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research chapter 8
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- 1autoregressive integrated moving average (ARIMA).Three steps are part of using ARIMA: a model is identified that could serve as the basis of the serial effects, the model parameters are estimated and the model fit is checked. When a researcher proposes three models for the underlying serial effects, all models go through these three steps and the one that best fits is chosen.
- 2Autocorrelationdescribes the relationship of dependency between the data points (or observations). There is regular autocorrelation and seasonal autocorrelation. In the former data points that are bordering each other are dependent and in the latter data points are dependent in terms of cycles or periods.
- 3single caseSeveral observations can also be made in one session, called single case. The experimenter controls the intervention. This design occupies itself mostly with causal reasoning in normal life situations. For example, if a doctor gives a patient an injection after they stepped on a rusty nail, this injection cannot be made into the same foot as the swelling from the injection could not be distinguished from the effects of the rusty nail.
- 4advantage of single-case experimental designsthey are low cost as one subject can serve as part of the control and treatment condition. Another advantage is that they also measure effects at several time points throughout all stages of the experiment and do not simply obtain only one cross-section of effect in one point in time. Yet, conducting experiments with this type of design takes a lot of time. In addition the question remains of how generalizing the results are if there were only a minimum amount of research subjects participating.
- 5cross-lagged panel designsome data points receive a temporary lag in their treatment regarding the outcome measure values. Panel study, a term from research in sociology, means that research is conducted in a longitudinal manner. Longitudinal research allows a participant to be placed in all conditions over time of the research and it takes into account how they respond differently over the time period.
- 6if no correlation can be found in the cross-lagged panelEven if no correlation can be observed in the cross-lagged correlational design, we still cannot conclude that there is no causation, as the design might have overlooked a causal relationship.
- 7three forms of paired correlationstest-retest correlations (rA1A2 and rB1B2), synchronous correlations (rA1B1 and RA2B2), and cross-lagged correlations (rA1B2 and rB1A2)
- 8Temporal erosiondescribes a decreasing correlation that we might obtain after a longer period of time has passed. Attenuation is the reduction in correlation that we observe.
- 9Path analysisdescribes a non-experimental approach whose goal is to infer causation. Often faced with controversy, this approach works by trying to eliminate associations between the variables in order to crystallize out the causal influence and exclude any influences that are not the cause.
- 10The third-variable problempresents the problem of the confounding variable. This variable is hidden and is responsible for a causal relationship between two variables, by being correlated with each variable itself. Another problem in non-experimental studies that aim to infer causation is the mediator variable, a variable which intrudes between X and Y.
- 11Cohort in longitudinal researchWhen talking about longitudinal research, the issue of cohorts has to be addressed. A cohort is characterized by people who were all born in the same generation and have hence lived through similar historical and personal important life events at approximately the same time. Longitudinal studies make use of prospective data collection, the same as with a panel study. In addition, longitudinal studies also take in information of the past, meaning data is gathered retrospectively as well. Longitudinal studies are not only commonly used in research in the social and behavioral sciences, but also in medicine and any human developmental research.
- 12fallacy of period centrismoccurs when generalization of the results yielded from analyzing a certain time period occurs, saying that other time periods would yield the same results. That is why plotting according to cohort is important when it is possible.
- 13age effectoccurs with the natural process of aging and examines the average changes happening. When events take place in chronological order and these are measured at their occurrence, this is a time-of-measurement effect. When we look at generations and measurements of this generation (or cohort), we speak of a cohort effect.
- 14When working with longitudinal or cross-sectional designs, we pay attention to what ?age, period, and cohort. It is not possible to account for all three variables and their effects concurrently. That is why the following designs show a lack in at least one of those variables:
- 15simple cross-sectional designthe observation of subjects that are not the same age is being done. This causes the problem though that all people of the same age are seen as from the same generation, confounding the participants age.
- 16simple longitudinal designWhen periodical observations of a cohort are made, we are using a simple longitudinal design. Yet, historical events that might occur are confounding variables to the results.
- 17cohort-sequential designis allowing the studying of cohorts and allows examining the age, but disregards time of measurement to a certain extent.
- 18time-sequential designneither the ages of the participants nor the times of observation are the same. Age and time are taken into account while cohort is being neglected to some extent.
- 19cross-sequential designdoes not fully account for age, but does so for cohort and time of measurement. Over several periods the observation of different cohorts is made but firstly a measure in the same period is made.
- 20Subclassification on Propensity ScoresPropensity scores are made by looking at variables of treated (or manipulation) and untreated (observed) participants and putting all those that differ into one composite variable. This composite variable is hence a summary of all differences from all the variables, which are also called covariates.
By making subclasses, certain differences such as age differences can be adjusted for. With age differences, subclasses of similar ages would be made and then all conditions are tested in each subclass to make a comparison of conditions in each subclass possible. All conditions must have the same amount of subclasses and the more subclasses there are the more precise the analysis is. - 21Multiple Confounding CovariatesIf there are more than one confounding covariates then one has to adjust not only for differences in age but also another for another covariate. The propensity score is then estimated by assuming that there are multiple confounding covariates. All confounding covariates are combined, yielding one single confounding covariate. A participant is put into either group 1 or group 2 depending on the probability, which is built on the participant’s covariates scores, predicting the group membership. The group membership, and not the confounding covariates, is hence responsible for the differences in outcomes.
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- Psychologie Flashcards on research chapter 8 , created by Adrian Mayer on 04/05/2018.
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