Designs:

A case study is a detailed investigation into a specific instance of a phenomenon, such as an individual person, a group, an object, or a situation, within a defined context. Findings from a case study can be generalized to understand similar cases within a wider population of interest. For example, studying the development of a child or a group of children from birth to adulthood, and then applying those findings to other children generally.

What Case Studies Don’t Tell You (Disadvantages):

1. Limited Generalizability: Studying a single case or just a few cases provides weak grounds for making broad generalizations to larger groups or situations.

2. Researcher Bias: In-depth involvement in a case can lead to researcher bias in interpreting the findings.

3. Causation Difficulty: This design is not well-suited for determining cause-and-effect relationships.

4. Missing Information: Important details may be absent, making it difficult to fully understand the case.

5. Representativeness Issues: The chosen case might not be typical or representative of the broader issue being studied.

6. Unusual Case Limitations: If a case is selected because it’s very unusual or unique, the findings might only apply to that specific unique case.

Retrospective and Prospective Study:

A retrospective study is a type of longitudinal study that examines data from the past. For instance, a researcher might look at old medical records to identify patterns or trends. Essentially, retrospective studies “look backwards” in time.

In contrast, prospective studies “look forwards” by collecting new data as events happen in real-time.

Retrospective Study Example in Health: In healthcare, a retrospective study could involve analyzing past medical records of patients with cancer to evaluate how effective a particular treatment used in the past was. Researchers can assess patient outcomes based on their treatment and other factors to understand the treatment’s success rates.

Prospective Study Example in Health: A prospective health study might track a group of pregnant individuals from early pregnancy through childbirth and beyond. Researchers would collect data on prenatal care, diet, lifestyle, and follow these individuals to monitor pregnancy outcomes, birth complications, and the health of their babies after birth. This type of study helps understand factors that influence maternal and child health during and after pregnancy.

Cohort Study Design:

A cohort is a group of people who share a common characteristic or experience. For example, individuals born in the same year or during a specific period, like 1981, can be a birth cohort. Cohort studies are observational studies where one or more groups (cohorts) are followed over time, with repeated assessments to see if there is a link between initial characteristics or risk factors and specific outcomes or diseases. As the study progresses, the outcomes of people in each cohort are measured, and researchers analyze these outcomes in relation to their initial characteristics.

Example of a Cohort Study:

To investigate if smoking is linked to lung cancer, a researcher forms two adolescent groups (cohorts). One group consists of people who have never smoked and continue not to smoke (unexposed), while the other group includes smokers (exposed). Both groups are followed over a set period, observing how many in each group develop lung cancer and how many do not. The table below illustrates potential outcomes:

Advantages of Cohort Studies:

• Matching for Confounding: Subjects in cohorts can be matched on certain characteristics to minimize the influence of confounding variables.

• Temporal Sequence: Cohort studies can demonstrate that potential causes happen before the outcomes.

• Data Source Flexibility: Can use existing data or collect new data for the study.

• Cost and Ease: Often less expensive and simpler to conduct compared to randomized controlled trials.

Disadvantages of Cohort Studies:

• Cohort Identification Challenges: Identifying appropriate cohorts can be difficult due to potential confounding variables.

• Lack of Randomization Bias: The absence of randomization may lead to imbalances in the characteristics of participants between groups.

• Blinding Difficulty: Blinding or masking researchers and/or participants is usually difficult in cohort studies.

• Long Outcome Timeframe: The outcomes of interest may take a long time to develop and observe.

• Time and Validity Concerns: Studies can be lengthy, potentially affecting the validity of the findings. The lack of randomization also reduces how well the findings can be generalized compared to randomized studies.

Randomized Controlled Trial (RCT):

The main difference between a Randomized Controlled Trial (RCT) and a cohort study is the random assignment of participants. RCTs involve randomly allocating participants to different treatment groups, including a control group. These trials aim to measure and compare the results of these different treatments. RCTs are purely experimental and quantitative in nature.

Example: In a study to see if pain medication is needed for males after circumcision, 200 eligible men were randomly divided into two groups. One group received pain relief tablets (Panadol) immediately after surgery, while the other group received a placebo (inactive pill). Results showed that most (90%) of patients given Panadol reported no pain, while over 90% on placebo reported significant pain.

Advantages of RCT:

• Highest Evidence Form: RCTs are considered the most trustworthy type of scientific evidence.

• Reduced Spurious Causality: They minimize the chance of false causal relationships.

