chapter 3 methodology data gathering procedure

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3.3 Methods of Quantitative Data Collection

Data collection is the process of gathering information for research purposes. Data collection methods in quantitative research refer to the techniques or tools used to collect data from participants or units in a study. Data are the most important asset for any researcher because they provide the researcher with the knowledge necessary to confirm or refute their research hypothesis. 2 The choice of data collection method will depend on the research question, the study design, the type of data to be collected, and the available resources. There are two main types of data which are primary data and secondary data. 34 These data types and their examples are discussed below.

Data Sources

Secondary data

Secondary data is data that is already in existence and was collected for other purposes and not for the sole purpose of a researcher’s project. 34 These pre-existing data include data from surveys, administrative records, medical records, or other sources (databases, internet). Examples of these data sources include census data, vital registration (birth and death), registries of notifiable diseases, hospital data and health-related data such as the national health survey data and national drug strategy household survey. 2 While secondary data are population-based, quicker to access, and cheaper to collect than primary data, there are some drawbacks to this data source. Potential disadvantages include accuracy of the data, completeness, and appropriateness of the data, given that the data was collected for an alternative purpose. 2 

Primary data

Primary data is collected directly from the study participants and used expressly for research purposes. 34 The data collected is specifically targeted at the research question, hypothesis and aims. Examples of primary data include observations and surveys (questionnaires). 34

• Observations: In quantitative research, observations entail systematically watching and recording the events or behaviours of interest. Observations can be used to collect information on variables that may be difficult to quantify through self-reported methods. Observations, for example, can be used to obtain clinical measurements involving the use of standardised instruments or tools to measure physical, cognitive, or other variables of interest. Other examples include experimental or laboratory studies that necessitate the collection of physiological data such as blood pressure, heart rate, urine, e.t.c. 2
• Surveys:  While observations are useful data collection methods, surveys are more commonly used data collection methods in healthcare research. 2, 34 Surveys or questionnaires are designed to seek specific information such as knowledge, beliefs, attitudes and behaviour from respondents. 2, 34 Surveys can be employed as a single research tool (as in a cross-sectional survey) or as part of clinical trials or epidemiological studies. 2, 34   They can be administered face-to-face, via telephone, paper-based, computer-based or a combination of the different methods. 2 Figure 3.7 outlines some advantages and disadvantages of questionnaires/surveys.

Designing a survey/questionnaire

A questionnaire is a research tool that consists of questions that are designed to collect information and generate statistical data from a specified group of people (target population). There are two main considerations in relation to design principles, and these are (1) content and (2) layout and sequence. 36 In terms of content, it is important to review the literature for related validated survey tools, as this saves time and allows for the comparison of results. Additionally, researchers need to minimise complexity by using simple direct language, including only relevant and accurate questions, with no jargon. 36 Concerning layout and sequence, there should be a logical flow of questions from general and easier to more sensitive ones, and the questionnaire should be as short as possible and NOT overcrowded. 36 The following steps can be used to develop a survey/ questionnaire.

Question Formats

Open and closed-ended questions are the two main types of question formats. 2   Open-ended questions allow respondents to express their thoughts without being constrained by the available options. 2, 38 Open-ended questions are chosen if the options are many and the range of answers is unknown. 38

On the other hand, closed-ended questions provide respondents with alternatives and require that they select one or more options from a list. 38 The question type is favoured if the choices are few and the range of responses is well-known. 38 However, other question formats may be used when assessing things on a continuum, like attitudes and behaviour. These variables can be considered using rating scales like visual analogue scales, adjectival scales and Likert scales. 2 Figure 3.8 presents a visual representation of some question types, including open-ended, closed-ended, likert rating scales, symbols, and visual Analogue Scales.

It is important to carefully craft survey questions to ensure that they are clear, unbiased and accurately capture the information researchers seek to gather. Clearly written questions with consistency in wording increase the likelihood of obtaining accurate and reliable data. Poorly crafted questions, on the other hand, may sway respondents to answer in a particular way which can undermine the validity of the survey. The following are some general guidelines for question wording. 39

Be concise and clear: Ask succinct and precise questions, and do not use ambiguous and vague words. For example, do not ask a patient, “ how was your clinic experience ? What do you mean by clinic experience? Are you referring to their interactions with the nurses, doctors or physiotherapists?

Instead, consider using a better-phrased question such as “ please rate your experience with the doctor during your visit today ”.

Avoid double-barrelled questions. Some questions may have dual questions, for example: Do you think you should eat less and exercise more?

Instead, ask:

• Do you think you should eat less?
• Do you think you should exercise more?

Steer clear of questions that involve negatives: Negatively worded questions can be confusing. For example, I find it difficult to fall asleep unless I take sleeping pills .

A better phrase is, “sleeping pills make it easy for me to fall asleep.”

