PSY 5107 Critique Descriptive/Observational Methods of Research

PSY 5107 Critique Descriptive/Observational Methods of Research

PSY 5107 Critique Descriptive/Observational Methods of Research

Scientific research can adopt two study designs; qualitative and quantitative research designs. Research can adopt a descriptive, observational, or interventional/experimental approach depending on the purpose and choice of the researcher. Observational research studies generally entail the study of the subject or population of interest in their natural state and circumstances without the implementation of any interventions or changes (Gilmartin-Thomas et al., 2018). These studies allow researchers to observe, study and describe the distribution and attributes of the study variables, without the need to establish any causal relationships or other hypotheses (Aggarwal et al., 2022). These types of approaches can effectively be adopted in healthcare-related studies such as determining the predisposing risk factors, outcomes of interventions, and incidence and prevalence of certain conditions of interest.

Observational studies are divided into descriptive and analytical studies. Descriptive observational studies describe the unique attributes of the studied variables or population whereas analytical observational studies identify determinants of health-related events by trying to address cause-and-effect dilemmas with no control over the variables in a cost-effective manner (Gilmartin-Thomas et al., 2018). Examples of observational studies include case reports, case series, ecological studies, cross-sectional studies, case-control studies, and cohort studies (Gilmartin-Thomas et al., 2018). Case-control studies,  cross-sectional studies, and cohort studies are comparative studies that involve control groups with no exposure of interest and thus can establish associations and relationships between the study variables.

The purpose of this paper is to describe and detail an observational/descriptive research method that I can use to explore a topic of interest. This will include the reasons for the choice from the existing methods, the strengths and weaknesses of the chosen research method, how to deal with the weaknesses, and whether the chosen research method is aligned with a quantitative or qualitative research design. An alternative observational research approach will also be discussed regarding similar aspects. A comparison of the similarities and differences between the two chosen research approaches will also be presented.

Cross-Sectional Studies

Cross-sectional studies are a type of descriptive observational study. Cross-sectional studies collect and analyze data at a given point in time with no longitudinal follow-up of study participants to give a snapshot of the health status of the population of interest (Wang et al., 2020). Information is collected to determine the presence, distribution, and level of the desired health-related attribute with the exposure and outcomes being assessed simultaneously (Aggarwal et al., 2022). The findings from the analysis of data obtained from cross-sectional studies can be used to determine the prevalence of the study variables such as diseases, predisposing factors, traits, or health behaviors in a particular population.

Click here to ORDER an A++ paper from our Verified MASTERS and DOCTORATE WRITERS: PSY 5107 Critique Descriptive/Observational Methods of Research

This is particularly important in evaluating the disease burden and healthcare needs of the population (Aggarwal et

PSY 5107 Critique Descriptive Observational Methods of Research
PSY 5107 Critique Descriptive Observational Methods of Research

al., 2022). These studies can also give valuable information on the determinants of the health of a population. An association or relationship between exposure and outcomes can be established but a causal inference can not be made. This is achieved by comparing the prevalence of a particular health outcome between individuals with exposure and those without exposure. The reason why I chose this observational research approach is that it is simple and not expensive to conduct. It is also time-saving since there is no follow-up of the study participants hence will be convenient for me. Since there is no manipulation of variables, obtaining ethical approval will be easier since the study population is not at risk of any harmful or risky interventions.

Strengths and Weaknesses of Cross-Sectional Studies

Cross-sectional studies have their strengths and weaknesses. The advantages of these research approaches are that they are easy to perform, relatively cheap, and require no longitudinal follow-up of study subjects thus a short data collection period. These studies can also establish the prevalence of health-related attributes and associations between exposures and disease outcomes (Aggarwal et al., 2022). Multiple exposures and associated outcomes can be studied at the same time. They can thus be used for risk stratification of individuals in the population to allow for the implementation of targeted policies and strategies for better health outcomes.

