This enables researchers to make comparisons both within and across studies. Repeating the same longitudinal analysis across a number of studies allows researchers to test whether results are consistent across studies, or differ in response to changing social conditions.
Data imputation is a technique for replacing missing data with an alternative estimate. There are a number of different approaches, including mean substitution and model-based multivariate approaches. Data linkage simply means connecting two or more sources of administrative, educational, geographic, health or survey data relating to the same individual for research and statistical purposes. For example, linking housing or income data to exam results data could be used to investigate the impact of socioeconomic factors on educational outcomes.
Data protection refers to the broad suite of rules governing the handling and access of information about people. Data protection principles include confidentiality of responses, informed consent of participants and security of data access. Data structure refers to the way in which data are organised and formatting in advance of data analysis. In analysis, the dependent variable is the variable you expect to change in response to different values of your independent or predictor variables. A derived variable is a variable that is calculated from the values of other variables and not asked directly of the participants.
It can involve a mathematical calculation e. Diaries are a data collection instrument that is particularly useful in recording information about time use or other regular activity, such as food intake. They have the benefit of collecting data from participants as and when an activity occurs.
As such, they can minimise recall bias and provide a more accurate record of activities than a retrospective interview. Dissemination is the process of sharing information — particularly research findings — to other researchers, stakeholders, policy makers, and practitioners through various avenues and channels, including online, written publications and events.
Dissemination is a planned process that involves consideration of target audiences in ways that will facilitate research uptake in decision-making processes and practice.
Dummy variables , also called indicator variables , are sets of dichotomous two-category variables we create to enable subgroup comparisons when we are analysing a categorical variable with three or more categories. Empirical data refers to data collected through observation or experimentation. Analysis of empirical data can provide evidence for how a theory or assumption works in practice. In metadata management, fields are the elements of a database which describes the attributes of items of data.
General ability is a term used to describe cognitive ability, and is sometimes used as a proxy for intelligent quotient IQ scores. Growth curve modelling is used to analyse trajectories of longitudinal change over time allowing us to model the way participants change over time, and then to explore what characteristics or circumstances influence these patterns of longitudinal change.
Hazard rate refers to the probability that an event of interest occurs at a given time point, given that it has not occurred before. Health assessments refers to the assessments carried out on research participants in relation to their physical characteristics or function.
These can include measurements of height and weight, blood pressure or lung function. Heterogeneity is a term that refers to differences, most commonly differences in characteristics between study participants or samples.
It is the opposite of homogeneity, which is the term used when participants share the same characteristics. Where there are differences between study designs, this is sometimes referred to as methodological heterogeneity.
Both participant or methodological differences can cause divergences between the findings of individual studies and if these are greater than chance alone, we call this statistical heterogeneity. See also: unobserved heterogeneity. Household panel surveys collect information about the whole household at each wave of data collection, to allow individuals to be viewed in the context of their overall household. To remain representative of the population of households as a whole, studies will typically have rules governing how new entrants to the household are added to the study.
As a way of encouraging participants to take part in research, they may be offered an incentive or a reward. These may be monetary or, more commonly, non-monetary vouchers or tokens.
Incentives are advertised beforehand and can act as an aid to recruitment; rewards are a token of gratitude to the participants for giving their time. In analysis, an independent variable is any factor that may be associated with an outcome or dependent variable. For example, the number of hours a student spends on revision may influence their test result. A key principle of research ethics , informed consent refers to the process of providing full details of the research to participants so that they are sufficiently able to choose whether or not to consent to taking part.
To put it another way, it is a measure of how thin or fat the lower and upper ends of a distribution are. It centres on the individual and emphasises the changing social and contextual processes that influence their life over time. Many longitudinal studies focus upon individuals, but some look at whole households or organisations. Metadata refers to data about data, which provides the contextual information that allows you to interpret what data mean. Missing data refers to values that are missing and do not appear in a dataset.
This may be due to item non-response, participant drop-out or attrition or, in longitudinal studies , some data e. Large amounts of missing data can be a problem and lead researchers to make erroneous inferences from their analysis.
There are several ways to deal with the issue of missing data, from casewise deletion to complex multiple imputation models. Multi-level modelling refers to statistical techniques used to analyse data that is structured in a hierarchical or nested way.
For example. Multi-level models can account for variability at both the individual level and the group e. Non-response bias is a type of bias introduced when those who participate in a study differ to those who do not in a way that is not random for example, if attrition rates are particularly high among certain sub-groups. Non-random attrition over time can mean that the sample no longer remains representative of the original population being studied.
Two approaches are typically adopted to deal with this type of missing data : weighting survey responses to re-balance the sample , and imputing values for the missing information. Panel studies follow the same individuals over time. They vary considerably in scope and scale. Examples include online opinion panels and short-term studies whereby people are followed up once or twice after an initial interview. Peer review is a method of quality control in the process of academic publishing, whereby research is appraised usually anonymously by one or more independent academic with expertise in the subject.
Period effects relate to changes in an outcome associated with living during a particular time, regardless of age or cohort membership e. Piloting is the process of testing your research instruments and procedures to identify potential problems or issues before implementing them in the full study.
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. Nurses were selected as the study population because of their knowledge about health and their ability to provide complete and accurate information regarding various diseases due to their nursing education.
They were relatively easy to follow over time and were motivated to participate in a long-term study. The cohort was limited to married women due to the sensitivity of questions about contraceptive use at that time. The original focus of the study was on contraceptive methods, smoking, cancer, and heart disease, but has expanded over time to include research on many other lifestyle factors, behaviours, personal characteristics, and also other diseases.
