Sentences with phrase «missing data in this study»

Although the amount of missing data in this study is similar to other daily process studies (e.g., Gil et al., 2003) and managed statistically, it is possible that unforeseen factors (e.g., lack of time or energy) added to the missing data.

Not exact matches

A few years ago the New England fishing fleets were in despair because the fish were nowhere to be found; a biologist, who had been making a laboratory study of the temperature of fishes» stomachs, combined his data with some ocean temperature data and correctly suggested where the missing creatures might be found.

In future updates, if more eligible studies are included, we will explore the impact of including studies with high levels of missing data in the overall assessment of treatment effect by using sensitivity analysiIn future updates, if more eligible studies are included, we will explore the impact of including studies with high levels of missing data in the overall assessment of treatment effect by using sensitivity analysiin the overall assessment of treatment effect by using sensitivity analysis.

In future updates we will explore the impact of including studies with high levels of missing data in the overall assessment of treatment effect by using sensitivity analysiIn future updates we will explore the impact of including studies with high levels of missing data in the overall assessment of treatment effect by using sensitivity analysiin the overall assessment of treatment effect by using sensitivity analysis.

Done well, these methods of blending data can provide a result with strong statistical power, finding an effect that might be missed in a single study.

«As soon as you're missing a single species in your data matrix, you may be missing a key function that is only represented by that species,» says Jetz, who has studied functional traits in plants and vertebrate animals, particularly birds.

Conclusions were hampered by missing data: Of more than 1,000 studies, only 29 had two - years or more follow - up of at least 80 percent of the patients enrolled in the study.

As befitting an article published in the nation's leading statistics journal, it introduces new statistical techniques to deal with problems that often emerge in randomized field trials: 1) missing data (for instance, not all students who initially joined the study participated in the follow - up testing sessions), and 2) noncompliance (some students, for example, refused the vouchers that were offered to them).

On the other hand, any study that looks for data in obscure factors like eye movements can be justly criticized for missing the main event, despite the fact that it qualifies for publication in any number of relatively credible journals.

The researchers acknowledge three specific limitations in this study: challenges with validity and external reliability of the authentic instruction rating scale, missing data due to fluctuations in teacher participation during the duration of the study, and the inability to account for a variety of variables that may impact study outcomes due to the limited time frame and scope of the program in which the study took place

In many instances we inferred significance in what social studies teachers left unsaid in our data, and we identified numerous missed opportunities where educators might have taken advantage of Twitter's affordanceIn many instances we inferred significance in what social studies teachers left unsaid in our data, and we identified numerous missed opportunities where educators might have taken advantage of Twitter's affordancein what social studies teachers left unsaid in our data, and we identified numerous missed opportunities where educators might have taken advantage of Twitter's affordancein our data, and we identified numerous missed opportunities where educators might have taken advantage of Twitter's affordances.

Indeed, a new study of state assessment data indicates that 49 percent of all public schools in the country did not make AYP in the 2010 — 11 school year, a dramatic increase from the 39 percent that missed the mark the previous year.

Identification of Peer Effects with Missing Peer Data: Evidence from Project STAR This paper studies peer effects on student achievement among first graders randomly assigned to classrooms in

Teachers in the other school profiled in the same study dug more deeply into student test data, examining questions students had missed to determine what concepts learners were struggling to grasp.

All in all, the stats are interesting to see and it's fantastic that BlackBerry is appearing in such great numbers throughout the studies, but there's something missing from all the data as well that's likely on everyone's mind after reading this.

Interpolation — the main task of the women studying ballistics in WW2 — is the construction or guessing of missing data using only two known data points.

You could then check your ideas with real - world data and try to demonstrate that post-volcanic rings are missing, before concluding «the potential biases identified in our study necessarily impact all existing hemispheric - scale estimates» and «bolster the case for a significant influence of explosive volcanism on climate in past centuries».

Then, instead of throwing out the data as hopelessly compromised and starting the experiment over with these factors corrected, you (a) do a study estimating how miscalibrated, how defective and how improperly located your instruments were and apply adjustments to all past data to «correct» the improper reading, (b) you do a study to estimate the effect of the external factors at the time you discover the problem and apply adjustments to all past data to «correct» the effects of the external factors even though you have no idea what the effect of the external factor actually was for a given instrument at the time the data was recorded, because you only measured the effect years later and then at only some locations, (c) you «fill in» any missing data using data from other instruments and / or from other measurements by the same instrument, (d) you do another study to determine how best to deal with measurements from different instruments over different time periods and at different locations and apply adjustments to all past data to «correct» for differences between readings from different instruments over different time periods at different locations.

However, in 2005, subsequent peer - reviewed studies examining Christy and Spencer's data found that a missing sign and an arithmetic error meant that their findings, if not the insight of using NASA satellite readings, were flawed.

The Medieval Warm Period in Antarctica: How two one - data - point studies missed the target.

«Errors in external forcing data (Santer et al's preferred explanation) Internal variability (which has been supported by numerous previous studies, including posts at CE) Values of CO2 climate sensitivity that are too high (interesting new post on this over at ClimateAudit) Missing physical processes in the climate models (e.g. solar indirect effects).»

One study, in this issue of Science, presents sea temperature data implying that most of the missing heat has been stored deep in the Atlantic.

Bad values filter: We flagged and excluded from further study values that had pre-existing indicators of data quality problems associated with instrumental error, in - filling of missing data, and / or post-hoc manipulations.

I made a study on the rainfall of my country in 10 years, extrapolated some data that were missing whose frequency was insignificant.

Identified erroneous, missing, incomplete, or implausible data by reviewing study data in accordance with the Data Management Plan and applicable standardized data management procesdata by reviewing study data in accordance with the Data Management Plan and applicable standardized data management procesdata in accordance with the Data Management Plan and applicable standardized data management procesData Management Plan and applicable standardized data management procesdata management processes.

Given that data were collected at the aggregate level (not linked by individual student over time), it was not possible to examine participant characteristics in relation to missing data over the course of the study.

Limitations of the cohort study data included sample attrition45 and the prevalence of missing data.46 47 In derivation of the childhood summary measures we sought to include the full availability of data over each of the three childhood follow - up investigations.

Missing data for longitudinal analysis (HOME Inventory, maternal health, depression, social support, stressful life events, family functioning and experience of being a mother) were dealt with using a three - step procedure to provide a balance between maintaining study power and minimising bias in parameter estimates.27 28 First, participants who had not completed any data points for these outcomes were deleted from analysis.

A total of 7368 subjects were included in this study after exclusion of subjects with missing data and those who were self - employed or could not work.

Explain the effects of the factors listed above (sample size, choice of information criterion, missing data, sample composition, class balance, number of indicators, and conditional independence violations) on the accuracy of a latent class analysis in the context of designing future studies.

Finally, as in any study with missing outcome data, we can not exclude potential bias if missing values do depend on unobserved values (not missing at random).39

Although adjustment for sample loss and missing data suggested that this loss did not affect study validity, the differences in response rates should not be overlooked.

We will describe other missing data and dropouts / attrition for each included study in the «Risk of bias» table, and we will discuss the extent to which these missing data could alter the results or conclusions of the review.

Although some studies have replicated its original factor structure (e.g., [21]-RRB-, other studies have failed to reproduce the same factor structure (e.g., [22 — 24]-RRB-, and normative data for parent - ratings of older children in Sweden are missing.

This makes it possible to collect data from dental records and in the present study, only three children were excluded due to missing records.

In this study, forty - one of the 381 children were excluded from analysis because of missing data in relevant scaleIn this study, forty - one of the 381 children were excluded from analysis because of missing data in relevant scalein relevant scales.