What is the difference between prospective and retrospective

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.

Variables is the term that tends to be used to describe data items within a dataset. This information would then be coded using a code-frame and the results made available in the dataset in the form of a variable about occupation. Vulnerable groups refers to research participants who may be particularly susceptible to risk or harm as a result of the research process. Different groups might be considered vulnerable in different settings. The term can encompass children and minors, adults with learning difficulties, refugees, the elderly and infirm, economically disadvantaged people, or those in institutional care.

Additional consideration and mitigation of potential risk is usually required before research is carried out with vulnerable groups. The term used to refer to a round of data collection in a particular longitudinal study for example, the age 7 wave 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 sweep often has the same meaning. Another key distinction in longitudinal research is between prospective and retrospective studies:. In reality, many studies use both prospective and retrospective methods. Meanwhile, household panel studies , which may start interviewing participants in adulthood, often collect an array of retrospective information about past events.

Research case studies Explore our case studies of longitudinal research. Explore by topic Learn how longitudinal data can be used to study the major issues facing society today. Administrative data Administrative data is the term used to describe everyday data about individuals collected by government departments and agencies. Age effects Age effects relates to changes in an outcome as a result of getting older. Attrition Attrition is the discontinued participation of study participants in a longitudinal study.

Biological samples Biological samples is the term used for specimens collected from human subjects from which biological information, such as genetic markers, can be extracted for analysis. Body mass index Body mass index is a measure used to assess if an individual is a healthy weight for their height.

Boosted samples Boosted samples are used to overcome sample bias due to attrition or to supplement the representation of smaller sub-groups within the sample. CAPI Computer-assisted personal interviewing CAPI is a technique for collecting data from participants using computers to eliminate common errors such as questionnaire routing and data entry mistakes.

CASI Computer-assisted self-interviewing CASI is a technique for collecting data from participants using computers to eliminate common errors such as questionnaire routing and data entry mistakes. Categorical variable A categorical variable is a variable that can take one of a limited number of discrete values.

CATI Computer-assisted telephone interviewing CATI is a technique for collecting data from participants using computers to eliminate common errors such as questionnaire routing and data entry mistakes.

Censoring For some study participants the exact time of an event will not be known because either: the study ends or the analysis is carried out before they have had the event, or the participant drops out of the study before experiencing the event. Census Census refers to a universal and systematic collection of data from all individuals within a population.

Codebook A codebook is a document online or hard-copy that contains all the information about how a dataset has been coded, such that it can be deciphered by a researcher not familiar with the original coding frame. Coding Coding is the process of converting survey responses into numerical codes to facilitate data analysis.

Cognitive assessments Cognitive assessments are exercises used to measure thinking abilities, such as memory, reasoning and language. Cohort studies Cohort studies are concerned with charting the lives of groups of individuals who experience the same life events within a given time period. Complete case analysis Complete case analysis is the term used to describe a statistical analysis that only includes participants for which we have no missing data on the variables of interest.

Confounding Confounding occurs where the relationship between independent and dependent variables is distorted by one or more additional, and sometimes unmeasured, variables. Continuous variable A continuous variable is a variable that has an infinite number of uncountable values e.

Cohort effects Cohort effects relates to changes in an outcome associated with being a member of a specific cohort of people e. Coverage In metadata management, coverage refers to the temporal, spatial and topical aspects of the data collection to describe the comprehensiveness of a dataset.

Cross-sectional Cross-sectional surveys involve interviewing a fresh sample of people each time they are carried out. This may provide more accurate results.

Retrospective cohort studies: This study is also known as the historic cohort study. This study is done to analyze the effect of a factor on the occurrence of the disease. These studies may help to analyze multiple outcomes. Case series and case reports: Case reports are a type of retrospective study in which the researcher reports symptoms or instructive case that was not previously seen with a medical condition. Case reports are a group of multiple unusual case series. Case-Control Studies: Case-control studies are better than case series and case reports.

