What measures are best to address potential biases in the selection of your data sources?

December 13, 2023 by Lillian

What measures are best to address potential biases in the selection of your data sources?

What is a Data Source

Data Source is very important. In data analysis and business intelligence, a data source is a vital component that provides raw data for analysis. A data source is a location or system that stores and manages data, and it can take on many different forms. From traditional databases and spreadsheets to cloud-based platforms and APIs, countless types of data sources are available to modern businesses.

Understanding the different types of data sources and their strengths and limitations is crucial for making informed decisions and deriving actionable insights from data. In this article, we will define what is a data source, examine data source types, and provide examples of how they can be used in different contexts.

In short, data source refers to the physical or digital location where data can be stored as a data table, data object, or another storage format. It’s also where someone can access data for further use — analysis, processing, visualization, etc.

You often deal with data sources when you need to perform any transformations with your data. Let’s assume you have an eCommerce website on Shopify. And you want to analyze your sales to understand how to enhance your store performance. You decided that you would use Tableau for data processing. As it is a standalone tool, you must somehow fetch the data you need from Shopify. Thus, Shopify will act as a data source for your further data manipulations.

The difference between what is being valued and what is believed to be valued (Casal & Mateu, 2003). Unlike random error, systematic error is not compensated by increasing the sample size (Department of Statistics, Universidad Carlos III de Madrid). However, although its importance is vital in the development of an investigation, it is relevant to mention that
none is exempt from them; and that the essential thing is to know them to try to avoid, minimize or correct them (Beaglehole et al., 2008).

Bias

The risk of bias appearing is intrinsically related to clinical research, which is particularly high in frequency since it works with variables that involve individual and population dimensions, which are also difficult to control. However, they also occur in basic sciences, a context in which experimental settings present conditions in which biases adopt peculiar characteristics and are less complex to minimize, since a series or a large part of the variables can be controlled.

From a statistical perspective, when trying to measure a variable, it must be considered that the value obtained as a result of the measurement (XM) is made up of two parts; the true value (XV) and the measurement error (XE); so that XM = XV + XE. Thus, the measurement error is in turn composed of two parts; one random and the other systematic or bias, which can be measurement, selection or confusion (Dawson-Saunders et al., 1994).

This explanation allows us to understand the fundamental characteristics of any measurement: accuracy (measurements close to the true value [not biased]); and precision (repeated measurements of a phenomenon with similar values) (Manterola, 2002).
The objective of this article is to describe the concepts that allow us to understand the importance of biases, the most frequent ones in clinical research, their association with the different types of research designs and the strategies that allow them to be minimized and controlled.

POSSIBILITIES OF COMMITTING BIAS
A simple way to understand the different possibilities of committing bias during research is to think about the three axes that dominate research: what will be observed or measured, that is, the variable under study; the one who will observe or measure, that is, the observer; and with what will be observed or measured, that is, the measuring instrument (Tables II and III) (Beaglehole et al.).

1. From the variable (s) under study.

There are a series of possibilities of bias that are associated with the variable under study, either at the time of its observation, the measurement of its magnitude and its subsequent classification (Manterola).

a) Periodicity: Corresponds to the variability in the observation; That is, what is observed can follow an abnormal pattern over time, either because it is distributed uniformly over time or because it is concentrated in periods. Knowledge of this characteristic is essential in biological events that present known cycles such as the circadian rhythm,
electroencephalographic waves, etc.

b) Observation conditions: There are events that require special conditions for their occurrence to be possible, such as environmental humidity and temperature, respiratory and heart rates. These are non-controllable situations that, if not adequately considered, can generate bias; context more typical of basic sciences.

c) Nature of the measurement: Sometimes there may be difficulty in measuring the magnitude or value of a variable, qualitative or quantitative. This situation may occur because the magnitude of the values is small (hormonal determinations), or due to the nature of the phenomenon under study (quality of life).

d) Errors in the classification of certain events:
They may occur as a result of modifications in the nomenclature used; fact that must be noted by the researcher. For example, neoplasm classification codes, operational definition of obesity, etc.

2. From the observer
The ability to observe an event of interest
(EI) varies from one subject to another. What’s more, when faced with the same stimulus it is possible that two individuals can have different perceptions. Therefore, homogenizing the observation, guaranteeing adequate conditions for its occurrence and adequate observation methodology, leads to minimizing measurement errors.

This is how we know that the error is inherent to the observer, independent of the measuring instrument used. This is why in the different clinical research models, strict conditions are required to homogenize the measurements made by different observers; using clear operational definitions or verifying compliance with these requirements among the subjects incorporated into the study.

3. From the measurement instrument (s) The measurement of biomedical phenomena using more than just the senses entails the participation of measurement instruments, which in turn may have technical limitations to be able to measure exactly what they are. is desired.

The limitations of measurement instruments apply both to “hard” devices and technology, as well as to population exploration instruments such as surveys, questionnaires, scales and others. Regarding the latter, it is important to consider that the verification of compliance with the technical attributes of these is usually left aside, which, independent of any consideration, are “measuring instruments”, since they have been designed to measure the occurrence of an EI; Therefore, they must be subject to the same considerations as any measuring instrument (Manterola).

These restrictions easily apply to diagnostic tests, in which there is always the probability of overdiagnosing subjects (false positives) or underdiagnosing them (false negatives), committing errors of a different nature in both cases.
Frequently, it is necessary to resort to the design of data collection instruments; whose purpose, like the application of diagnostic tests, is to separate the population according to the presence of some IS.

Thus, if an instrument lacks adequate sensitivity, it will determine a low identification rate of subjects with IS (true positives). On the contrary, screening instruments with low specificity will decrease the probability of finding subjects without the IS (true negatives).

For example, a questionnaire intended to carry out a prevalence study of gastroesophageal reflux may consider inappropriate items to detect the problem in a certain group of subjects, altering their sensitivity. The same instrument, with an excessive number of items of little significance in relation to the problem, may lack adequate specificity to measure EI.

Probability:

Cohorts Cases and controls Cross section Ecological studies

Selection bias Low High Medium Not applicable
Recall bias Low High High Not applicable Confusion
bias Low Medium Medium High
Follow-up losses High Low Not applicable Not applicable
Time required High Medium Medium Low
Cost High Medium Medium Low
Table III. Most common types of bias in observational studies.
MANTEROLA, C. & OTZEN, T. Biases in clinical research. Int. J. Morphol., 33(3):1156-1164, 2015. Another way of classifying biases is that which is related to the frequency in which they occur and the stage of the study in which they originate; It is known that in clinical research, the most frequent biases that affect the validity of a study can be classified into three categories: selection (generated during the selection or monitoring of the study population), information (originated during measurement processes in the study population) and confusion (occur due to the impossibility of comparing the study groups).

1. Selection biases
This type of bias, particularly common in case-control studies (events that occurred in the past can influence the probability of being selected in the study); It occurs when there is a systematic error in the procedures used to select the subjects of the study (Restrepo Sarmiento & Gómez-Restrepo, 2004). Therefore, it leads to an estimate of the effect different from that obtainable for the white population.

It is due to systematic differences between the characteristics of the subjects selected for the study and those of the individuals who were selected for us. For example: hospital cases and those excluded from these either because the subject dies before arriving at the hospital due to the acute or more serious nature of their condition; or for not being sick enough to require admission to the hospital under study; or due to the costs of entry; the distance of the healthcare center from the home of the subject who is excluded from the study, etc.

They can occur in any type of study design, however, they occur most frequently in retrospective case series, case-control, cross-sectional, and survey studies. This type of bias prevents extrapolation of conclusions in studies carried out with volunteers drawn from a population without IS. An example of this situation is the so-called Berkson bias; Also called Berkson’s fallacy or paradox, or admission or diagnostic bias; which is defined as the set of selective factors that lead to systematic differences that can be generated in a case-control study with hospital cases.

It occurs in those situations in which the combination between an exposure and the IS under study increases the risk of admission to a hospital, which leads to a systematically higher exposure rate among hospital cases compared to controls (for example: negative association between cancer and pulmonary tuberculosis, in which tuberculosis acted as a protective factor for the development of cancer; which was explained by the low frequency of tuberculosis in those hospitalized for cancer, a fact
that does not mean that among these subjects the frequency of the disease is less).

Another subtype of selection bias is the so-called Neymann bias (prevalence or incidence), which occurs when the condition under study determines premature loss due to death of the subjects affected by it; For example, if in a group
of 1000 subjects with high blood pressure (risk factor for myocardial infarction) and 1000 non-hypertensive subjects, followed for 10 years; An intense association is observed between arterial hypertension and myocardial infarction. However, it may occur that an association is not obtained due to the non-incorporation in the analysis of subjects who die from myocardial infarction during follow-up.

Another subtype of selection bias is the so-called non-response bias (self-selection or volunteer effect), which occurs when the degree of motivation of a subject who voluntarily participates in research can vary significantly in relation to other subjects; either over or under reporting.

Another that should be mentioned is the membership (or belonging) bias, which occurs when among the subjects under study there are subgroups of individuals who share a particular attribute, related positively or negatively with the variable under study; For example, the profile of surgeons’ habits and lifestyles may differ significantly from that of the general population, such that incorporating a large number of this type of subjects in a study may determine findings conditioned by this factor.

Another is the bias of the selection procedure, which occurs in some clinical trials (CT), in which the random assignment process to the study groups is not respected (Manterola & Otzen, 2015). Another type of selection bias is loss to follow-up bias, which can occur especially in cohort studies, when subjects from one of the study cohorts are lost totally or partially (≥ 20%) and pre-follow-up cannot be completed. -established, thus generating a relevant alteration in the results (Lazcano-Ponce et al., 2000; Manterola et al., 2013).