• Policy and Practice Impact: RCTs significantly influence healthcare policy and clinical practice guidelines.

Limitations of RCT:

• Limited External Validity: The results of RCTs may not always apply perfectly to real-world settings.

• Ethical Considerations: Ethical concerns can arise in certain research situations.

• Time for Outcomes: It can take a long time to observe the outcomes of interventions.

Case Series/Clinical Series:

A case series is a descriptive study that follows a group of patients or subjects who have a known exposure, such as those who have received similar treatments or whose medical records are reviewed for shared exposures and outcomes. Case series can suggest hypotheses for further research but cannot establish cause-and-effect relationships on their own. The internal validity of case series is generally low because they lack a comparison group that did not receive the same exposure.

Correlation Study Design:

A correlation study explores whether two variables are related, meaning if a change in one variable is associated with a change in the other. However, it’s important to remember that correlation does not mean causation. There are three types of correlation: positive, negative, and no correlation.

A correlation coefficient, ranging from +1 to -1, is typically used to measure the strength and direction of the relationship between variables.

Types of Correlation:

1. Positive Correlation: Occurs when an increase in one variable is linked to an increase in another variable, and a decrease in one variable is linked to a decrease in the other. For example, a person’s wealth might be positively correlated with the number of rental properties they own. This suggests that as wealth increases, property ownership also tends to increase.

2. Negative Correlation: Observed when an increase in one variable is associated with a decrease in another variable, and vice versa. For instance, there might be a negative correlation between a country’s education level and its crime rate. If education levels rise in a country, the crime rate tends to fall. However, this doesn’t mean lack of education directly causes crime; both might be influenced by a common underlying factor like poverty.

3. No Correlation (Uncorrelated): In cases of no correlation, changes in one variable are not consistently related to changes in the other. For example, among millionaires, there might be no correlation between their wealth level and their happiness level. This indicates that more wealth doesn’t automatically lead to more happiness.

Comparative Study Design:

Comparative studies examine two or more cases, examples, or events that are similar in some ways but different in others. The goal is to understand the reasons for these differences and to apply the findings to broader groups that these cases represent. The ability to generalize findings improves when multiple cases from the same group are analyzed.

Ethnographical Research Design:

Ethnographical research, or ethnography, is the in-depth study of behavior as it naturally occurs within a specific culture or social group. Its main goal is to understand the relationship between culture and behavior. Culture here refers to the beliefs, values, and attitudes of a particular group of people. Ethnographic research methods were originally developed by anthropologists to study and describe human cultures.

Characteristics of good ethnography, according to Spindler & Hammond (2000), include:

1. Prolonged Participant Observation: Researchers immerse themselves in the culture or social group they are studying and observe behavior over an extended period.

2. Extended Time at Site: Researchers spend a significant amount of time within the community or culture being studied to gain a deep, comprehensive understanding of their way of life.

3. Extensive Data Collection: This involves gathering detailed notes, audio recordings, video recordings, and other data, often without starting with specific hypotheses or pre-defined categories.

Phenomenological Research Design:

Phenomenological research is the study of phenomena, which can be events, situations, experiences, or ideas, as they are experienced by individuals. This approach aims to understand and describe the core essence of lived experiences from the viewpoint of the participants. Phenomenology starts with acknowledging that there is a gap in our understanding and that gaining clarity would be valuable. In phenomenological research, researchers aim to explore the fundamental meaning and nature of experiences as they are lived. It often uses in-depth interviews and analysis to find common themes and structures that underlie these experiences. This research design is valuable for understanding how people make sense of the world and their experiences.

Grounded Theory Research Design:

Grounded theory is a systematic research approach that aims to develop theories or concepts that are based on data itself. It was developed by sociologists Barney Glaser and Anselm Strauss in the 1960s. The main goal of grounded theory is to create new, abstract theories from empirical observations, rather than testing existing theories.

Key features of grounded theory research include:

• Data-Driven Approach: Grounded theory begins with data collection and analysis. Researchers gather and analyze data without pre-set ideas or hypotheses.

• Constant Comparison: New data is continuously compared with data already collected, allowing researchers to identify patterns and categories as they emerge.

• Theory Development: Through this iterative data analysis, researchers develop theories or concepts that explain the phenomenon being investigated.

• Purposeful Sampling: Researchers use specific sampling methods to select participants and gather data that is most relevant to the research question and the developing theory.

Grounded theory is widely used in social sciences, especially in fields like sociology and psychology, to develop new theories and understand complex social phenomena.