The identified weaknesses include the risk of selection and measurement biases thus the findings may not be inferred as the true representation of the general population (Aggarwal et al., 2022). The exact temporal sequence and relationship between the exposure and associated health outcome cannot be determined to show which preceded the other due to the absence of longitudinal follow-up. The associations between exposures and disease outcomes cannot be used to determine cause-and-effect relationships thus additional studies may be needed to make a causal inference. These studies also determine the prevalence of health attributes and thus cannot give the incidence in cases of outbreaks.

Strategies to Address the Weaknesses of Cross-sectional Studies

Certain strategies can be adopted to address the weaknesses and limitations of cross-sectional studies. These include conducting multiple repeated temporal cross-sectional studies on a specific concept to improve the quality of causal relationships (Taris et al., 2021). Time-separated cross-sectional studies on a similar subject of interest also reduce the potential risk of selection and measurement biases. This longitudinal approach may also provide new insights and information such as the incidence of a particular health attribute. A sample that is representative of the population should also be selected. Relatively larger sample size should be considered to increase the precision of the prevalence estimates. There should also be clarity on the inclusion and exclusion criteria of the exposed and control study groups.

Cross-sectional Research Approach Alignment

Cross-sectional studies are more aligned with quantitative research design. This is because numerical data is collected and analyzed and involves the calculation of the prevalence ratio. Quantitative-based cross-sectional studies use quantitative statistical data to establish the prevalence of specific health conditions, attitudes, and knowledge of the population of interest (Kesmodel, 2018). The measurements from the findings can be used in the comparison of the exposed group and the control group.

Case Series

Case series is the other alternative observational descriptive approach to research studies. A case series entails a description of the clinical or epidemiological characteristics and outcomes among an aggregate of individuals with either a given disease or an exposure over a given time (Torres-Duque et al., 2020). The data collection can take a prospective or retrospective approach. This kind of study has no control or comparison group since the aim of the study is a description of the population and outcomes of interest and not the comparison of risks (Torres-Duque et al., 2020). Valid statistical associations between exposure and outcomes cannot be established. Cause-and-effect relationships cannot also be determined and thus require further exploration through other research methods such as randomized control trials. There is also no randomization during the selection of study participants. These attributes make the evidence and findings from the case series to be of limited value.

These studies are, however, essential in exploring unusual, new, or rare diseases, description of new emerging adverse outcomes related to interventions, and opening a new line of investigations from new hypotheses generated from chance observations which lead to advancements in the clinical field (Aggarwal et al., 2022). They can also explore the safety of new therapeutic interventions. The reasons I would consider this approach is its inexpensive nature, simplicity, and ready availability of the required data.

Strengths and Weaknesses of Case Series

Case series have various advantages. These studies are easy to conduct, inexpensive, quick, require little effort, have readily available data, have no serious ethical scrutiny except for matters of confidentiality, and similar studies among different populations at different times can identify geographic variations and temporal variations in disease prevalence (Aggarwal et al., 2022). They are also of educational value, describe outcomes of novel interventions, generate new hypotheses which can further be pursued using other research methods, identify new unusual conditions, and offer opportunities for refinement of new interventions and protocols (Rezigalla et al., 2020). Resource planning and allocation can be implemented based on information obtained such as the disease burden.

Case series also have weaknesses and pitfalls. These limitations include selection bias, lack of control over the study variables, lack of comparison of cases, and lack of generalizability of the findings (Rezigalla et al., 2020). The limitations in the number of cases in case series which may just be a chance occurrence and biases created by the availability and accuracy of the records are the other pitfalls of case series (Aggarwal et al., 2022). Conclusions based on findings can thus not be inferred from the general population putting into question their validity and reliability. Associations and relationships between variables cannot be determined due to the absence of control groups. Cause-and-effect relationships as well as causal inference can not be made.