2. Measurement bias

This type of bias occurs when a defect occurs when measuring exposure or evolution that generates different information between the study groups that are compared (precision). It is therefore due to errors made in obtaining the information that is required once the eligible subjects are part of the study sample (classification of subjects with and without IS; or of exposed and non-exposed).

In practice, it can present itself as the incorrect classification of subjects, variables or attributes, within a category different from the one to which they should have been assigned. The probabilities of classification can be the same in all groups under study, called “non-differential incorrect classification” (the degree of misclassification) MANTEROLA, C. & OTZEN, T. Biases in clinical research. Int. J. Mor

How to best handle data storage and archiving after the project is finished?

December 13, 2023 by Lillian

How to best handle data storage and archiving after the project is finished?

What is Data Collection?

Data collection is the procedure of collecting, measuring, and analyzing accurate insights for research using standard validated techniques.

To collect data, we must first identify what information we need and how we will collect it. We can also evaluate a hypothesis based on collected data. In most cases, data collection is the primary and most important step for research. The approach to data collection is different for different fields of study, depending on the required information.

Research Data Management ( RDM) is present in all phases of research and encompasses the collection, documentation, storage and preservation of data used or generated during a research project. Data management helps researchers: organize it, locate it, preserve it, reuse it.

Additionally, data management allows:

Save time and make efficient use of available resources : You will be able to find, understand and use data whenever you need.
Facilitate the reuse of the data you have generated or collected: Correct management and documentation of data throughout its life cycle will allow it to remain accurate, complete, authentic and reliable. These attributes will allow them to be understood and used by other people.
Comply with the requirements of funding agencies : More and more agencies require the presentation of data management plans and/or the deposit of data in repositories as requirements for research funding.
Protect and preserve data : By managing and depositing data in appropriate repositories, you can safely safeguard it over time, protecting your investment of time and resources and allowing it to serve new research and discoveries in the future.

Research data is “all that material that serves to certify the results of the research that is carried out, that has been recorded during it and that has been recognized by the scientific community” (Torres-Salinas; Robinson-García; Cabezas-Clavijo, 2012), that is, it is any information collected, used or generated in experimentation, observation, measurement, simulation, calculation, analysis, interpretation, study or any other inquiry process that supports and justifies the scientific contributions that are disseminated in research publications.

They come in any format and support, for example:

Numerical files, spreadsheets, tables, etc.
Text documents in different versions
Images, graphics, audio files, video, etc.
Software code or records, databases, etc.
Geospatial data , georeferenced information

Joint Statement on Research Data from STM, DataCite and Crossref

In 2012, DataCite and STM drafted an initial joint statement on linking and citing research data.

The signatories of this statement recommend the following as best practices in research data sharing:

When publishing their results, researchers deposit the related research data and results in a trusted data repository that assigns persistent identifiers (DOIs when available). Researchers link to research data using persistent identifiers.
When using research data created by others, researchers provide attribution by citing the data sets in the references section using persistent identifiers.
Data repositories facilitate the sharing of research results in a FAIR manner, including support for metadata quality and completeness.
Editors establish appropriate data policies for journals, outlining how data will be shared along with the published article.
The editors establish instructions for authors to include Data Citations with persistent identifiers in the references section of articles.
Publishers include Data Citations and links to data in Data Availability Statements with persistent identifiers (DOIs when available) in the article metadata recorded in Crossref.
In addition to Data Citations, Data Availability Statements (human and machine readable) are included in published articles where applicable.
Repositories and publishers connect articles and data sets through persistent identifier connections in metadata and reference lists.
Funders and research organizations provide researchers with guidance on open science practices, track compliance with open science policies where possible, and promote and incentivize researchers to openly share, cite, and link research data.
Funders, policy-making institutions, publishers, and research organizations collaborate to align FAIR research data policies and guidelines.
All stakeholders collaborate to develop tools, processes and incentives throughout the research cycle to facilitate the sharing of high-quality research data, making all steps in the process clear, easy and efficient for researchers through provision of support and guidance.
Stakeholders responsible for research evaluation factor data sharing and data citation into their reward and recognition system structures.

The first phase of an investigation requires designing and planning your project. To do this, you must:

Know the requirements and programs of the financing agencies
Search research data
Prepare a Data Management Plan .

Other prior considerations:

If your research involves working with humans, informed consent must be obtained.
If you are involved in a collaborative research project with other academic institutions, industry partners or citizen science partners, you will need to ensure that your partners agree to the data sharing.
Think about whether you are going to work with confidential personal or commercial data.
Think about what systems or tools you will use to make data accessible and what people will need access to it.

During the project…

This is the phase of the project where the researcher organizes, documents, processes and stores the data.

Is required :

Update the Data Management Plan
Organize and document data
Process the data
Store data for security and preservation

The description of data must provide a context for its interpretation and use, since the data itself lacks this information, unlike scientific publications. It is about being able to understand and reuse them .

The following information should be included:

The context: history of the project, objectives and hypotheses.
Origin of the data: if the data is generated within the project or if it is collected (in this case, indicate the source from which it was extracted).
Collection methods, instruments used.
Typology and format of data (observational, experimental, computational data, etc.)
Description standards: what metadata standard to use.
Structure of data files and relationships between files.
Data validation, verification, cleaning and procedures carried out to ensure its quality.
Changes made to the data over time since its original creation and identification of the different versions.
Information about access, conditions of use or confidentiality.
Names, labels and description of variables and values.

Template for a data dictionary
READme file template for a data set
Cornell University
REadme file template (Madroño consortium)

STRUCTURE OF A DATASET

The data must be clean and correctly structured and ordered:

A data set is structured if:

Each variable forms a column
Each observation forms a row
Each cell is a simple measurement

Some recommendations :

Structure the data in TIDY (vertical) format i.e. each value is a row, rather than horizontally. Non-TIDY (horizontal) data.
Columns are used for variables and their names can be up to 8 characters long without spaces or special signs.
Avoid text values to encode variables, better encode them with numbers .
In each cell, a single value
If you do not have a value available , provide the missing value codes.
Provide data tables , which collect all the data encodings and denominations used.
Use data dictionary or separate list of these short variable names and their full meaning

DATA SORTING

Ordered data or “TIDY DATA” are those obtained from a process called “DATA TIDYING” or data ordering. It is one of the important cleaning processes during big data processing.

Ordered data sets have a structure that makes work easier; They are easy to manipulate, model and visualize. ‘Tidy’ data sets are arranged in such a way that each variable is a column and each observation (or case) is a row.” (Wikipedia).

Practical Guide to Publishing Tabular Data to CSV Files
Content prepared by Carlos de la Fuente García, expert in open data, within the framework of the Aporta Initiative, of the Ministry of Economic Affairs and Digital Transformation
Data Quality Review Checklist
For research data during the life of the project it is recommended:
- Store data in readable formats for the long term.
- Check the files from time to time.
- Clearly organize and label stored data so that it is easily findable and accessible.
- Take into consideration the physical degradation of optical and magnetic media in case it is necessary to copy or migrate data.
- Store data on different media, even for a short-term project, for example on the hard drive and on CD.
- Create digital versions of paper documentation, in PDF/A Format for long-term preservation and storage.
- Take into account support conservation factors, such as changes in temperature, relative humidity, light, etc.
Storage media are complementary to each other and some of the most common options are:
- Personal or project data storage (using USB drives, computer hard drives or network drives within the institution), recommended only for use in the course of research.
- Institutional repository (Dehesa or that of your university).
- National data archiving services.
- Cloud data warehouse.
- Repositories (RIO, Zenodo, disciplinary repositories).
The repository selected to store the data must guarantee its long-term preservation, and ease of recovery and access.
When choosing a repository, some aspects must be taken into account:
According to the OpenAIRE Research Data Management Briefing Paper, data should be deposited in a data repository according to the following order of preference :
1. Multidisciplinary data repository. Zenodo, Dryad, Dataverse, Figshare, Mendeley Data,
2. Other data repositories
3. Together with the scientific publications ODYSSEY
REPOSITORY FINDERS
Repository search engines could also help you:
- Re3data: global registry of research data repositories from different academic disciplines. Managed and maintained by DataCite .
- Fairsharing : search engine for standards, data repositories and open access policies in all disciplines.
- Repository Finder – Allows researchers to search for the most suitable repository to deposit data.
Consolidated thematic data repository for that discipline. Re3data or Data Repositories, for example, in Social Sciences, World Values Survey or Wellcome Library in History of Medicine Institutional data repository. Digital CSIC or Harvard Dataverse, Dehesa (repository of the University of Extremadura).

There may be exceptions to open dissemination, based on reasons of confidentiality, privacy, security, industrial exploitation, etc. (H2020, Work Programme, Annexes, L Conditions related to open access to research data).

There are some reasons why certain types of data cannot and/or should not be shared , either in whole or in part, for example:

When the data constitutes or contains sensitive information . There may be national and even institutional regulations on data protection that will need to be taken into account. In these cases, precautions must be taken to anonymize the data and, in this way, make its access and reuse possible without any errors in the ethical use of the information.
When the data is not the property of those who collected it or when it is shared by more than one party, be they people or institutions . In these cases, you must have the necessary permissions from the owners to share and/or reuse the data.
When the data has a financial value associated with its intellectual property , which makes it unwise to share the data early. Before sharing them, you must verify whether these types of limits exist and, according to each case, determine the time that must pass before these restrictions cease to apply.

