When longitudinal studies are observational, in the sense that they observe the state of the world without manipulating it, it has been argued that they may have less power to detect causal relationships than experiments.
However, because of the repeated observation at the individual level, they have more power than cross-sectional observational studies by virtue of being able to exclude time-invariant unobserved individual differences and also by virtue of observing the temporal order of events. Some of the disadvantages of longitudinal studies include the fact that they take a lot of time and are very expensive. Therefore, they are not very convenient. Longitudinal studies allow social scientists to distinguish short-term from long-term phenomena, such as poverty.
It is impossible to conclude which of these possibilities is the case by using one-off cross-sectional studies. Types of longitudinal studies include panel studies and cohort studies. A retrospective study is a longitudinal study that looks back in time. Another possible reason for changing measures is poor psychometric properties of scales used in earlier data collection. Previously, researchers have used transformed scores e. In response to critiques of these scaling methods, new procedures have been developed to model longitudinal data using changed measurement e.
Recently, McArdle and colleagues proposed a joint model approach that estimated an item response theory IRT model and latent curve model simultaneously. They provided a demonstration of how to effectively handle changing measurement in longitudinal studies by using this new proposed approach.
Unfortunately, this creates problems at the interpretation stage. Random walk variables are dynamic variables that I mentioned earlier when describing the computational modeling approach. These variables have some value and are moved from that value. Although the general trend of retirement savings should be positive i.
The random walks i. Indeed, one way to know if one is measuring a dynamic variable is if one observes a simplex pattern among inter-correlations of the variable with itself over time. In a simplex pattern, observations of the variable are more highly correlated when they are measured closer in time e.
Of course, this pattern can also occur if its proximal causes rather than itself is a dynamic variable. As noted, dynamic or random walk variables can create problems for poorly designed longitudinal research because one may not realize that the level of the criterion Y , say measured at Time 3, was largely near its level at Time 2, when the presumed cause X was measured.
That is, the criterion variable Y at Time 1 is actually causing the presumed causal variable X at Time 2. For example, performances might affect self-efficacy beliefs such that self-efficacy beliefs end up aligning with performance levels. This is why the multiple wave measurement practice is so important in passive observational panel studies. However, the multiple waves of measurement might still create problems for random walk variables, particularly if there are trends and reverse causality.
Consider the self-efficacy to performance example again. If performance is trending over time and self-efficacy is following along behind, a within-person positive correlation between self-efficacy and subsequent performance is likely be observed even if there is no or a weak negative causal effect because self-efficacy will be relatively high when performance is relatively high and low when performance is low. Meanwhile, there are other issues that random walk variables may raise for both cross-sectional and longitudinal research, which Kuljanin et al.
A related issue for longitudinal research is nonindependence of observations as a function of nesting within clusters. This issue has received a great deal of attention in the multilevel literature e. However, there is one more nonindependence issue that has not received much attention. Thus, on average it will appear the variable is negatively causing itself. Fortunately, this problem is quickly mitigated by more waves of observations and more cases i. State-of-the-art advice is to use maximum likelihood ML: EM algorithm, Full Information ML or multiple imputation MI techniques, which are particularly superior to other missing data techniques under the MAR missingness mechanism, and perform as well as—or better than—other missing data techniques under MCAR and MNAR missingness mechanisms MAR missingness is a form of systematic missingness in which the probability that data are missing on one variable [ Y ] is related to the observed data on another variable [ X ].
Also, when it comes to selecting a missing data technique to analyze incomplete data, one of the above techniques e. One cannot safely avoid the decision altogether—that is, abstinence is not an option. One must select the least among evils. Rogelberg et al. Listwise deletion can further lead to extreme reductions in statistical power. Next, single imputation techniques e. Unfortunately, researchers often get confused into thinking that multiple imputation suffers from the same problems as single imputation; it does not.
In multiple imputation, missing data are filled in several different times, and the multiple resulting imputed datasets are then aggregated in a way that accounts for the uncertainty in each imputation Rubin, The operative word in multiple imputation is multiple , not imputation. Longitudinal modeling tends to involve a lot of construct- or variable-level missing data i. Such conditions create many partial nonrespondents, or participants for whom some variables have been observed and some other variables have not been observed.
Thus a great deal of missing data in longitudinal designs tends to be MAR e. Lastly, because these newer missing data techniques incorporate all of the available data, it is now increasingly important for longitudinal researchers to not give up on early nonrespondents.
Attrition need not be a permanent condition. If a would-be respondent chooses not to reply to a survey request at Time 1, the researcher should still attempt to collect data from that person at Time 2 and Time 3. Applying this advice to longitudinal research on aging and retirement, it means that even when a participant fails to provide responses at some measurement points, continuing to make an effort to collect more data from the participant in subsequent waves may still be worthwhile.
It will certainly help combat the issue of attrition and allow more usable data to emerge from the longitudinal data collection. I think there are two questions here: How to model longitudinal data of categorical variables, and how to model discontinuous change patterns of variables over time. In terms of longitudinal categorical data, there are two types of data that researchers typically encounter. One type of data comes from measuring a sample of participants on a categorical variable at a few time points i.
The research question that drives the data analyses is to understand the change of status from one time point to the next. For example, researchers might be interested in whether a population of older workers would stay employed or switch between employed and unemployed statuses e. To answer this question, employment status employed or unemployed of a sample of older workers might be measured five or six times over several years.