Cohort studies can also be retrospective. Retrospective cohorts are also called historical cohorts. Health records of a certain group of patients would already have been collected and stored in a database, so it is possible to identify a group of patients — the cohort — and reconstruct their experience as if it had been prospectively followed up.
Although patient information was probably collected prospectively, the cohort would not have initially identified the goal of following individuals and investigating the association between risk factor and outcome. In a retrospective study, it is likely that not all relevant risk factors have been recorded. This may affect the validity of a reported association between risk factor and outcome when adjusted for confounding. In addition, it is possible that the measurement of risk factors and outcomes would not have been as accurate as in a prospective cohort study.
Many of the advantages and disadvantages of retrospective cohort studies are similar to those of prospective studies. As previously described, retrospective cohort studies are typically constructed from previously collected records, in contrast to prospective design, which involves identification of a prospectively followed group, with the objective of investigating the association between one or more risk factors and outcome.
However, an advantage to both study designs is that exposure to risk factors can be recorded before the outcome occurs. This is important because it allows the sequence of risk and outcome factors to be evaluated. Use of previously collected and stored records in a database indicates that the retrospective cohort study is relatively inexpensive and quick and easy to perform.
However, with retrospective cohorts, it is possible that not all relevant risk factors have been identified and recorded. Another disadvantage is that many health professionals will have become involved in patient care, making the measurement of risk factors and outcomes less consistent than that achieved with a prospective study design. Rock and pop fame is associated with risk taking, substance use and premature mortality.
This retrospective cohort study [9] examined the relationships between fame and premature mortality and tested how these relationships vary with the type of performer solo or band member and nationality and whether the cause of death was linked to adverse childhood experiences.
The cohort included 1, rock and pop stars that reached fame between and The study examined the risk and protective factors for star mortality, relative contributions of adverse childhood experiences and other performance characteristics to cause premature death between rock and pop stars. 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.
Investigators tend to spend less on data collection and may even take lesser years of observation. In most investigations, data collection is known to cost more.
Therefore, less data collection will result in less cost. Since the investigator is starting afresh when performing a prospective study, it requires more data collection and will automatically cost more. The time taken when performing a prospective study is usually longer compared to the retrospective study. In a retrospective study, there is already available data for the researcher to extract from, thereby reducing the time spent on data collection.
Therefore, the only thing that is required is to make longitudinal observations and perform data analysis. A prospective study, on the other hand, involves waiting for the outcome of interest to showcase before analysis can be done. Some time will be spent gathering subjects, collecting background data, and waiting for enough data to be gathered before analysis will eventually take place.
The cohort required for a prospective study is usually a set of people who have not experienced the event before, but are just prone to it. A retrospective study requires a cohort that has experienced the event in the past. There are clear differences in the kind of cohort required for retrospective and prospective study. For example, a medical researcher is investigating the causes of HIV in humans. A retrospective approach will recruit subjects who are HIV positive, while a prospective approach will recruit people who are HIV negative but are at risk of contracting the disease.
This may include people who have sex without protection, share clipper with a 3rd party, have an HIV positive partner, etc. Prospective and retrospective studies see both examples of longitudinal studies. The process involved in these studies is almost the same, except for the fact that prospective studies are done before the outcome and retrospective survey is done after.
Also, the data used for prospective and retrospective studies can be collected using similar tools. For example, interviews, online forms, questionnaires, diagnosis, etc. This includes the first data collected before the initial outcome i. Although there are some differences in the process of data collection, they do interact at some point.
After collecting the data, the next step is data analysis, which is very similar in prospective and retrospective studies. It is noteworthy that the data analysis process in a prospective study is retrospective.
The data collected will take some time before it is analyzed because data analysis cannot take place until enough events or outcomes have occurred. This is where the analysis becomes retrospective. A case-control study is a kind of study designed to determine if an event is associated with an outcome. For example, if having unprotected sex is associated with contracting HIV. There are different steps involved in case-control, but the first thing is to identify the cases i. After then, you should look back in time to know the members of the case who were exposed to unprotected sex and compare the frequency of the exposure in the case-cohort to the control cohort.
Also known as a case-referent study, the process is said to be retrospective because it starts with an outcome, before tracing it back to the exposures. It is also a type of epidemiological study and is often mistaken for a cohort study. By relationships between prospective and retrospective studies, case-control is more of a retrospective study compared to a prospective study. Therefore, there is not much relationship between prospective study and case-control.
Case-control studies are relatively inexpensive and frequently used because it can be carried out by small teams and individual researchers. Compared to the prospective cohort study, they are cheaper and tend to take less time. A cohort study is an important aspect of epidemiological research, understanding risk factors and also providing a possible treatment for diseases in various participants all over the world.
Groundbreaking medical discoveries, vaccines, antidotes, medications, etc are arrived at through cohort study. Although useful in other aspects, it is mostly used in the medical field to make investigations that uncover new diseases, symptoms, and cures. This approach does bring with it some challenges that are often related to sample size complexity, longevity, etc. However, if carefully planned and implemented, a cohort study can make valuable contributions to different sectors of the economy.
To do this, one should have a proper understanding of the different types of a cohort study and its relationship with other important study techniques. Want to conduct a cohort study? There are many ways to select a sample for your systematic investigation—some researchers rely on probability sampling techniques while What happens in experimental research is that the researcher alters the independent variables so as to determine their impacts on the Hypothesis testing is as old as the scientific method and is at the heart of the research process.
Research exists to validate or disprove More often than not, researchers struggle with outcomes that are inconsistent with the realities of the target population. While there are
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