What best considerations have you made for accessibility in your data collection?

December 12, 2023 by Lillian

What best considerations have you made for accessibility in your data collection?

What are Data Collection Methods?

Data collection methods are techniques and procedures used to gather information for research purposes. These methods can range from simple self-reported surveys to more complex experiments and can involve either quantitative or qualitative approaches to data gathering.

Some common data collection methods include surveys, interviews, observations, focus groups, experiments, and secondary data analysis. The data collected through these methods can then be analyzed and used to support or refute research hypotheses and draw conclusions about the study’s subject matter.

Data collection methods play a crucial role in the research process as they determine the quality and accuracy of the data collected. Here are some mejor importance of data collection methods.

Determines the quality and accuracy of collected data.
Ensures that the data is relevant, valid, and reliable.
Helps reduce bias and increase the representativeness of the sample.
Essential for making informed decisions and accurate conclusions.
Facilitates achievement of research objectives by providing accurate data.
Supports the validity and reliability of research findings.

To familiarize ourselves with the concept of universal accessibility, it is important to mention its historical process. In 1948, the United Nations (UN) promulgated the Universal Declaration of Human Rights, which drafted the Principles of Equal Rights and Opportunities for all Citizens, but it was not until 1963 at the First International Congress for the Suppression of Architectural Barriers carried out in Switzerland, whose main objective proposes to try new measures for the design of buildings by eliminating barriers that obstruct access for people with disabilities.

In 1982, Spain approved the Law on Social Integration of the Disabled (lismi), in that same year, the UN promoted the development of the World Program of Action towards the Disabled; In 2003, the Law on Equality, Non-Discrimination and Universal Accessibility (LIONDAO) incorporated the concept of universal accessibility in which it promoted equal opportunities benefiting all people; In 2006, the UN again held a convention on the rights of people with disabilities and in 2013, the General Law on the Rights of People with Disabilities and their Social Inclusion established that all services, environments, goods and products be accessible.

The study area of the Technological Development Corporation (CDT) (2018) defines universal accessibility as the condition that spaces usable by all people must meet in safe and comfortable conditions with the aim of moving autonomously and naturally. It is a space that must have equal opportunities and social inclusion for people with different abilities, free of obstacles and barriers (urban, architectural and mobility) that prevent correct movement.

Accessibility seeks the inclusion of all citizens in public and private spaces, it must be “integral and guarantee not only mere accessibility, but also circulation, use, orientation, security and functionality” (Olivera, 2006: 332). Pedestrian mobility is one of the main requirements in the physical accessibility of cities (Ipiña García, 2019: 159).

Universal accessibility is directly related to the quality of life of the inhabitants of a city, it must be understood that people have the right to enjoy all the services that the city can provide, being the responsibility of the public and private sectors to modify the environment to that can be used under conditions of equality, taking into account social, economic and geographical needs.

One of the main problems in terms of universal accessibility is that cities were not designed for the use of all people, but it is a fact that currently regulations, laws, plans, programs, etc. have been implemented that They have gradually transformed some sectors of our cities, improving the quality of life of users. To achieve these changes, it is necessary to have knowledge, empathy and awareness in order to generate simple and intuitive spaces that have equal opportunities.

UNIVERSAL ACCESSIBILITY AS AN IMPORTANT PART OF PUBLIC SPACE

There is an intimate relationship between universal accessibility and public space, due to the permanent dynamics of the inhabitants in the city; so the latter would not exist without public space and it would perish without citizens. “As the city is a historical fact, the public space is also historical; It is part of the cultural manifestations of a civilization, which is always limited in time and space” (Gamboa Samper, 2003: 13).

Since the 19th century, Camillo Sitte, one of the precursors of the German school, considered that the city should be designed for pedestrians; since then, people have thought about creating functional and flexible spaces that can be used by everyone. “Ultimately, the success of a city must be measured in its ability to guarantee access to all citizens to the benefits that have made cities one of the most wonderful human inventions” (D. Davila, 2012: 60). Consequently, public space is a collective site for public use that must guarantee well-being for all people, this includes responding to the needs of citizens, thus promoting universal accessibility.

Squares, parks and gardens are part of the public space, but they are also made up of streets that allow people to move to reach their destination. At a smaller mobility scale, the pedestrian can be defined as any person who travels on foot through public or private space (Municipal Government of Cusco, sf). Pedestrian movement or mobility must meet certain requirements so that it is carried out under quality conditions; accessibility, safety, comfort and attractiveness (Alfonzo, 2005; Pozueta et al., 2009), when satisfied, the pedestrian environment will have the necessary quality for the pedestrian to move, which will have a decisive impact on service levels. pedestrian aspect of the urban environment (Olszewski and Wibowo, 2005) (Larios Gómez, 2017: 6).

In terms of universal accessibility, it is important to adapt at least one accessible pedestrian route in spaces with greater pedestrian flow. In the analysis of an urban space, priority must be given to the implementation of accessible routes that link main avenues, secondary streets, stops and access to public transport and vehicle parking (Boudeguer Simonetti et al., 2010: 39), in this way the spaces They may be used by all people under equal conditions.

The public space is characterized by being easily accessible, allowing interaction between its inhabitants, creating social ties that allow generating a link with the space, this causes citizens to experience their environment, identifying and appropriating the elements that make up the public space. One of the problems we currently have is that society has gradually stopped going to these spaces due to insecurity, inaccessibility, pollution, lack of maintenance on the streets and gardens; generating its abandonment and deterioration.

When the public space meets the characteristics of security, universal accessibility, mobility, identity, inclusion and permanence, it is said to be a quality space that allows the city to be experienced, enjoying the pedestrian routes, observing the architectural elements that are part of it, such as the facades of the buildings, the planters, benches and lamps of the urban furniture.

The parks and gardens that are fundamental in the cities, not only for providing them with green areas, but for preserving a part of their history, in this sense, Segovia and Jordán (2005) affirm that the quality of public space can be evaluated above all by the intensity and quality of the social relationships it facilitates, by its capacity to welcome and mix different groups and behaviors, and by its opportunity to stimulate symbolic identification. cultural expression and integration.

For public space to play the role of being a system that allows interaction between people and the enjoyment of recreational places, it is essential that citizens can enter them without physical barriers, being accessible to all inhabitants, ” An environment is needed with a level of quality that allows environmental sustainability and, of course, services that articulate the appropriate functioning of urban public spaces with the population” (Rueda et al., 2012) (Alvarado Azpeitia et al., 2017 : 131), which consists of generating a public road on which cars, bicycles and public transport can also travel, always giving importance and priority to the pedestrian.

A space accessible to everyone

As noted in previous paragraphs, in the 19th century people were already thinking about creating cities designed for pedestrians, but it was not until 2003 that the term “Universal Accessibility” was implemented, which aims to include all people regardless of your age and physical, visual, mental, hearing and multiple disability condition; creating or adapting spaces that allow their use and movement autonomously and implementing Universal Design or Design for All, benefiting the greatest number of people possible.

Architect Wilson Castellanos Parra mentions that believing that universal accessibility responds exclusively to the needs of people with reduced mobility is a mistake; It is more than a ramp, it is understood as “the condition that environments, processes, goods, products and services, as well as objects or instruments, tools and devices, must meet; to be understandable and applicable to all people” (Castellanos Parra, 2016); In the virtual conference “Universal Accessibility in Colombian Architecture Curricula” he describes some criteria to identify accessibility conditions in environments, these are:

1. Wandering (refers to the spaces of approach, spaces traveled),

2. Apprehension (achieving certain requirements when carrying out any activity such as: signage elements), location (auxiliary services) and communication (interactive communication such as: graphics, information panels, etc.).

Universal accessibility is linked to various topics such as: the chain of accessibility, mobility, design of complete streets, among others; that seek the movement of people in conditions of equality, quality and safety.

The Secretariat of Agrarian, Territorial and Urban Development (sedatu) in collaboration with the Inter-American Development Bank (bid) produced The Street Manual: Road Design for Mexican cities where, in an illustrated manner, a pyramid that classifies the hierarchy is shown. of mobility.

Under this classification, all people can make their trips in inclusive, safe, sustainable and resilient conditions; Priority should be given to pedestrians and drivers of non-motorized vehicles to promote a more efficient and inclusive use of road space (sedatu; Inter-American Development Bank, 2019: 62).

How is consent and data collection from minors best addressed?

December 12, 2023 by Lillian

How is consent and data collection from minors best addressed?

Data collection

Data is a collection of facts, figures, objects, symbols, and events gathered from different sources. Organizations collect data with various data collection methods to make better decisions. Without data, it would be difficult for organizations to make appropriate decisions, so data is collected from different audiences at various points in time.

For instance, an organization must collect data on product demand, customer preferences, and competitors before launching a new product. If data is not collected beforehand, the organization’s newly launched product may fail for many reasons, such as less demand and inability to meet customer needs.

Although data is a valuable asset for every organization, it does not serve any purpose until analyzed or processed to get the desired results.

The right to the protection of personal data: origin, nature and scope of protection.

Origins and legal autonomy

The approach to the study of any right with constitutional status requires, without a doubt, a reference to its origins, for which, on this occasion, the generational classification of human rights developed at a doctrinal level will be very useful.

In general, historically the recognition of four generations of fundamental rights, individual or first generation rights, has prevailed; public freedoms or second generation rights; social or third generation rights; and rights linked to the emergence of new technologies and scientific development, classified in the fourth generation, these have corresponded to ideological and social moments with their own characteristics and differentiating features.

