Common Biases In Business Research

In research, bias occurs when an error is introduced into sampling or testing which results in selecting or encouraging one outcome, conclusion, or answer over others. Bias can happen at any phase of research, including study design, methodology selection, data collection, and stating conclusions [1]. Given the significant threats of these biases on the reliability and validity of research conclusions, understanding different types of biases, their consequences, and treatment methods is the corner stone in avoiding such biases and an important step in critically evaluating research. This chapter discusses biases that are common in quantitative research, biases associated with quantitative research and biases that usually occur in quantitative research using qualitative data. It will focus on introducing business researchers to their definitions and sources. The chapter also suggests methods to uncover those biases and provides remedies and ways to deal with such biases

The Status of Loggerhead, Caretta caretta; Kemp's Ridley, Lepidochelys kempi; and Green, Chelonia mydas, Sea Turtles in U.S. Waters: A Reconsideration

Assessing the status of widely distributed marine species can prove difficult because virtually every sampling technique has assumptions, limitations, and biases that affect the results of the study. These biases often are overlooked when the biological and nonbiological implications of the results are discussed. In a recent review, Thompson (1988) used mostly unpublished population census data derived from studies conducted by the National Marine Fisheries Service (NMFS) to draw conclusions about the status of Kemp's ridley, Lepidochelys kempi; Atlantic coast green turtles, Chelonia mydas; and the loggerhead sea turtle, Caretta caretta

Characterizing sampling biases in the trace gas climatologies of the SPARC Data Initiative

Monthly zonal mean climatologies of atmospheric measurements from satellite instruments can have biases due to the non-uniform sampling of the atmosphere by the instruments. We characterize potential sampling biases in stratospheric trace gas climatologies of the Stratospheric Processes and their Role in Climate (SPARC) Data Initiative using chemical fields from a chemistry climate model simulation and sampling patterns from 16 satellite-borne instruments. The exercise is performed for the long-lived stratospheric trace gases O3 and H2O. Monthly sample biases for O3 exceed 10% for many instruments in the high latitude stratosphere and in the upper troposphere/lower stratosphere, while annual mean sampling biases reach values of up to 20% in the same regions for some instruments. Sampling biases for H2O are generally smaller than for O3, although still notable in the upper troposphere/lower stratosphere and Southern Hemisphere high latitudes. The most important mechanism leading to monthly sampling bias is the non-uniform temporal sampling of many instruments, i.e., the fact that for many instruments, monthly means are produced from measurements which span less than the full month in question. Similarly, annual mean sampling biases are well explained by non-uniformity in the month-to-month sampling by different instruments. Non-uniform sampling in latitude and longitude are shown to also lead to non-negligible sampling biases, which are most relevant for climatologies which are otherwise free of sampling biases due to non-uniform temporal sampling

Internet mobility survey sampling biases in measuring frequency of use of transport modes

We develop a quantitative analysis of the biases that arise when measuring trip frequencies for a general population through an online survey instrument. Data from a national official survey in Italy, concerning both mobility behaviors and skills in using computers and internet, have been deployed to assess differences in mobility levels between those that can answer a computer/internet survey and those that cannot. Positive correlations were found between ability in using ICT tools and trip frequencies. These latter are about 15% to 150% higher for the "ICT literate", according to the travel means under consideration. A Heckman sample selection model showed us that these biases have different explanations. People knowing how to use internet are different from the others in they car driving behavior due to a range of self-related factors. Conversely, public transport patterns of use are more similar between the two groups: the observed bias is mainly due to the fact of using internet in itself, which could for example lead to a more active lifestyle. Such distinction is of practical interest because it can help defining a method to correct these biases. According to our results, the overestimation of public transport frequency of use of an internet survey could be corrected by looking at the internet diffusion in the population. On the contrary, corrections for car driving frequencies are more complex and should be based on differences in attitudinal and personal characteristics between internet survey respondents and the remainder of the populatio

Estimating sampling biases and measurement uncertainties of AIRS/AMSU-A temperature and water vapor observations using MERRA reanalysis