Ask for specific answers. It is better to ask for more precise information. For example, “what is your age in years?________ Is preferable to -Which age category do you belong to?

☐  <18 years

☐ 18 – 25 years

☐ 25 – 35 years

☐ > 35 years

The options above will give more room for errors because the options are not mutually exclusive (there are overlaps) and not exhaustive (there are older age groups above 35 years).

Avoid leading questions. Leading questions reduces objectivity and make respondents answer in a particular way. Questions related to values and beliefs should be neutrally phrased. For example, the question below is worded in a leading way – Conducting research is challenging. Does research training help to prepare you for your research project?

An appropriate alternative: Research training prepares me for my research project.

Strongly agree           Agree                    Disagree              Strongly disagree

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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CHAPTER 3 - RESEARCH METHODOLOGY: Data collection method and Research tools

As it is indicated in the title, this chapter includes the research methodology of the dissertation. In more details, in this part the author outlines the research strategy, the research method, the research approach, the methods of data collection, the selection of the sample, the research process, the type of data analysis, the ethical considerations and the research limitations of the project. The research held with respect to this dissertation was an applied one, but not new. Rather, numerous pieces of previous academic research exist regarding the role of DMOs in promoting and managing tourist destinations, not only for Athens in specific, but also for other tourist destinations in Greece and other places of the world. As such, the proposed research took the form of a new research but on an existing research subject. In order to satisfy the objectives of the dissertation, a qualitative research was held. The main characteristic of qualitative research is that it is mostly appropriate for small samples, while its outcomes are not measurable and quantifiable (see table 3.1). Its basic advantage, which also constitutes its basic difference with quantitative research, is that it offers a complete description and analysis of a research subject, without limiting the scope of the research and the nature of participant’s responses (Collis & Hussey, 2003).

Related Papers

fit K attipoe

Waqar Muhammad

business ethics and leadership

Mary Constantoglou

As the tourism sector is continually evolving, touristic destinations and service providers should give close and thoughtful attention to customers' satisfaction, particularly during the Covid-19 pandemic period. Tourism for Greece represents one of the most valuable pillars of the economy and the impact of the pandemic to the sector and GDP will be significant. In this era, it is evident the importance of the Sustainable Development Goals and effective Destination Management that will take into consideration all aspects of the local communities. Customer satisfaction is crucial to improving strategies that destinations must follow to service quality and satisfaction management strategies. Recent consumer and technological trends make customer satisfaction more important than ever. This paper aims to investigate the characteristics, preferences, images, satisfaction levels, and the overall experience gained by the tourists visiting Lesvos island in the North Aegean Region Greece. Primary research was conducted and the airport of the island during departure in 2019. The useful gathered questionnaires (201) provided helpful information to the island's DMO related to the visitors' demographic characteristics, destination perception, awareness and competitiveness, satisfaction and overall experience. The basic research findings were the strong impression of the visitors about the authenticity of the destination. They also believe that prices are excellent and the rate of value for money is high. At the same time, visitors think that the island is not promoted very good and the image/brand of the island is not very clear and well defined. It is the first research conducted to visitors departing from Lesvos island to the authors' best knowledge. The results and discussion of this study will be useful to the islands' DMO and the island's tourism authorities and the North Aegean Region and other similar island destinations, which wish to maximize the benefits of tourism development.

Spyros Langkos

DOI: 10.13140/2.1.3231.1683 INDEPENDENT STUDΥ - THESIS " Athens as an international tourism destination: An empirical investigation to the city’s imagery and the role of local DMO’s.” The aim of this project was to identify the role of DMOs in promoting Athens as a tourist destination, as well as to evaluate their effectiveness in terms of marketing and managing the tourist product of Athens, its popularity and imagery. The aim of this thesis is to identify the role of DMOs in promoting Athens as a tourist destination, as well as to evaluate their effectiveness in terms of marketing and managing the tourist product of Athens, its popularity and imagery. For that purposes, 6 personal interviews were conducted with executives who were working in 6 famous local DMOs operating both generally in Greece and specifically in Athens. The result of this study indicated that DMOs are playing a crucial role for the promotion of Athens as a tourist destination. DMOs key responsibilities include: development of sophisticated online marketing strategies, creation of high quality published material, participation in international tourism fairs for developing relationships with key stakeholders and development of network synergies with airline companies, and international tourism organizations. Athens is a destination with great potential for future growth and for that reason DMOs have designed certain plans for the next three years in order to exploit the opportunities which are presented. The future plans of the DMOs give particular emphasis in the opening in new tourist markets and more particularly in the markets of Russia, Turkey China, and USA. Besides, DMOs will focus in five forms of tourism which can be developed successfully in Athens, namely: 1) cultural tourism, 2) health tourism, 3) luxury tourism, 4) city break tourism, and 5) convention tourism On the other hand, the executives of the DMOs underlined several problems which prevent the tourism development of Athens. The majority of these problems are related with the business environment in Greece which has become less competitive due to the crisis. Besides, the city as a destination faces the problems of seasonality as well as missing infrastructures. Finally, the research showed that DMOs have established strong and long term relationships with DMOs in foreign countries. These partnerships allow the Greek DMOs to be updated concerning the trends of the global tourism market as well as enhance the movement of tourists between cooperating countries. Nevertheless, the promotion of Athens as a tourism destination requires a more concerted effort between the public and the private stakeholders which are involved in the tourism industry. The benefits will be multiplied for businesses, the state and the society in general. Keywords & terms: Destination Marketing Organizations, DMO’s, tourism destination, tourist product, popularity & imagery, interviews, online marketing strategies, Athens, Greece, international tourism fairs, stakeholder relationships, network synergies, airline companies, future growth, tourist markets, cultural tourism, health tourism, luxury tourism, city break tourism, convention tourism, tourism development of Athens, business environment in Greece, seasonality, infrastructures