In particular, the fourth generation is presented as a response to the phenomenon known as “liberties pollution” , a term coined by some authors to refer to the degradation of classic fundamental rights in the face of recent uses of new technology.

Indeed, the technological development that has occurred since the second half of the 20th century has shown the limitations and insufficiency of the right to privacy – first generation right – as the only mechanism to respond to the specific dangers contained in the automated processing of personal information. , which is why starting in the seventies, the dogmatic and jurisprudential construction of a new fundamental right began to take shape: the right to the protection of personal data.

From a theoretical point of view, the reformulation of the classic notion of the right to privacy – no longer as a right of exclusion, as it had initially been conceived, but rather as a power to control information relating to one itself, represented a clear breaking point in the conceptualization that had been maintained on it until that moment.

On the other hand, in the jurisprudential context, the legal conformation of this right – which was classified as the right to informational self-determination – originates in a ruling issued by the German Federal Constitutional Court in 1983, declaring the unconstitutionality of a law that regulated the demographic census process at that time. In contrast, Chilean jurisprudence was particularly late in the configuration of the right to the protection of personal data, since its first approximation occurred in 1995, when the Constitutional Court linked it, precisely, to the protection of privacy.

It is true that the right to privacy constitutes an important, if not essential, antecedent in terms of the formation of the right that is the object of our study; however, this does not mean that both should be confused, an issue that in its moment sparked countless debates. Some authors, for example, stated that the right to the protection of personal data constituted a form of manifestation of the particular characteristics that the right to privacy acquires in the computer age, denying the autonomy that it is possible to attribute to it today.

From our perspective, and as the Spanish Constitutional Court was responsible for announcing at the beginning of this century, two fundamental rights closely linked to each other, as well as clearly differentiated, coexist in our legal system: the right to privacy and the right to the protection of personal data. With the first, the confidentiality of the information related to an individual is protected, while with the second the proper use of the information related to a subject is guaranteed, once it has been revealed to a third party, since the confessed data It is therefore not public and, consequently, cannot circulate freely.

Thus, the legal power to have and control at all times the use and traffic of this information belongs entirely to its owner. In other words, the fundamental right to data protection does not constitute a right to secrecy or confidentiality, but rather a power to govern its publicity. In this way, while the right to privacy would be a power of exclusion, the right to protection of personal data is consecrated, instead, as one of disposition.

In accordance with what was stated above , the latter seems to be the position finally adopted by the Chilean Constitution. In this regard, it is worth remembering that the Organization for Economic Cooperation and Development (OECD) pointed out, in 2015, that our country was in compliance with its personal data protection regulations, pointing out that among its member states, only Chile and Turkey had not yet perfected their legislation on the matter.

On this level, the reform of article 19 number 4 of the constitutional text was framed, which since June 16, 2018 has assured all people “ respect and protection of private life and the honor of the person and their family.” , and also, the protection of your personal data “, adding that ” the treatment and protection of these data will be carried out in the manner and conditions determined by the law .”

As can be seen, the new wording of the Chilean fundamental norm now enshrines the right to the protection of personal data in an autonomous and differentiated manner, a trend adopted for several years by the fundamental charters of other countries in Europe and Latin America, with Chile joining the this majority trend.

Natural capacity as an essential element for the exercise of personality rights

The tendency followed by the Chilean legal system to give relevance to what is known as natural capacity – or maturity – as an essential substrate on which to base the exercise capacity of children and adolescents, is especially marked in the field of the rights of personality – or in other words, in the field of extra-patrimonial legal acts, and it is precisely in this context that the first voices in favor of maintaining that, although the dichotomy “capacity for enjoyment/capacity for exercise” could still have some relevance in the patrimonial sphere, it was, on the other hand, unsustainable in the extra-patrimonial personality sphere.

It seems that denying the capacity to exercise personality rights in the space when the subject, despite his or her chronological age, meets the intellectual and volitional conditions sufficient to exercise them on his or her own, becomes a plausible violation of dignity and freedom. free development of the personality of the individual, recognized in article 1 of our Constitution as superior values of the regulatory system ( People are born free and equal in dignity and rights ).

Certainly, it has been discussed whether the distinction between the capacity to enjoy and the capacity to exercise is applicable in the field of personality rights, since the enjoyment or exercise of these rights is personal. Hence, it is difficult to speak of authentic legal representation in this environment, with this representation being very nuanced or being configured rather as assistance or action by parents or guardians/curators in compliance with their duty to care for the child or adolescent especially justified when it comes to avoiding harm.

Given the above and in accordance with the principle of favor filii , the implementation of personality rights by their legitimate holders can only be limited when their will to activate them is contrary to preponderant interests in attention to the full development of their personality, in the same way that the will of their representatives can be limited when their intervention is contrary to the interests of the child or adolescent.

Well, it is precisely in that context, in which the idea of adopting the criterion of sufficient maturity, self-government or natural capacity emerges strongly, as a guideline to follow to delimit the autonomous exercise of personality rights, avoiding With this, the person who has not yet reached the age of majority is simply the holder of the right, but cannot, however, exercise it. In this way, the general rule becomes that the girl, boy or adolescent who is sufficiently mature can freely dispose of her or his rights.

With the above meaning, it should be noted that in this new scenario the question is limited to specifying what is meant by showing sufficient maturity, since we are faced with an indeterminate legal concept around which there is no unified legal definition. Each boy and girl is different, and therefore it is very difficult to establish when or not they have the necessary exercise capacity, due to their intellectual development, to be the master of their own person.

What are the potential worst limitations of your data collection approach?

December 11, 2023 by Lillian

Inteligencia artificial y ciencia scaled 1

What are the potential worst limitations of your data collection approach?

What is Data Collection?

Data collection is the procedure of collecting, measuring, and analyzing accurate insights for research using standard validated techniques.

Put simply, data collection is the process of gathering information for a specific purpose. It can be used to answer research questions, make informed business decisions, or improve products and services.

Data Collection Methods

There are many ways to collect information when doing research. The data collection methods that the researcher chooses will depend on the research question posed. Some data collection methods include surveys, interviews, tests, physiological evaluations, observations, reviews of existing records, and biological samples.

Main types of limitations

Some methodological limitations

Sample size : Is the number of units of analysis you use in your study determined by the type of research problem you are investigating? Keep in mind that if your sample size is too small, it will be difficult to find meaningful relationships and generalizations from the data, since statistical tests typically require a larger sample size to ensure a representative distribution of the population. and be considered representative of the groups of people, objects, processes, etc., studied. Although, of course, sample size is less relevant in qualitative research.

Lack of available and/or reliable data: Lack of data or reliable data is likely an aspect that may limit the scope of your analysis, the size of your sample, or may be a significant obstacle to finding a trend, generalization, or relationship. significant. You should not only describe these limitations, but also offer reasons why you believe the data is missing or unreliable, which will be very useful as an opportunity to describe future research needs.

The lack of previous research studies on the topic : Referencing and criticizing previous research studies constitutes the basis of the bibliographic review and helps lay the foundation for understanding the research problem being investigated. Depending on the scope of your research topic, there may be little prior research on your topic. Of course, before assuming that this is true, the main international databases should be widely consulted. It is important to highlight that discovering a limitation of this type can serve as an opportunity to identify new gaps in the literature and consequently new research.

Measure used to collect the data: Sometimes, after completing the interpretation of the results, you discover that the way you collected data inhibited your ability to conduct a thorough analysis of the results. For example, not including a specific question in a survey that, in retrospect, could have helped address a particular issue that arose later in the study.

Self-reported data : Self-reported data is limited by the fact that it can rarely be independently verified. In other words, I am referring to the case where the researcher has to investigate what people think about a topic, whether in interviews, focus groups, or in questionnaires, at face value. These self-reported data may contain several potential sources of bias that you should be aware of and note as limitations. These biases can become evident if they are inconsistent with data from other sources. These are: 1) selective memory , that is, remembering or not remembering experiences or events that occurred at some point in the past; 2) “telescope” effect , where self-informants remember events that occurred once as if they occurred at another time; 3) attribution , which refers to the act of attributing positive events and outcomes to one’s own person, but attributing negative events and outcomes to external forces; and 4) exaggeration, the act of representing results or embellishing events as more significant than they really were (Price and Murnan, 2004).

Possible limitations of the researcher

Access: If the study depends on having access to people, organizations or documents and, for any reason, access is denied or limited in some way, the reasons for this situation must be described.

Longitudinal effects : The time available to investigate a problem and measure change or stability over time is in most cases very limited, for example, due to the expiration date of project assignments, these limitations are advisable that are expressed in the research report or in a scientific article.

Cultural limitations and other types of bias: Bias is when a person, place or thing is seen or shown in an inaccurate way. The bias is generally negative, although one can have a positive bias as well, especially if that bias reflects your reliance on research that supports only your hypothesis. When revising your article, critically review the way you have stated a problem, selected the data to study, what you may have omitted, the way you have arranged procedures, events, people or places.

No one expects science to be perfect, especially not the first time, and even your colleagues can be very critical, but no one’s work is beyond limitations. Our knowledge base is based on discovering each piece of the puzzle, one at a time, and the limitations show us where we need to make greater efforts next time. From a peer review perspective, I do not believe that limitations are inherently bad, on the contrary, omitting them would leave hidden flaws that could be repeated, it is necessary to see them as an opportunity, even the limitations of your study can be the inspiration from another researcher.