Measures to Address the Weaknesses of Case Series

The limitations of the case series can be addressed by certain measures. These include predefining the inclusion and exclusion criteria before beginning the study and clear specifications of the duration of the study (Aggarwal et al., 2022). Sampling strategies can also be employed based on the disease or outcome of interest with the data collected systematically and analyzed using robust methods (Torres-Duque et al., 2020). These measures will greatly enhance the validity, relevance, quality, and strength of evidence from the case series. A larger number of cases can also be included in the study to further strengthen the obtained evidence.

Research Alignment of Case Series

Case series are a type of quantitative research study. They fall under descriptive observational studies. These studies use quantitative statistical data to determine the exposure and outcomes in an aggregate of individuals over a given time without the use of a control group. Numerical data is used during data collection and analysis. There is also the use of qualitative data during the description of attributes of the population or variable of interest. There are no particular measures in the case series.

Similarities and Differences Between Cross-Sectional Studies and Case Series

Various similarities and differences exist between cross-sectional studies and case series. This is determined by factors such as the directionality of the study, the primary use, the duration of the study, and the presence of comparison among others (Ranganathan et al., 2019). The similarities include the quick, simple and inexpensive nature, the lack of control of the variables, the descriptive observational research design adopted, the minimal ethical scrutiny, the establishment of associations between variables without determination of cause-and-effect relationships, lack of generalizability of the findings from lack of randomization and presence of selection bias, and low strength of research evidence. The differences include the primary use of the research, the research directionality, the measures obtained, and the use of a control group.

Table 1: A comparison between case studies and cross-sectional studies

Feature Cross-sectional studies Case Series
Study Design Quantitative Quantitative
Study type Descriptive observational Descriptive observational
Cost Inexpensive Inexpensive
Ethical scrutiny Minimal Minimal
Measures Prevalence ratio None
Control or comparison group Present Absent
Directionality Exposure and outcomes are assessed simultaneously at a given point in time. Exposure and outcomes are assessed prospectively or retrospectively.
Primary Use They can determine the prevalence of a condition.

Associations between exposure and outcomes can be established without causal relationships.

They can identify outbreaks or the emergence of new unusual conditions.

No valid statistical associations between variables can be established.

Strength of evidence Low Very low



Observational descriptive research approaches offer the description of the study variables without manipulations of the outcomes of interest. Cross-sectional studies and case series are examples of these types of research approaches. These two types of studies are relatively cheap, quick, and easy to conduct compared to experimental studies. They can establish associations between study variables but the cause-and-effect relationships cannot be determined. Both of these study types have individual strengths and weaknesses. They also share some similarities and differences. Strategies can be put in place to address their shortcomings so that better quality and strength of findings can be obtained. The primary use, strengths, and weaknesses are some of the considerations when choosing the type of research approach to adopt.



Aggarwal, R., & Ranganathan, P. (2019). Study designs: Part 2 – descriptive studies. Perspectives in Clinical Research, 10(1), 34.

Gilmartin-Thomas, J. F. M., Liew, D., & Hopper, I. (2018). Observational studies and their utility for practice. Australian Prescriber, 41(3), 82–85.

Kesmodel, U. S. (2018). Cross-sectional studies – what are they good for? Acta Obstetricia Et Gynecologica Scandinavica, 97(4), 388–393.

Ranganathan, P. (2019). Understanding Research Study Designs. Indian Journal of Critical Care Medicine, 23(S4).

Rezigalla, A. A. (2020). Observational study designs: Synopsis for selecting an appropriate study design. Cureus.

Taris, T. W., Kessler, S. R., & Kelloway, E. K. (2021). Strategies addressing the limitations of cross-sectional designs in occupational health psychology: What they are good for (and what not). Work &Amp; Stress, 35(1), 1–5.

Torres-Duque, C. A., Maria Patino, C., & Carvalho Ferreira, J. (2020). Case series: An Essential Study Design to build knowledge and pose hypotheses for rare and new diseases. Jornal Brasileiro De Pneumologia, 46(4).

Wang, X., & Cheng, Z. (2020). Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. Chest, 158(1S), S65–S71.