When transition between qualitative statuses is of theoretical interest, this type of panel data can be modeled via Markov chain models. The simplest form of Markov chain models is a simple Markov model with a single chain, which assumes a the observed status at time t depends on the observed status at time t —1, b the observed categories are free from measurement error, and c the whole population can be described by a single chain. The first assumption is held by most if not all Markov chain models.
In addition, because the observations may contain measurement error, a number of different observed patterns over time could reflect the same underlying latent transition pattern in qualitative status. This way, a large number of observed patterns e. It is also important to note that subpopulations in a larger population can follow qualitatively different transition patterns.
Another type of longitudinal categorical data comes from measuring one or a few study units on many occasions separated by the same time interval e. Studies examining this type of data mostly aim to understand the temporal trend or periodic tendency in a phenomenon.
For example, one can examine the cyclical trend of daily stressful events occurred or not over several months among a few employees. Another example is the study of performance of a particular player or a sports team i. The research question could be to find out time-varying factors that could account for the cyclical patterns of game performance. The statistical techniques typically used to analyze this type of data belong to the family of categorical time series analyses. A detailed technical review is beyond the current scope, but interested readers can refer to Fokianos and Kedem for an extended overview.
In terms of modeling discontinuous change patterns of variables, Singer and Willett and Bollen and Curran provided guidance on modeling procedures using either the multilevel modeling or structural equation modeling framework.
Here I briefly discuss two additional modeling techniques that can achieve similar research goals: spline regression and catastrophe models. Then due to a critical organizational event e. A spline model can be used to capture the dramatic change in the trend of newcomer attitude as a response to the event see Figure 4 for an illustration of this example. The time points at which the variable changes its trajectory are called spline knots. At the spline knots, two regression lines connect. Location of the spline knots may be known ahead of time.
However, sometimes the location and the number of spline knots are unknown before data collection. In general, spline models can be considered as dummy-variable based models with continuity constraints. For example, some systems in organizations develop from one certain state to uncertainty, and then shift to another certain state e.
This nonlinear dynamic change pattern can be described by a cusp model, one of the most popular catastrophe models in the social sciences. Researchers have applied catastrophe models to understand various types of behaviors at work and in organizations see Guastello, for a summary.
Estimation procedures are also readily available for fitting catastrophe models to empirical data see technical introductions in Guastello, Generally, but mostly on the conceptual level, I think we will see an increased use of computational models to assess theory, design, and analysis.
Indeed, I think this will be as big as multilevel analysis in future years, though the rate at which it will happen I cannot predict. The primary factors slowing the rate of adoption are knowledge of how to do it and ignorance of the cost of not doing it cf. Vancouver, Tamanini et al.
Factors that will speed its adoption are easy-to-use modeling software and training opportunities. On the methodology level I think research simulations i. They offer a great deal of control and the ability to measure many variables continuously or frequently.
I predict that significant advances in various areas will be made in the near future through the appropriate application of mixture latent modeling approaches. They could also integrate continuous variables and discrete variables, as either predictor or outcome variables, in a single analytical model to describe and explain simultaneous quantitative and qualitative changes over time.
Despite or rather because of the power and flexibility of these advanced mixture techniques to fit diverse models to longitudinal data, I will repeat the caution I made over a decade ago—that the application of these complex models to assess changes over time should be guided by adequate theories and relevant previous empirical findings Chan, My hope or wish for the next big thing is the use of longitudinal methods to integrate the micro and macro domains of our literature on work-related phenomena.
This will entail combining aspects of growth modeling with multi-level processes. Although I do not have a particular conceptual framework in mind to illustrate this, my reasoning is based on the simple notion that it is the people who make the place. The analytical tools exist for undertaking such analyses. What are lacking at this point are the conceptual frameworks. If most theories are indeed theories of change, then this advancement promises to revolutionize what passes for theory in the organizational sciences i.
My preferred approach is iterative: a authors first collect longitudinal data, then b inductively build a parsimonious computational model that can reproduce the data, then c collect more longitudinal data and consider its goodness of fit with the model, then d suggest possible model modifications, and then repeat steps c and d iteratively until some convergence is reached e. Exactly how to implement all the above steps is not currently well known, but developments in this area can potentially change what we think good theory is.
I will play it safe and treat it as the latter. Consistent with several other responses to this question, I hope that researchers will soon begin to incorporate far more complex dynamics of processes into both their theorizing and their methods of analysis.
Although process dynamics can and do occur at all levels of analysis, I am particularly excited by the prospect of linking them across at least adjacent levels. For example, basic researchers interested in the dynamic aspects of affect recently have begun theorizing and modeling emotional experiences using various forms of differential structural equation or state-space models e.
Chow et al. These models extend LGMs to include a broader array of sources of change e. All of these models share a common interest in modeling the underlying dynamic patterns of a variable e. I believe that applying a dynamical systems framework will greatly advance our research. Applying the dynamic systems framework e. Dynamic systems models can also answer the why question better by specifying how elements of a system work together over time to bring about the observed change at the system level.
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Montfort H. Oud and A. Satorra et al. Mahwah, NJ : Lawrence Erlbaum. McArdle J. Latent variable modeling of differences and changes with longitudinal data. Data cleaning is an important preliminary step in the data analysis process and involves preparing a dataset so that it can be correctly analysed. Data harmonisation involves retrospectively adjusting data collected by different surveys to make it possible to compare the data that was collected.
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.
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