This is because there is a control arm in these studies which provides an effective comparison. Prospective studies are done in the present to analyze the outcome in the future. In the prospective study, the information is required to be generated and is not available before the start of the study. An example of a prospective study is to follow-up with a group of alcohol drinkers to identify whether drinking alcohol for years is linked to liver disease.

It is to be noted that none of the subjects enrolled under the study should have the outcome of interest. For instance, in the above example, none of the subjects should have liver disease before the start of the study. The follow-up is an important and inherent property of prospective study and loss of follow-up may affect the outcome of the study.

New disease risk factors: Prospective studies are important in analyzing the risk factors for new diseases for which the data is not available. This will help in the effective management of the disease. The data collected for the retrospective study may be gotten from medical reports from previous diagnoses, old articles, magazines, newspapers, etc.

A prospective study, on the other hand, does not use old magazines and newspapers as a data collection tool. Data is mostly collected by observing and interviewing the subject. The reason is clear from the fact that prospective study involves events that are yet to occur. There are usually no old or existing reports of the event. The researchers are expected to perform independent research whose record may now be kept for the future retrospective study.

In a retrospective study, the data may be analyzed immediately the investigator has access to it because there is enough data collected during a period to get meaningful assertions.

In prospective study, on the other hand, needs some time before data analysis can take place. Data analysis cannot take place until there is enough outcome to make an assertion. So, time must elapse before you can compare the incidence. There is no condition to the amount of time that must elapse before the data is analyzed. Rather, it depends solely on the type of research that is being carried out, and at the researcher's discretion.

Retrospective studies are conducted on a small scale and do not require much time to complete, unlike the prospective surveys. In the medical context, they have the potential to address rare diseases. For example, there are reports that Ebola has been eliminated from Nigeria and some other African countries where it was. Although not common at the moment, a retrospective study might be performed by investigating people who were cured of Evola in the past.

In a prospective study, there is usually room for fewer biases compared to a retrospective study. Also, a prospective study has more useful applications in modern medicine.

A prospective cohort study is usually very expensive compared to a retrospective cohort study. This is usually because there are no available material or record that contains relevant data that shows the required outcome.

Therefore, all the data will have to be collected by the researcher, and therefore incurring more cost than the retrospective study. Although there is usually data to choose from when dealing with the retrospective study, sometimes, this data may be unavailable, making it difficult to perform proper investigation.

Since this is something that has happened in the past, it might not be easy to gather this data again. In some cases, this data may be corrupted and inaccurate as it is being passed from one source to another. Retrospective cohort study is mostly conducted to build on existing research or discovery while prospective cohort study helps to make discoveries.

In some cases, we could say prospective studies are used to discover new theories. When conducting a study to discover the reason why an event occurred we use a retrospective study. But if we want to discover if it will occur, we settle for a prospective study. They can be used for the same purpose, but through different approaches. For example, a researcher who wants to find out what the symptoms of malaria are can use both retrospective and prospective study.

For the retrospective study, people who have had malaria in the past will be investigated to discover the symptoms they had and the common ones will be mapped. In the case of a prospective study, people who are not vaccinated against malaria will be studied to see if they will eventually get the disease. If they get the disease, a report of the symptoms noticed during the study will be reported. Retrospective study is conducted on a smaller scale, and as such make use of a small sample size.

Two sample sizes may be taken and compared together, but they are usually small samples. A prospective study, on the other hand, is conducted on a larger scale compared to a retrospective study. This may likely be because it may be difficult to find subjects for the cohort because some of them may be dead or unwilling to share their past. Or especially if it is a case of rape and may bring back memories of the rape. The cost of performing a retrospective survey is lesser than a prospective survey.

Case-Control studies are usually but not exclusively retrospective, the opposite is true for cohort studies. The following notes relate case-control to cohort studies:. Cohort studies are usually but not exclusively prospective, the opposite is true for case-control studies. The following notes relate cohort to case-control studies:. Download a free trial here. Prospective vs.