What is the difference between longitudinal and cross section

A longitudinal study is an observational study that involves repeatedly examining the same subjects to detect changes that may occur over a period of time while a cross sectional study is a type of observational study that involves collecting data from many different individuals at a specific point in time. Longitudinal studies look at variables repeatedly over a period of time while cross sectional studies look at variables at a particular point in time. Moreover, longitudinal studies observe the same sample while cross sectional studies observe different samples.

While longitudinal studies observe change at both group and individual level, cross sectional studies give a snapshot of the population at a specific point in time. In addition, longitudinal studies tend to be expensive and time-consuming while cross sectional studies are not expensive and do not take a lot of time. Longitudinal studies can study cause-and-effect relationship between variables while cross sectional studies cannot.

Cherry, Kendra. Thomas, Lauren. A pilot study is usually conducted on a small subset of eligible participants who are encouraged to provide feedback on the length, comprehensibility and format of the process and to highlight any other potential issues.

Population refers to all the people of interest to the study and to whom the findings will be able to be generalized e.

Owing to the size of the population, a study will usually select a sample from which to make inferences. See also: sample , representiveness. A percentile is a measure that allows us to explore the distribution of data on a variable. It denotes the percentage of individuals or observations that fall below a specified value on a variable. The value that splits the number of observations evenly, i.

Primary research refers to original research undertaken by researchers collecting new data. It has the benefit that researchers can design the study to answer specific questions and hypotheses rather than relying on data collected for similar but not necessarily identical purposes.

See also: secondary research. In prospective studies, individuals are followed over time and data about them is collected as their characteristics or circumstances change.

Qualitative data are non-numeric — typically textual, audio or visual. Qualitative data are collected through interviews, focus groups or participant observation.

Qualitative data are often analysed thematically to identify patterns of behaviour and attitudes that may be highly context-specific.

Quantitative data can be counted, measured and expressed numerically. They are collected through measurement or by administering structured questionnaires. Quantitative data can be analysed using statistical techniques to test hypotheses and make inferences to a population. Questionnaires are research instruments used to elicit information from participants in a structured way.

They might be administered by an interviewer either face-to-face or over the phone , or completed by the participants on their own either online or using a paper questionnaire. Questions can cover a wide range of topics and often include previously-validated instruments and scales e. Recall error or bias describes the errors that can occur when study participants are asked to recall events or experiences from the past.

It can take a number of forms — participants might completely forget something happened, or misremember aspects of it, such as when it happened, how long it lasted, or other details. Certain questions are more susceptible to recall bias than others. For example, it is usually easy for a person to accurately recall the date they got married, but it is much harder to accurately recall how much they earned in a particular job, or how their mood at a particular time.

Record linkage studies involve linking together administrative records for example, benefit receipts or census records for the same individuals over time.

A reference group is a category on a categorical variable to which we compare other values. It is a term that is commonly used in the context of regression analyses in which categorical variables are being modelled.

Repeated measures are measurements of the same variable at multiple time points on the same participants, allowing researchers to study change over time. Representativeness is the extent to which a sample is representative of the population from which it is selected.

Representative samples can be achieved through, for example, random sampling, systematic sampling, stratified sampling or cluster sampling. Research ethics relates to the fundamental codes of practice associated with conducting research. Academic research proposals need be approved by an ethics committee before any actual research either primary or secondary can begin.

Research impact is the demonstrable contribution that research makes to society and the economy that can be realised through engagement with other researchers and academics, policy makers, stakeholders and members of the general public. It includes influencing policy development, improving practice or service provision, or advancing skills and techniques. Residuals are the difference between your observed values the constant and predictors in the model and expected values the error , i.

Respondent burden is a catch all phrase that describes the perceived burden faced by participants as a result of their being involved in a study. It could include time spent taking part in the interview and inconvenience this may cause, as well as any difficulties faced as a result of the content of the interview. Response rate refers to the proportion of participants in the target sample who completed the survey.

