Introduction

The design of the research depends largely on the nature of the study question. In other words,
knowing what kind of information the study should collect is a first step in determining how the
study will be conducted (Consultores, 2021b). Conducting research is important as it gives the
evidence needed to support theories and hypothesis. Developmental research methods aim to
examine changes or differences in behaviour by looking at different ages or stages across a lifetime.
This is usually carried out using two basic methods, the longitudinal and cross-sectional. Cross-
sectional research is where individuals of different ages are studied at the same time; this is in
contrast with longitudinal studies which involves testing one age group repeatedly over many years
(Barnes, 2020).

Cross-Sectional Research

Cross-sectional study design is a type of observational study design. In a cross-sectional study, the
investigator measures the outcome and the exposures in the study participants at the same time.
Unlike in case–control studies (participants selected based on the outcome status) or cohort studies
(participants selected based on the exposure status), the participants in a cross-sectional study are
just selected based on the inclusion and exclusion criteria set for the study. Once the participants
have been selected for the study, the investigator follows the study to assess the exposure and the
outcomes. Cross-sectional designs are used for population-based surveys.

In a cross-sectional study, the investigator measures the outcome and the exposures in the study
participants at the same time. The participants are just selected based on the inclusion and exclusion
criteria set for the study. Once the participants have been selected for the study, the investigator
follows the study to assess the exposure and the outcomes.

After the entry into the study, the participants are measured for outcome and exposure at the same
time [Figure 1]. The investigator can study the association between these variables. It is also possible
that the investigator will recruit the study participants and examine the outcomes in this population.
The investigator may also estimate the prevalence of the outcome in those surveyed (Setia, 2016)

Figure 1

Example of Cross-Sectional Studies

Antibiotic resistance in Propionibacterium acnes strains (Sardana K, Gupta T, Kumar B, Gautam HK,
2016)(Sardana et al., 2016) A study by Sardana et al. evaluated the antibiotic resistance in isolates of
Propionibacterium acnes in a tertiary care hospital in India. They recruited 80 patients of acne
vulgaris, collected specimen for isolation from open or closed comedones. These specimens were
then cultured, the growth identified, and antibiotic susceptibility and resistance were assessed.

They isolated P. acnes 52% of the cases. In these isolates, resistance for erythromycin, clindamycin,
and azithromycin was observed in 98%, 90%, and 100% of the isolates, respectively. However,
sensitivity for tetracycline, doxycycline, minocycline, and levofloxacin was observed in 69%, 56%,
98%, and 90% of the isolates, respectively. We will discuss this study briefly later in the manuscript
as well.

Strengths

1.The main strength of cross-sectional studies is that they are relatively quick and inexpensive to
conduct (Wang and Cheng, 2020). (pubrica-academy, 2020) defined that questionnaires are mainly
used in most of the cross sectional researches. Cross sectional researches are relatively cheap and
quick, and allow the researchers to obtain lot of information very quickly.

2. Another strength of cross-sectional research designs is that it may be useful for public health
planning, monitoring, and evaluation. For example, sometimes the National AIDS Programme
conducted cross-sectional sentinel surveys among high-risk groups and ante-natal mothers every
year to monitor the prevalence of HIV in these groups.

(Shinde S, Setia MS, Row-Kavi A, Anand V, 2009) (Shinde et al., 2009) The authors presented a cross-
sectional analysis to monitor the prevalence of HIV and risk behaviors in male sex workers. They also
evaluated the association between HIV and sociodemographic factors. The data were collected by
interviewer-administered questionnaires (for sociodemographic and behavior data), clinical
evaluation for sexually transmitted infections (STIs), and serological evaluation for STIs (including
HIV).

Weakness

1 (Mann, 2003) defined that the most important weakness with this Cross-Sectional research is
differentiating cause and effect from simple association. For example, a study finding an association
between low CD4 counts and HIV infection does not demonstrate whether HIV infection lowers CD4
levels or low CD4 levels predispose to HIV infection. Moreover, male homosexuality is associated
with both but causes neither.

3 Cross-sectional research is unable to measure incidence (Gaille, 2018).

4. The primary weakness of cross-sectional research is that the temporal link between the outcome
and the exposure cannot be determined because both are examined at the same time. For example,
(Nicola Di Girolamo, 2019) defined in a zoo, reproduction is found to be more commonly impaired in
animals with stereotypies. With a cross-sectional study, it is impossible to determine whether the
inability to reproduce exacerbates the stereotypies or the contrary.
Longitudinal Research

A longitudinal research is a type of correlational research study that involves looking at variables
over an extended period of time. This research can take place over a period of weeks, months, or
even years. In some cases, longitudinal studies can last several decades. Longitudinal design is used
to discover relationships between variables that are not related to various background variables.
This observational research technique involves studying the same group of individuals over an
extended period.