References

Price, J.H. y Murnan, J. (2004). Research Limitations and the Necessity of Reporting Them. American Journal of Health Education, 35, 66-67.

What are the limitations of the research?

How can they affect the results of a scientific study of social reality?

Research limitations are aspects or conditions that are identified as possible obstacles to achieving the objectives of a research. Furthermore, such limitations restrict or condition the validity, applicability and generalization of the results of a study or investigation. They are aspects that the researcher recognizes and points out as factors that could have influenced the results or that limit the interpretation and extrapolation of the findings (Booth et al., 2008; Yin, 2017; Black, 1999; and, Leedy and Ormrod, 2016).

It is important to highlight limitations in a research report so that readers understand the restrictions inherent to the study and can interpret the results appropriately (American Psychological Association, 2020).

Common limitations

Let’s look at some of the limitations that are frequently mentioned in research reports. These are not the only ones, others can be identified; Here are some of the typical limitations associated with quantitative and qualitative approaches in research:

Sample size

If the sample used in the research is small, the results may not be representative of the general population. This may limit the generalizability of the findings.

Selection bias

If the sample is not selected randomly or if it has specific characteristics, it may introduce bias into the results.

Response bias

In studies involving surveys or questionnaires, missing or biased responses from participants can affect the validity of the results.

Assumptions of normality

In some statistical methods, data are assumed to follow a normal distribution. If this assumption is not met, there may be problems in data analysis.

Resource limitations

For research that follows the quantitative approach, limited availability of funding or access to data may restrict the depth and breadth of the research. Qualitative data collection and analysis is often a time- and resource-intensive process, which can limit the amount of data that can be collected.

Measurement tools

If the instruments used to collect data are not reliable or valid, the results may not accurately reflect the variables being studied.

Information bias

If participants do not provide accurate or complete information, whether intentionally or unintentionally, this can bias the results.

Temporal context

The results of a study can be influenced by when it was conducted, as conditions can change over time.

Temporary effects

In longitudinal research, it can be difficult to control for temporal effects, which can lead to misinterpretations of causal relationships.

Limitations on generalization

Some studies may be limited in terms of the applicability of the results to specific populations or particular situations.

Validity and reliability

Validity and reliability in qualitative research can be difficult to establish due to the subjective nature of the reality from which the data is obtained for analysis and interpretation.

Limited generalization

In qualitative research, results focus on specific contexts and cannot always be widely generalized.

Researcher bias

Researcher bias can influence the collection and analysis of qualitative data if the researcher is not aware of his or her own perspectives and biases.

Subjective interpretation

Despite criteria of scientific rigor and transparency, the interpretation of qualitative data is subjective and depends on the perspective of the researcher, which can generate debates about objectivity.

Uncontrolled external factors

Factors outside the researcher’s control that may influence the results, such as unexpected events or changes in the environment.

Ethical limitations

In research involving human subjects, there may be ethical restrictions on the collection of certain types of data or the manipulation of variables (National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, 1979).

Pointing out limitations can be useful to guide future research and improvements in methodological design. It is important that researchers are aware of these limitations and address them appropriately in their research reports to ensure the transparency and validity of their studies.

How to better document and inform yourself of any changes made during the data collection process?

December 11, 2023 by Lillian

How to better document and inform yourself of any changes made during the data collection process?

What is Data Collection?

Data collection is the procedure of collecting, measuring, and analyzing accurate insights for research using standard validated techniques.

What is a change control process and how is it implemented?

A change control process allows project managers to submit requests to stakeholders for review, which are then approved or rejected. It is an important process to help manage large projects with many moving parts.

When it comes to managing multiple projects , things can get difficult. From coordinating work schedules to tracking goals and results, the last thing you want to deal with is a major project change. However, if you implement a change control process, you can easily submit project change requests.

The change control process is essential for large-scale initiatives where teammates from multiple departments work together. Below we will analyze the process in more detail and show you specific examples that will help you implement your own change control procedure.

What does the change control process mean?

Change control is a process used to manage change requests for projects and other important initiatives. It is part of a change management plan that defines the roles to manage change within a team or company. While a change process has many parts, the easiest way to visualize it is by creating a change log to track project change requests.

In most cases, anyone involved can request changes. A request can be as small as a modification to the project schedule or as large as a new deliverable. However, it is important to note that not all requests will be approved, as it is up to key participants to approve or reject change requests.

Since a change control process includes many moving parts and differs from company to company, it is advisable to incorporate tools that help process cycles flow smoothly. Tools like workflow management software can help you manage work and communications in one place.

Change control vs. change management

Can’t you understand the difference between change control and change management? Don’t worry! There are many differences between change control and a change management plan . Change control is just one of the many pieces of a change management strategy.

Change control: A change control process is important for any company as it can facilitate the flow of information when changes need to be made to a project. A successful process must define success metrics, organize workflows, facilitate team communication, and set them up for success.
Change Management: A change management plan involves coordinating budget, schedule, communications, and resources. While a change control process consists of a formal document that describes a change request and the impact of that change, change management refers to the overall plan.

As you can see, a change control process is only a small part of a larger change management plan. So, although they are related, both terms are different.

What are the benefits of a change control process?

Implementing a change control process, with the support of organizational software , can help you efficiently organize and manage your team’s work, as well as project deliverables and deadlines. It is also very important when you consider the possible consequences of not being able to manage changes effectively.

A change management process can help you execute a resource management plan or other work management objectives. Here are some additional benefits of implementing a change control process.

Higher productivity

A change control process will eliminate confusion around project deliverables and allow you to focus on execution rather than gathering information. As a result, you will achieve greater productivity and efficiency, especially with the help of productivity software .

Without a properly implemented process, productivity can suffer due to time spent on the details of the job. Due to limited availability for more important work, employees fail to meet a quarter (26%) of deadlines each week.

Effective communication

Proper documentation of changes can help reduce communication problems. When goals and objectives are clearly defined, team communication can flourish. However, it is important to note that a change control process will not solve all communication problems. It can also be helpful to adopt work management software to keep communication about different projects in one place.

A change control process can also be shared with the executives involved to easily provide context around change requests.

Greater collaboration and teamwork

Effective communication, in addition to being a benefit in itself, also helps improve collaboration. Clear communication about project changes enhances collaboration and teamwork.

For example, when changes are clearly communicated from the beginning, stakeholders have more time to focus on creativity and teamwork. Without effective communication, those involved are forced to spend their time gathering information instead of working with team members and fostering creativity.

To further improve collaboration, try combining the change control process with task management software to set your team up for success.

The five stages of a change control process

Like the five phases of project management , there are five key steps to creating a change control process. Although there may be some small differences, there are key elements that are common to all processes. From inception to implementation, each of these essential steps helps change requests move through the different stages quickly and efficiently and avoid unnecessary changes.

Some prefer to have the procedure in a change control process flow as it may be easier to visualize. Regardless of how it is displayed, the result will always be the final decision to approve or reject a change request.

Let’s take a closer look at what goes into each stage of an effective change control process.

1. Start of the change request

The initial phase of the process begins with a change request. There are numerous reasons why you might request a change, such as submitting a request to adjust the delivery date of a creative asset that is taking longer than expected. And while a request will most likely come from a stakeholder or project leader, anyone can submit a change request.

If a team member wants to make a request, they must submit it through a change request form. As a project manager, you should maintain a change log and store it in a place that is easy to find and accessible to everyone.

Once the application form has been completed, you will need to update the change log with a name, a brief description, and any other information you consider important, such as the date, name of the applicant, etc. The change log stores all changes made to the project, which can be useful if you manage multiple projects that span several months.

Below we provide some examples of the different fields you can include in a change request form.

Project’s name
The date
Request Description
Applicant
Change Manager
Priority
Impact of change
Term
Comments

The fields you include will depend on the level of detail you want your change log to have and the type of change you receive.

2. Evaluation of the change request

Once the initial form has been submitted and approved, the application will be evaluated. At this stage, the requested changes are analyzed.

The evaluation phase is not necessarily where a decision is made. At this stage the application is reviewed to obtain all the necessary information. The information will likely be reviewed by a project or department leader, who will evaluate some key details such as the resources needed, the impact of the request, and to whom the request should be referred.

If the change request passes the initial evaluation stage, the analysis phase begins where a decision will be made.

3. Analysis of the change request

The change impact analysis phase culminates with a final decision made by the relevant project leader on whether the request will be approved or rejected. While you can also participate in the decision-making process, it is always advisable to obtain formal approval from a project leader. In some cases, there may even be a change control committee to oversee the approval of requests.

An approved change request must be signed and communicated to the team to then continue with the rest of the phases of the process. The change must be documented in the change log and in all channels where project communication is maintained to ensure that all project participants clearly understand the necessary changes.

If the change request is rejected, it must also be documented in the change log. And while it’s not necessary to communicate a denied request to the team, it might be helpful to notify them to avoid confusion.

4. Implementation of the requested change

If the requested change is approved, the process will move to the implementation phase. This is where you and others involved in the project will work to apply the changes to the project.

Implementation of changes may vary depending on the stage of the project, but generally will involve updating the project schedule and deliverables and informing the entire team. Then you can start with the concrete work. It is important to evaluate the scope of the project to ensure that adjustments to the schedule do not have a significant impact on the proposed objectives.

It is best to share the request information in a shared workspace and in the change log to avoid a decrease in productivity when trying to find new information. You can even share a business case to cover all the aspects you consider necessary.

5. Closing the change request

Once the request has been documented, shared, and implemented, the request is ready to be closed. While some teams don’t have a formal closure plan, it’s helpful to have one to store information in a place that all team members can reference in the future.