Longitudinal surveys are designed with the expectation that response rates will decline over time so will typically seek to recruit a large initial sample in order to compensate for likely attrition of participants. In retrospective studies, individuals are sampled and information is collected about their past. This might be through interviews in which participants are asked to recall important events, or by identifying relevant administrative data to fill in information on past events and circumstances.

Sample is a subset of a population that is used to represent the population as a whole. This reflects the fact that it is often not practical or necessary to survey every member of a particular population.

In the case of a household panel study like Understanding Society, the larger population from which the sample was drawn comprised all residential addresses in the UK. Sample size refers to the number of data units contained within a dataset. It most frequently refers to the number of respondents who took part in your study and for whom there is usable data.

However, it could also relate to households, countries or other institutions. The size of a sample , relative to the size of the population , will have consequences for analysis: the larger a sample is, the smaller the margin of error of its estimates, the more reliable the results of the analysis and the greater statistical power of the study.

A sampling frame is a list of the target population from which potential study participants can be selected. Scales are frequently used as part of a research instrument seeking to measure specific concepts in a uniform and replicable way.

Typically, they are composed of multiple items that are aggregated into one or more composite scores. A scatterplot is a way of visualising the relationship between two continuous variables by plotting the value of each associated with a single case on a set of X-Y coordinates. Secondary research refers to new research undertaken using data previously collected by others.

It has the benefit of being more cost-effective than primary research whilst still providing important insights into research questions under investigation. Skewness is the measure of how assymetrical the distribution of observations are on a variable. A statistical model is a mathematical representation of the relationship between variables. Statistical software packages are specifically designed to carry out statistical analysis; these can either be open-source e. R or available through institutional or individual subscription e.

SPSS ; Stata. It uses standardised content to facilitate the use of metadata for data discovery and sharing, and the relationship between metadata elements. Respondents may be required to answer some questions only if they had provided a relevant response to a previous question. Only respondents who are currently at university may be asked to answer a question relating to their degree subject.

This is important when considering missing data. Survey weights can be used to adjust a survey sample so it is representative of the survey population as a whole. They may be used to reduce the impact of attrition on the sample , or to correct for certain groups being over-sampled. Survival analysis is an analytical technique that uses time-to-event data to statistically model the probability of experiencing an event by a given time point.

For example, time to retirement, disease onset or length of periods of unemployment. The term used to refer to a round of data collection in a particular longitudinal study for example, the age 7 sweep of the National Child Development Study refers to the data collection that took place in when the participants were aged 7.

Note that the term wave often has the same meaning. The population of people that the study team wants to research, and from which a sample will be drawn. Time to event refers to the duration of time e.

Survival analysis can be used to analyse such data. Tracing or tracking describes the process by which study teams attempt to locate participants who have moved from the address at which they were last interviewed.

Unobserved heterogeneity is a term that describes the existence of unmeasured unobserved differences between study participants or samples that are associated with the observed variables of interest. The existence of unobserved variables means that statistical findings based on the observed data may be incorrect.

Part of the documentation that is usually provided with statistical datasets, user guides are an invaluable resource for researchers. The guides contain information about the study, including the sample , data collection procedures, and data processing.

Use guides may also provide information about how to analyse the data, whether there are missing data due to survey logic , and advice on how to analyse the data such the application of survey weights. Here are the list of 21 major differences between cross-sectional study and longitudinal study. They are:.

About Sandesh Adhikari Articles. Cross-sectional studies are used to assess the burden of disease or health needs of a population and are particularly useful in informing the planning and allocation of health resources. A cross-sectional survey may be purely descriptive and used to assess the burden of a particular disease in a defined population. A disadvantage of cross-sectional research is that it just tells researchers about differences, not true changes. Those are called cohort effects and they could affect our measurements.

Cross sectional study designs and case series form the lowest level of the aetiology hierarchy. In the cross sectional design, data concerning each subject is often recorded at one point in time. Cohort studies are used to study incidence, causes, and prognosis.

Because they measure events in chronological order they can be used to distinguish between cause and effect. Cross sectional studies are used to determine prevalence.

Longitudinal studies require enormous amounts of time and are often quite expensive.