Data is first collected at the outset of the study, and may then be repeatedly gathered throughout
the length of the study. Doing this allows researchers to observe how variables change over time.

For example, imagine that a group of researchers is interested in studying how exercise during
middle age could affect cognitive health as people age. The researchers hypothesize that people who
are more physically fit in their 40s and 50s will be less likely to experience cognitive declines in their
70s and 80s.

To test this hypothesis, the researchers recruit a group of participants who are in their mid-40s to
early 50s. They collect data related to how physically fit the participants are, how often they work
out, and how well they do on cognitive performance tests. Periodically over the course of the study,
the researchers collect the same types of data from the participants to track activity levels and
mental performance (Cherry, 2022).

Examples of Longitudinal Research

Example 1: The oldest recorded longitudinal research on growth was conducted in the 18th century
by Count Philibert Gueneau de Montbeillard. Through Longitudinal Research he measured his son
every six months and published the information in the encyclopedia "Histoire Naturelle." (Elizabeth
M. Miller, 2018).

Example 2: A researcher wants to know the effects of a low-carb diet on weight loss. So, he gathers a
group of obese men and kicks off the systematic investigation using his preferred longitudinal study
method. He records information like how much they weigh, the number of carbs in their diet, and
the like at different points. All these data help him to arrive at valid research outcomes (Formplus
Blog, 2021).

Strengths

According to Newman (2010) Longitudinal research design allow researchers to follow their subjects
in real time. This means you can better establish the real sequence of events, allowing you insight
into cause-and-effect relationships. According to (Thomas, 2022) A cross-sectional study on the
impact of police on crime might find that more police are associated with greater crime and wrongly
conclude that police cause crime when it is the other way around. However, a longitudinal study
would be able to observe the rise or fall in crime some time after increasing the number of police in
an area.

Prospective longitudinal research eliminate the risk of recall bias, or the inability to correctly recall
past events. Example: You are studying the effect of low-carb diets on weight loss. If you asked your
subjects to remember how many carbs or how much they weighed at any point in time in the past,
they might have difficulty doing so. In a longitudinal study, you can keep track of these variables in
real time (Thomas, 2022).

Weakness

Longitudinal studies are time-consuming and often more expensive than other types of studies, so
they require significant commitment and resources to be effective. Since longitudinal studies
repeatedly observe subjects over a period of time, any potential insights from the study can take a
while to be discovered. Example: (Consultores, 2021a) In their study on the impact of low-carb diets
on weight loss, participants who are not very successful may feel more discouraged and therefore
more likely to quit. Therefore, it might seem that the diet is more successful than it actually is.

Recent developments in Cross-sectional design and Longitudinal design in the use amongst
researchers.

Improve representativity of the sample and restore sample size: The Dutch Longitudinal Internet
Study for the Social Sciences (LISS) recruited several additional samples since its inception in 2007.
We focus on developments of sample introduced in 2009. This top-up sample had two key
objectives: (i) to restore the overall sample size to its original level; and (ii) to address non-response
bias identified in the initial wave. This resulted in a sample design, which disproportionately sampled
groups with a lower propensity to respond in the initial recruitment in 2007, such as elderly
respondents, single-person households, and non-Westerners (Lynn, P. and Lugtig, 2017). (Lynn and
Lugtig 2017). The remainder of the population was also sampled, but at a lower rate than the groups
targeted due to the low initial response propensities, so that the overall responding sample size was
brought back to the initial level.

● Increase the overall sample size: Some studies have sought to increase the overall sample size
beyond the initial level or to return the overall sample to its initial size to reduce sampling variance
and make analysis of certain small groups possible. For example, the German Socio-Economic Panel
(SOEP), which started in 1984 added large-scale additions to the sample in 2000 and 2011 (Haisken-
DeNew, J.P. and Frick, 2005) increasing the number of responding households by around 45% and
20%, respectively. Also the Household, Income and Labour Dynamics in Australia (HILDA) Survey,
which started in 2001 added a general population-wide sample top-up in 2011, which added about
30% to the size of the responding sample (Watson, 2014).
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