During the closure phase, all documentation, change logs, and communication should be stored in a shared space that can be accessed in the future. It’s also a good idea to store the original change form and the revised project plan you created during the process.

Once the documents are stored in the appropriate place, you can finish the related tasks and work towards the successful completion of your project. Some project leaders also organize a post-mortem meeting before officially ending the project.

What steps will you take to ensure the best generalization of your findings?

December 11, 2023 by Lillian

What steps will you take to ensure the best generalization
of your findings?

The Generalization

Generalization is applied by researchers in academia. It can be defined as the extension of the results and conclusions of a research carried out on a population sample to the general population. Although the reliability of this extension is not absolute, it is statistically probable.

Since good generalization requires data on large populations, quantitative research—experimental, for example—provides the best basis for producing a broad generalization. The larger the sample population, the more the results can be generalized. For example, a comprehensive study of the role that computers play in the writing process might reveal that students who compose most of their text on a computer are statistically likely to move more chunks of text than students who do not compose on a computer.

Transferability

Transferability is applied by readers of the research. Although generalizability usually applies only to certain types of quantitative methods, transferability can apply to varying degrees to most types of research. Unlike generalizability, transferability does not imply broad statements, but rather invites readers of the research to make connections between the elements of a study and their own experience. For example, high school teachers could selectively apply the results of a study showing that heuristic writing exercises help students at the college level to their own classrooms.

Interrelationships between Generalization and Transferability

Generalizability and transferability are important elements of any research methodology, but they are not mutually exclusive. Generalization, to varying degrees, relies on the transferability of research results. It is important for researchers to understand the implications of these two aspects of research before designing a study. Researchers seeking to make a generalizable claim must carefully examine the variables involved in the study.

Among them are the population sample used and the mechanisms for formulating a causal model. Furthermore, if researchers want the results of their study to be transferable to another context, they must maintain a detailed account of the environment surrounding their research, and include a rich description of that environment in their final report. With the knowledge that the sample population was large and varied, as well as detailed information about the study itself, readers of the research can generalize and transfer the results to other situations with greater confidence.

Generalization

Generalization is not only common to research, but also to everyday life. In this section, we establish a working definition of generalization as it applies within and outside of academic research. We also define and consider three different types of generalization and some of their likely applications. Finally, we discuss some of the potential shortcomings and limitations of generalizability that researchers should consider when constructing a study that they hope will produce potentially generalizable results.

Definition

In many ways, according to Shavelson et al (1991), generalization is nothing more than making predictions based on recurring experience. If something happens frequently, we hope that it will continue to happen in the future. Researchers use the same type of reasoning when generalizing the results of their studies.

Once researchers have collected enough data to support a hypothesis, a premise can be formulated about the behavior of that data. This is what makes it generalizable to similar circumstances. However, due to its foundation in probability, this generalization cannot be considered conclusive or exhaustive.

Although generalization can occur in informal and non-academic contexts, in academic studies it usually only applies to certain research methods. Quantitative methods allow some generalization. Experimental research, for example, often produces generalizable results. However, this experimentation must be rigorous to obtain generalizable results.

Generalization Example 1

An example of generalization in everyday life is driving. Driving a car in traffic requires drivers to make assumptions about the likely outcome of certain actions. When approaching an intersection where a driver is preparing to turn left, the driver passing through the intersection assumes that the driver turning left will yield to him before turning. The driver passing through the intersection applies this assumption with caution, recognizing the possibility that the other driver may turn prematurely.

American drivers also generalize that everyone drives on the right side of the road. However, if we try to generalize this assumption to other settings, such as England, we will be making a potentially disastrous mistake. It is therefore evident that generalization is necessary to form coherent interpretations in many different situations. However, we do not expect our generalizations to work the same in all circumstances. With enough evidence we can make predictions about human behavior. At the same time we must recognize that our assumptions are based on statistical probability.

Generalization Example 2

Consider this example of generalizable research in the field of English studies. A study of students’ evaluations of composition instructors could reveal that there is a strong correlation between the grade students expect to earn in a course and whether they give their instructor high marks.

The study may find that 95% of students who expect to receive a “C” or lower in their class give their instructor a grade of “average” or lower. Therefore, there would be a high probability that prospective students who expect a “C” or less will not give their instructor high grades. However, the results would not necessarily be conclusive. Some students might buck the trend.

Type 2 Generalization

A second form of generalization focuses on measurements rather than treatments. For a result to be considered generalizable outside the test group, it must produce the same results with different forms of measurement. In terms of the heuristic example above, the results will be more generalizable if the same results are obtained when evaluated “with questions that have slightly different wording, or when we use a six-point scale instead of a nine-point scale” (Runkel and McGrath, 1972, p.46).

A third type of generalization concerns the subjects of the test situation. Although the results of an experiment may be internally valid, that is, applicable to the group being tested, in many situations the results cannot be generalized beyond that particular group. Researchers hoping to generalize their results to a broader population should ensure that their test group is relatively large and chosen at random. However, researchers must take into account the fact that test populations of more than 10,000 subjects do not significantly increase generalizability (Firestone,1993).

Potential limitations

No matter how carefully these three forms of generalizability are applied, there is no absolute guarantee that the results obtained in a study will occur in all situations outside the study. To determine causal relationships in a test environment, precision is of utmost importance. However, if researchers want to generalize their findings, range and variance must take precedence over precision.

Therefore, it is difficult to test accuracy and generalizability simultaneously, as focusing on one reduces the reliability of the other. One solution to this problem is to make a greater number of observations. This has a double effect: first, it increases the sample population, which increases generalizability. Second, precision can be reasonably maintained because random errors across observations will be averaged (Runkel and McGrath, 1972).

How do you ensure the best validity of your data?

December 11, 2023 by Lillian

inteligencia artificial para el diagno stico temprano de la esquizofrenia foto freepik 1

How do you ensure the best validity of your data?

Data

What is Data Collection?

Data collection is the procedure of collecting, measuring, and analyzing accurate insights for research using standard validated techniques.

Validity is an evaluation criterion used to determine how important the empirical evidence and theoretical foundations that support an instrument, examination, or action taken are. Also, it is understood as the degree to which an instrument measures what it purports to measure or that it meets the objective for which it was constructed. This criterion is essential to consider a test valid. Validity along with reliability determine the quality of an instrument.

Currently, this has become a relevant element within the measurement due to the increase in new instruments used at crucial moments, for example when selecting new personnel or when determining the approval or disapproval of an academic degree. Likewise, there are who point out the need to validate the content of existing instruments.

The validation process is dynamic and continuous and becomes more relevant as it is further explored. The American Psychological Association (APA), in 1954, identified 4 types of validity: content, predictive, concurrent and construct. However, other authors classify it into appearance, content, criterion and construct validity.

Content validity is defined as the logical judgment about the correspondence that exists between the trait or characteristic of the student’s learning and what is included in the test or exam. It aims to determine whether the proposed items or questions reflect the content domain (knowledge, skills or abilities) that you wish to measure.

To do this, evidence must be gathered about the quality and technical relevance of the test ; It is essential that it is representative of the content through a valid source, such as: literature, relevant population or expert opinion. The above ensures that the test includes only what it must contain in its entirety, that is, the relevance of the instrument.

This type of validity can consider internal and external criteria. Among the internal validity criteria are the quality of the content, curricular importance, content coverage, cognitive complexity, linguistic adequacy, complementary skills and the value or weighting that will be given to each item. Among the external validity criteria are: equity, transfer and generalization, comparability and sensitivity of instruction; These have an impact on both students and teachers.

The objective of this review is to know the methodologies involved in the content validity process. This need arises from the decision to opt for a multiple-choice written exam, which measures knowledge and cognitive skills, as a modality to obtain the professional title of nurse or nurse midwife in a health school at a Chilean university. This process began in 2003 with the development of questions and their psychometric analysis; however, it is considered essential to determine the content validity of the instrument used.

To achieve this objective, a search was carried out in different databases of the electronic collection, available in the University’s multi-search system, using the key words: content validity, validation by experts, think-aloud protocol/ spoken thought . For the selection of publications, the inclusion criteria used were: articles published from 2002 onwards; full text, without language restriction, it should be noted that bibliography of classic authors on the subject was incorporated. 58 articles were found, of which 40 were selected.

The information found was organized around the 2 most used methodologies to validate content: expert committee and cognitive interview.

Content validity type

There are various methodologies that allow determining the content validity of a test or instrument, some authors propose that among them are the results of the test , the opinion of the students, cognitive interviews and evaluation by experts; others perform statistical analyzes with various mathematical formulas, for example, they use factor formulas with structural equations, these are less common.

In cognitive interviews, qualitative data is obtained that can be delved into; unlike expert evaluation that seeks to determine the skill that the exam questions are intended to measure. Some experts point out that to validate the content of an instrument, the following are essential: review of research, critical incidents, direct observation of the applied instrument, expert judgment and instructional objectives. The methods frequently mentioned in the reviewed articles are the expert committee and the cognitive interview.

Expert Committee

It is a methodology that allows determining the validity of the instrument through a panel of expert judges for each of the curricular areas to be considered in the evaluation instrument, who must analyze – at a minimum – the coherence of the items with the objectives of the courses, the complexity of the items and the cognitive ability to be evaluated. Judges must have training in question classification techniques for content validity. This methodology is the most used to perform content validation.