We use MERRA (Modern Era Retrospective-Analysis for Research Applications) temperature and water vapor data to estimate the sampling biases of climatologies derived from the AIRS/AMSU-A (Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A) suite of instruments. We separate the total sampling bias into temporal and instrumental components. The temporal component is caused by the AIRS/AMSU-A orbit and swath that are not able to sample all of time and space. The instrumental component is caused by scenes that prevent successful retrievals. The temporal sampling biases are generally smaller than the instrumental sampling biases except in regions with large diurnal variations, such as the boundary layer, where the temporal sampling biases of temperature can be ± 2 K and water vapor can be 10% wet. The instrumental sampling biases are the main contributor to the total sampling biases and are mainly caused by clouds. They are up to 2 K cold and > 30% dry over midlatitude storm tracks and tropical deep convective cloudy regions and up to 20% wet over stratus regions. However, other factors such as surface emissivity and temperature can also influence the instrumental sampling bias over deserts where the biases can be up to 1 K cold and 10% wet. Some instrumental sampling biases can vary seasonally and/or diurnally. We also estimate the combined measurement uncertainties of temperature and water vapor from AIRS/AMSU-A and MERRA by comparing similarly sampled climatologies from both data sets. The measurement differences are often larger than the sampling biases and have longitudinal variations

Effect Inference from Two-Group Data with Sampling Bias

In many applications, different populations are compared using data that are sampled in a biased manner. Under sampling biases, standard methods that estimate the difference between the population means yield unreliable inferences. Here we develop an inference method that is resilient to sampling biases and is able to control the false positive errors under moderate bias levels in contrast to the standard approach. We demonstrate the method using synthetic and real biomarker data

SPARC Data Initiative: climatology uncertainty assessment

The SPARC Data Initiative aims to produce trace gas climatologies for a number of species from a number of instruments. In order to properly compare these climatologies, and interpret differences between them, it is necessary to know the uncertainty in each calculated climatological mean field. The inhomogeneous and finite temporal-spatial sampling pattern of each instrument can lead to biases and uncertainties in the mean climatologies. Sampling which is unevenly weighted in time and space leads to biases between a data set's climatology and the truth. Furthermore, the systematic sampling patterns of some instruments may mean that uncertainties in mean fields calculated through traditional methods that assume random sampling may be inappropriate. We aim to address these issues through an exercise wherein high resolution chemical fields from a coupled Chemistry Climate Model are sub-sampled based on the sampling pattern of each instrument. Climatologies based on the sub-sampled data can be compared to those calculated with the full data set, in order to assess sampling biases. Furthermore, investigating the ensemble variability of climatologies based on subsampled fields will allow us to assess the proper methodology for estimating the uncertainty in climatological mean fields

Quantifying the efficiency and biases of forest Saccharomyces sampling strategies

Saccharomyces yeasts are emerging as model organisms for ecology and evolution, and researchers need environmental Saccharomyces isolates to test ecological and evolutionary hypotheses. However, methods for isolating Saccharomyces from nature have not been standardized and isolation methods may influence the genotypes and phenotypes of studied strains. We compared the effectiveness and potential biases of an established enrichment culturing method against a newly developed direct plating method for isolating forest floor Saccharomyces spp. In a European forest, enrichment culturing was both less successful at isolating S. paradoxus per sample collected and less labor intensive per isolated S. paradoxus colony than direct isolation. The two methods sampled similar S. paradoxus diversity: the number of unique genotypes sampled (i.e., genotypic diversity) per S. paradoxus isolate and average growth rates of S. paradoxus isolates did not differ between the two methods, and growth rate variances (i.e., phenotypic diversity) only differed in one of three tested environments. However, enrichment culturing did detect rare S. cerevisiae in the forest habitat, and also found two S. paradoxus isolates with outlier phenotypes. Our results validate the historically common method of using enrichment culturing to isolate representative collections of environmental Saccharomyces. We recommend that researchers choose a Saccharomyces sampling method based on resources available for sampling and isolate screening. Researchers interested in discovering new Saccharomyces phenotypes or rare Saccharomyces species from natural environments may also have more success using enrichment culturing. We include step-by-step sampling protocols in the supplemental materials