How to handle discrepancies while you collect data for systemic review – Pubrica
                     How to Handle Discrepancies While you 
Collect Data for Systemic Review 
 
 
Dr. Nancy Agnes, Head, Technical Operations, Pubrica, [email protected] 
 
 
 
In-Brief a discrepancy was identified. Another author double-
 checked the data extraction. There was no masking, 
 (3)
Systematic reviews have studied rather than reports and disputes were settled by conversation . 
as the unit of interest. So, multiple reports of the  
same study need to be identified and linked together III. AVOIDING DATA EXTRACTION MISTAKES 
before or after data extraction. Because of the  
growing abundance of data sources (e.g., studies 1. Population specification error:The problem of 
registers, regulatory records, and clinical research calculating the wrong people or definition rather 
reports), review writers can determine which than the correct concept is known as a population 
sources can include the most relevant details for the specification error. When you don't know who to 
review and provide a strategy in place to address survey, no matter what data extraction tool you 
discrepancies if evidence were inconsistent use, the data analysis is slanted. Consider who 
(1)
throughout sources . The key to effective data you want to survey. Similarly, having population 
collection is creating simple forms and gathering definition errors occurs when you believe you 
enough clear data that accurately represents the have the correct sample respondents or 
source in a formal and ordered manner. definitions when you don't. 
 
I. INTRODUCTION 2. Sample Error:When a sampling frame does not 
 properly cover the population needed for a study, 
The systematic review is designed to find all sample frame error occurs. A sample frame is a 
experiments applicable to their research question and set of all the objects in a population. If you 
synthesize data about the design, probability of bias, choose the wrong sub-population to decide an 
and outcomes of those studies. As a result, decisions entirely alien result, you'll make frame errors are 
on how to present and analyze data from these studies a few examples of sample frames. A good 
significantly impact a systematic review. Data sampling frame allows you to cover the entire 
collected should be reliable, complete, and available target community or population. 
 (2)
for future updating and data sharing . The methods 3. Selection Error:A self-invited data collection 
used to make these choices must be straightforward, error is the same as a selection error. It comes 
and they should be selected with biases and human even though you don't want it. We've all 
error in mind. We define data collection methods prepared our sample frame before going out on 
used in a systematic review, including data extraction the field study. But what if a participant self-
directly from journal articles and other study papers. invites or participates in a study that isn't part of 
 our study? From the outset, the respondent is not 
II. DATA EXTRACTION FOR SYSTEMIC on our research's syllabus. When you choose an 
REVIEW incorrect or incomplete sample frame, the 
 analysis is automatically tilted, as the name 
One scientist extracted the characteristics and implies. Since these samples aren't important to 
findings of the observational cohort studies. The your research, it's up to you to make the right 
mainobjectives of each scientific analysis were also evidence-based decision. 
derived, and the studies were divided into two groups 4. Non-response Error:The higher the non-
based on whether they dealt with biased reporting or response bias, the lower the response rate. The 
source discrepancies. When the published results field data collection error refers to missing data 
were chosen from different analyses of the same data rather than an data analysis based on an incorrect 
with a given result, this was referred to as selective sample or incomplete data. It can be not easy to 
analysis reporting. When information was missing in maintain a high response rate on a large-scale 
one source but mentioned in another, or when the survey. Environmental or observational errors 
information provided in two sources was conflicting, may cause measurement errors. It's not the same 
 (4)
as random errors that have no known cause . 
 
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They established and used three criteria to determine IV. CONCLUSION 
methodological quality because there was no  
recognized tool to evaluate the empirical studies' Data extraction mistakes are extremely common. It 
organizational quality. may lead to significant bias in impact estimates. 
However, few studies have been conducted on the 
1. Self-determining data extraction by at least impact of various data extraction methods, reviewer 
two people characteristics, and reviewer training on data 
extraction quality. As a result, the evidence base for 
2. Definition of positive and negative findings. existing data extraction criteria appears to be lacking 
because the actual benefit of a particular extraction 
3. Safety of selective reporting bias in the process (e.g. independent data extraction) or the 
empirical study composition of the extraction team (e.g. experience) 
has not been adequately demonstrated. It is 
For each study, two authors independently evaluated unexpected, considering that data extraction is such 
these things. Since the first author was personally an important part of a systematic review. More 
involved in the study's design, an independent comparative studies are required to gain a better 
assessor was invited to review it. Any discrepancies understanding of the impact of various extraction 
were resolved through a consensus discussion with a methods. Studies on data extraction training, in 
third reviewer who was not concerned with the particular, are required because no such work has 
 (5)
included studies . been done to date. In the future, expanding one's 
knowledge base will aid in the development of 
 
 
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successful training methods for new reviewers and 
 (6)
students . 
 
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