It is therefore essential that before carrying out this validation, two problems are resolved: first, determine what can be measured and second, determine who will be the experts who will validate the instrument. For the first, it is essential that the author does an exhaustive bibliographic review on the topic, he can also work with focus groups; This period is defined by some authors as a stage of development.

For the second, although there is no consensus that defines the characteristics of an expert, it is essential that he or she knows about the area to be investigated, whether at an academic and/or professional level, and that, in turn, he or she knows about complementary areas. However, other authors are more emphatic when defining who is an expert and consider it a requirement, for example, that they have at least 5 years of experience in the area. All this requires that the sample be intentional.

The characteristics of the expert must be defined and, at the same time, the number of them determined. Delgado and others point out that there should be at least 3, while García and Fernández , when applying statistical variables, concluded that the ideal number varies between 15 and 25 experts; However, Varela and others point out that the number will depend on the objectives of the study, with a range between 7 and 30 experts.

There are other less strict authors when determining the number of experts; they consider the existence of various factors, such as: geographical area or work activity, among others. Furthermore, they point out that it is essential to anticipate the number of experts who will not be able to participate or who will defect during the process.

Once it is decided what the criteria will be to select the experts, they are invited to participate in the project; During the same period, a classification matrix is prepared, with which each judge will determine the degree of validity of the questions.

For the process of preparing the matrix, the Likert scale of 3, 4 or 5 points is used where the evaluation of the possible answers could be classified into different types, for example: a) excellent, good, average and bad; b) essential; useful; useful, but not essential or necessary. The above depends on the type of matrix and the specific objectives pursued.

Furthermore, other studies mention having incorporated spaces where the expert can provide their contributions and appreciations regarding each question. Subsequently, each expert is given – via email or in person in an office provided by the researcher – the classification matrix and the instrument to be evaluated.

Once the results of the experts are obtained, the data is analyzed; The most common way is to measure the agreement of the evaluation of the item under review, reported by each of the experts, it is considered acceptable when it exceeds 80%; those that do not reach this percentage can be modified and subjected to a new validation process or simply be eliminated from the instrument.

Other authors report using Lashe’s (1975) statistical test to determine the degree of agreement between the judges; they observe a content validity ratio with values between -1 and +1. When the value is positive it indicates that more than half of the judges agree; On the contrary, if this is negative, it means that less than half of the experts are. Once the values are obtained, the questions or items are modified or eliminated.

To determine content validity using experts, the following phases are proposed: a) define the universe of admissible observations; b) determine who are the experts in the universe; c) present – by the experts – the judgment through a concrete and structured procedure on the validity of the content and d) prepare a document that summarizes the data previously collected.

The literature describes other methodologies that can be used together or individually. Among them are:

– Fehring Model: aims to explore whether the instrument measures the concept it wants to measure with the opinion of a group of experts; It is used in the field of nursing, by the American Nursing Diagnostic Association (NANDA), to analyze
the validity of interventions and results. The method consists of the following phases:

a) Experts are selected, who determine the relevance and relevance of the topic and the areas to be evaluated using a Likert scale.
b) The scores assigned by the judges and the proportion of these in each of the categories of the scale are determined, thereby obtaining the content validity index (CVI); This index is achieved by adding each of the indicators provided by the experts in each of the items, and, finally, it is divided by the total number of experts. Each of these particular indices are averaged, those whose average does not exceed 0.8 are discarded.
c) The format of the text is definitively edited, taking into account the CVI value, according to the aforementioned parameter, those items that will make up the final instrument and those that, due to their low CVI value, are considered critical and must be reviewed are determined. .

An example of a specific use of this model was the adaptation carried out by Fehring to carry out the content validity of nursing diagnoses; In this case, the author proposes 7 characteristics that an expert must meet, which are associated with a score according to their importance. It is expected to obtain at least 5 of them to be selected as an expert.

The maximum score is obtained by the degree of Doctor of Nursing (4 points) and one of the criteria for the minimum scores (1 point) is having one year of clinical practice in the area of interest; It is important to clarify that the authors recognize the difficulty that exists in some countries due to the lack of expertise of professionals.

– Q Methodology: it was introduced by Thompson and Stephenson in 1935, in order to identify in a qualitative-quantitative way common patterns of opinion of experts regarding a situation or topic. The methodology is carried out through the Q ordering system, which is divided into stages: the first brings together the experts as advised by Waltz (between 25 and 70), who select and order the questions according to their points of view. on the topic under study, in addition, bibliographic evidence is provided as support.

The second phase consists of collecting this information, by each of the experts, according to relevance, which goes along a continuum, from “strongly agree” to “strongly disagree”; Finally, statistical analyzes are carried out to determine the similarity of all the information and the dimensions of the phenomenon. 30

– Delphi Method: allows obtaining the opinion of a panel of experts; It is used when there is little empirical evidence, the data are diffuse or subjective factors predominate. It allows experts to express themselves freely since opinions are confidential; At the same time, it avoids problems such as poor representation and the dominance of some people over others.

During the process, 2 groups participate, one of them prepares the questions and designs exercises, called the monitor group, and the second, made up of experts, analyzes them. The monitoring group takes on a fundamental role since it must manage the objectives of the study and, in addition, meet a series of requirements, such as: fully knowing the Delphi methodology, being an academic researcher on the topic to be studied and having skills for interpersonal relationships.

The rounds happen in complete anonymity, the experts give their opinion and debate the opinions of other peers, make their comments and reanalyze their own ideas with the feedback of the other participants. Finally, the monitoring group generates a report that summarizes the analysis of each of the responses and strategies provided by the experts. It is essential that the number of rounds be limited due to the risk of abandonment of the process by the experts.

The latter is the most used due to its high degree of reliability, flexibility, dynamism and validity (content and others); Among its attributes, the following stand out: the anonymity of the participants, the heterogeneity of the experts, the interaction and prolonged feedback between the participants, this last attribute is an advantage that is not present in the other methods. Furthermore, there is evidence that indicates that it is a contribution to the security of the decision made, since this responsibility is shared by all participants.

What is your plan for quality control in data collection?

December 11, 2023 by Lillian

What is your plan for quality control in data collection?

What is Data Collection?

Data collection is the procedure of collecting, measuring, and analyzing accurate insights for research using standard validated techniques.

The ability to identify and resolve quality-related problems quickly and efficiently is essential for anyone working in quality control or interested in process improvement. With the seven basic quality tools in your possession, you can easily manage the quality of your product or process, whatever industry you serve.

Where did quality tools originate?

The seven quality tools were originally developed by Japanese engineering professor Kaoru Ishikawa. They were implemented by Japan’s industrial training program during the postwar period, when the country turned to statistical quality control as a means of quality assurance. His goal was to implement basic, easy-to-use tools that workers from diverse backgrounds and with varied skill sets could implement without extensive training.

Today, these quality management tools are still considered the reference for solving a variety of problems. They are often implemented in conjunction with today’s most widely used process improvement methodologies , such as various phases of Six Sigma, TQM, continuous improvement processes, and Lean management.

The seven quality tools

1. Stratification

Stratification analysis is a quality control tool used to classify data, objects, and people into separate and distinct groups. Separating data through stratification can help you determine its meaning and reveal patterns that might otherwise go unnoticed when grouped together.

Whether you examine equipment, products, shifts, materials, or even days of the week, stratification analysis allows you to understand data before, during, and after it is collected.

To get the most out of the stratification process, think about what information about your data sources can affect the final results of the analysis. Make sure you configure your data collection to include that information.

2. Histogram

Quality professionals are often tasked with analyzing and interpreting the behavior of different groups of data, in an effort to manage quality. This is where quality control tools like the histogram come into play.

The histogram can help you represent the frequency distribution of data clearly and concisely across different groups in a sample, allowing you to quickly and easily identify areas for improvement within processes. The structure is similar to that of a bar chart: each bar within a histogram represents a group, and the height of the bar represents the frequency of the data within that group.

Histograms are particularly useful when breaking down the frequency of data into categories such as age, days of the week, physical measurements, or any other category that can be arranged chronologically or numerically.

3. Check (or count) sheet

Check sheets can be used to collect quantitative or qualitative data. When used to collect quantitative data, they may be called count sheets. A check sheet collects data in the form of check or count marks that indicate how many times a particular value has occurred, allowing you to quickly focus on defects or errors within your process or product, defect patterns, and even , the causes of specific defects.

With their simple setup and easy-to-read graphs, check sheets make it easy to record preliminary frequency distribution data when measuring processes. This particular chart can be used as a preliminary data collection tool when creating histograms, bar charts, and other quality tools.

4. Cause and effect diagram (fishbone or Ishikawa diagram)

Introduced by Kaoru Ishikawa, the fishbone diagram helps users identify the various factors (or causes) that lead to an effect, usually represented as a problem to be solved. Named for its resemblance to a fishbone, this quality management tool works by defining a quality-related problem on the right side of the diagram, with individual root causes and subcauses branching off to its left.

The causes and subcauses in this diagram are generally classified into six main groups: measurements, materials, personnel, environment, methods, and machines. These categories can help you identify the possible source of your problem while maintaining a structured and orderly diagram.

5. Pareto diagram (80-20 rule)

As a quality control tool, the Pareto chart operates according to the 80-20 rule. This rule assumes that, in any situation, 80% of the problems in a process or system are caused by the top 20% of factors, often called the “vital few.” The remaining 20% of problems are caused by the 80% of the least important factors.

The Pareto chart is a combination of a bar and line chart, which represents individual values in descending order using bars, while the cumulative total is represented by the line.

The goal of the Pareto chart is to highlight the relative importance of a variety of parameters, allowing you to identify and focus your efforts on the factors that have the greatest impact on a specific part of a process or system.

6. Scatter plot

Of the seven quality tools, the scatterplot is the most useful for representing the relationship between two parameters, which is ideal for quality control professionals trying to identify cause-and-effect relationships.

The variable values are on the Y axis of the diagram and the independent values are on the X axis. Each point represents an intersection point. When joined together, those points can highlight the relationship between the two parameters. The stronger the correlation in the diagram, the stronger the relationship between the parameters.

Scatter plots can be useful as a quality control tool when used to define relationships between quality defects and possible causes, such as environment, activity, personnel, etc. Once the relationship between a particular defect and its cause has been established, you can implement focused solutions with possible better results.

. Control chart (also called Shewhart chart)

Named after Walter A. Shewhart, this quality improvement tool can help quality improvement professionals determine whether or not a process is stable and predictable, making it easier to identify factors that can lead to variations or defects.

Control charts use a center line to represent an average or mean, as well as an upper and lower line to represent control limits based on historical data. By comparing historical data with data collected from your current process, you can determine if your process is controlled or affected by specific variations.

Using a control chart can save your organization time and money by predicting process performance, especially in terms of what your customer or organization expects from the final product.

Additional: flowcharts

Some sources change the stratification to include flowcharts as one of the seven basic tools of quality control. Flowcharts are commonly used to document organizational structures and process flows, making them ideal for identifying bottlenecks and unnecessary steps within a process or system.

Mapping your current process can help you more effectively identify which activities are completed by whom, how processes flow from one department or task to another, and what steps can be eliminated to streamline the process.

What are the advantages and disadvantages of different data collection methods?

December 11, 2023December 11, 2023 by Lillian

What are the advantages and disadvantages of different data collection methods?

What is Data Collection?

Data collection is the procedure of collecting, measuring, and analyzing accurate insights for research using standard validated techniques.

Collecting data helps your organization answer relevant questions, evaluate results, and better anticipate customer probabilities and future trends.

In this article you will learn what data collection is, what it is used for, the advantages and disadvantages it has, the skills or abilities that a professional requires to carry out correct data collection, the methods used and some tips to carry it out. cape.

What is data collection?

According to Dr. Luis Eduardo Falcón Morales, director of the Master’s Degree in Applied Artificial Intelligence at the Tecnológico de Monterrey, he explains to us that currently everything generates data in any format, whether written, by video, comments on social networks, tweets, etc. .

“The issue here is that then this data collection begins to collect information to try to find information about the processes on which these data are being generated,” said Falcón Morales.

So we can say that data collection is the process of searching, collecting and measuring data from different sources to obtain information about the processes, services and products of your company or business and to be able to evaluate these results so that you can make better decisions.

What is data collection used for?

Teacher Luis Eduardo indicated that data collection mainly serves to improve continuous improvement processes but it must be understood that it also depends to a large extent on the problem being attacked or the objective set for which said collection is being carried out.

Next, he gives us some uses of data collection:

Identify business opportunities for your company, service or product.
Analyze structured data (data that is in a standardized format, meets a defined structure, and is easily accessible to humans and programs) in a simple way to understand the context in which said data was generated.
Analyze unstructured data (data sets, typically large collections of files, not stored in a structured database format, such as social media comments, tweets, videos, etc.) in a simple way to understand context in which said data were developed.
Store data according to the characteristics of a specific audience to support the efforts of your marketing area.
Better understand the behaviors of your clients, users and leads.

Data Collection Methods

Phone vs. Online vs. In-Person Interviews

Essentially there are four choices for data collection – in-person interviews, mail, phone, and online. There are pros and cons to each of these modes.

In-Person Interviews
- Pros: In-depth and a high degree of confidence in the data
- Cons: Time-consuming, expensive, and can be dismissed as anecdotal
Mail Surveys
- Pros: Can reach anyone and everyone – no barrier
- Cons: Expensive, data collection errors, lag time
Phone Surveys
- Pros: High degree of confidence in the data collected, reach almost anyone
- Cons: Expensive, cannot self-administer, need to hire an agency

Web/Online Surveys
- Pros: Cheap, can self-administer, very low probability of data errors
- Cons: Not all your customers might have an email address/be on the internet, customers may be wary of divulging information online.

In-person interviews always are better, but the big drawback is the trap you might fall into if you don’t do them regularly. It is expensive to regularly conduct interviews and not conducting enough interviews might give you false positives. Validating your research is almost as important as designing and conducting it.

We’ve seen many instances where after the research is conducted – if the results do not match up with the “gut-feel” of upper management, it has been dismissed off as anecdotal and a “one-time” phenomenon. To avoid such traps, we strongly recommend that data-collection be done on an “ongoing and regular” basis.

This will help you compare and analyze the change in perceptions according to marketing for your products/services. The other issue here is sample size. To be confident with your research, you must interview enough people to weed out the fringe elements.

A couple of years ago there was a lot of discussion about online surveys and their statistical analysis plan. The fact that not every customer had internet connectivity was one of the main concerns.

Although some of the discussions are still valid, the reach of the internet as a means of communication has become vital in the majority of customer interactions. According to the US Census Bureau, the number of households with computers has doubled between 1997 and 2001.

Data Collection Examples

Data collection is an important aspect of research. Let’s consider an example of a mobile manufacturer, company X, which is launching a new product variant. To conduct research about features, price range, target market, competitor analysis, etc. data has to be collected from appropriate sources.

The marketing team can conduct various data collection activities such as online surveys or focus groups.

The survey should have all the right questions about features and pricing, such as “What are the top 3 features expected from an upcoming product?” or “How much are your likely to spend on this product?” or “Which competitors provide similar products?” etc.

For conducting a focus group, the marketing team should decide the participants and the mediator. The topic of discussion and objective behind conducting a focus group should be clarified beforehand to conduct a conclusive discussion.

Data collection methods are chosen depending on the available resources. For example, conducting questionnaires and surveys would require the least resources, while focus groups require moderately high resources.

Advantages and disadvantages of data collection

Falcón Morales pointed out that the main advantage, and the most important, is knowledge itself, because knowing is power in some way in your company, it is knowing what your customers [4] think is something negative or positive about your product, service or process.

However, he indicated that the main disadvantage is that people often think that “data collection is magic” and that is not the case. It is a process of continuous improvement, therefore it has no end.

“It is not I apply it once and that’s it, no, it is an endless cycle,” said the director of the Master’s Degree in Applied Artificial Intelligence.

The other disadvantage is the ethical question of the professional or the company to handle the data, “since we do not know what use they may give it.”

Skills to carry out data collection

The director of the Master’s Degree in Applied Artificial Intelligence explained that the main skills are soft skills. They are between them:

Critical thinking
Effective communication
Proactive problem solving
Intellectual curiosity
Business sense

Methods for data collection

Data collection can be carried out through research methods, which are:

Analytical method : this method reviews each data in depth and in an orderly manner; goes from the general to the particular to obtain conclusions.
Synthetic method : here the information is analyzed and summarized; Through logical reasoning he arrives at new knowledge.
Deductive method : this method starts from general knowledge to reach singular knowledge.
Inductive method : from the analysis of particular data, general conclusions are reached.

Tips for carrying out data collection

Falcón Morales provided 5 tips to the professional to collect data:

Make a plan with the objective to be solved.
Gather all the data.
Define the data architecture.
Establish data governance.
Maintain a secure data channel.

What measures are best to address potential biases in the selection of your data sources?

What is a Data Source

2. Measurement bias

How to best handle data storage and archiving after the project is finished?

What is Data Collection?

Joint Statement on Research Data from STM, DataCite and Crossref

During the project…

What best considerations have you made for accessibility in your data collection?

What are Data Collection Methods?

How is consent and data collection from minors best addressed?

Data collection

The right to the protection of personal data: origin, nature and scope of protection.

Origins and legal autonomy

Natural capacity as an essential element for the exercise of personality rights

What are the potential worst limitations of your data collection approach?

Data Collection Methods

Main types of limitations

What are the limitations of the research?

How to better document and inform yourself of any changes made during the data collection process?

What is Data Collection?

What is a change control process and how is it implemented?

What does the change control process mean?

Change control vs. change management

What are the benefits of a change control process?

Higher productivity

Effective communication

Greater collaboration and teamwork

The five stages of a change control process

1. Start of the change request

2. Evaluation of the change request

3. Analysis of the change request

4. Implementation of the requested change

5. Closing the change request

What steps will you take to ensure the best generalization of your findings?

The Generalization

Transferability

Interrelationships between Generalization and Transferability

Generalization

Definition

Generalization Example 1

Generalization Example 2

Potential limitations

How do you ensure the best validity of your data?

Data

What is Data Collection?

Content validity type

Expert Committee

What is your plan for quality control in data collection?

What is Data Collection?

Where did quality tools originate?

The seven quality tools

1. Stratification

2. Histogram

3. Check (or count) sheet

4. Cause and effect diagram (fishbone or Ishikawa diagram)

5. Pareto diagram (80-20 rule)

6. Scatter plot

. Control chart (also called Shewhart chart)

Additional: flowcharts

What are the advantages and disadvantages of different data collection methods?

What is Data Collection?

What is data collection?

What is data collection used for?

Data Collection Methods

Phone vs. Online vs. In-Person Interviews

Data Collection Examples

Advantages and disadvantages of data collection

Skills to carry out data collection

Methods for data collection

Tips for carrying out data collection

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What steps will you take to ensure the best generalization
of your findings?