HOTELARIA & TURISMO UNIV ALGARVE, PORTUGAL

Aan Jaelani (SCOPUS ID: 57195963463)

Dear Participant, I am Spyros Langkos and I am collecting data from you which will be used in my dissertation for: Athens as an international tourism destination. An empirical investigation to the city’s imagery and the role of local DMO’s, as part of my MSc in Marketing Management at the University of Derby. The objective of the dissertation research, will be to evaluate the contribution of Athens DMO’s towards the rising popularity of the city of Athens as an international destination within the context of Destination Marketing and the information you will be asked to provide will be used to help to provide insights to achieve this objective. The data you provide will only be used for the dissertation, and will not be disclosed to any third party, except as part of the dissertation findings, or as part of the supervisory or assessment processes of the University of Derby. The data you provide will be kept until the 31st of December 2014, so that it is available for scrutiny by the University of Derby as part of the assessment process. If you feel uncomfortable with any of the questions being asked, you may decline to answer specific questions. You may also withdraw from the study completely, and your answers will not be used. And, if you later decide that you wish to withdraw from the study, please write to me at Spyros Langkos, email: [email protected] no later than the 30th of March 2014 and I will be able to remove your response from my analysis and findings, and destroy your response. The Researcher Spyros Langkos

Turismo y Sociedad

Andres Camacho-Murillo

One of the most important tools for conducting research on tourism topics is the utilised research method. Leguizamón’s book shows the adequacy of the scientific method to examine research problems in tourism issues. The systematic process described in the book includes the research problem delimitation, research hypothesis formulation, data collection, hypothesis testing and analysis and interpretation of results. The scientific method has been applied in experimental studies (whether causal or quasi-experiments as noted by the author) on diverse tourism issues, including the demand for international (schiff & Becken, 2011) and domestic (Alegre, Mateo, & pou, 2013) tourism, on competition in the package tour industry (Davies & Downward, 1998), on the impact of tourism on economic growth (Ivanov & Webster, 2007) and poverty alleviation (Croes & Vanegas, 2008), among other issues. The volume provides other research designs besides experimental research that can be applied to tourism related issues, including exploratory, descriptive and evaluative designs.

Gregory T Papanikos

This abstract book includes all the summaries of the papers presented at the 9th Annual International Conference on Tourism 10-13 June 2013, organized by the Sciences and Engineering Research Division of the Athens Institute for Education and Research. In total there were 34 papers and 45 presenters, coming from 19 different countries (Australia, Canada, China, Cyprus, Egypt, Hong Kong, Hungary, Ireland, Israel, Lithuania, Poland, Portugal, South Africa Spain, Taiwan, Turkey, UAE, UK, USA). The conference was organized into IX sessions that included areas of Tourism Marketing Issues, Tourism Destination and Development, Special Tourism Themes Entrepreneurship, Economics and Business in the Tourism Industry and other related fields. As it is the publication policy of the Institute, the papers presented in this conference will be considered for publication in one of the books of ATINER.

The tourism industry in Greece is one of the most important sectors of the country’s economy it terms of value (Hellenic Statistical Authority, 2014). There are several public and private organizations which are involved in the tourism industry in Greece for promoting destinations such as the Destination Management Organizations (DMOs). In this context, the aim of this project is to evaluate the contribution of Athens DMO’s towards the rising popularity of the city of Athens as an international destination within the context of Destination Marketing. More specifically, the project has the following objectives:  To identify the activities which are performed by DMOs for promoting Athens and to evaluate the strategic role of DMO’s.  To identify the importance of destination marketing through its application in the Greek Tourism Industry and the particular case of Athens.  To portrait the opinions and activity planning of Greek DMO’s Executives, who are considered to be experts in the tourism field.  To provide insights and new trends of high informational value about the Tourism Industry in Athens.  To highlight the latest incentives and programming concerning the city’s future developments.  To identify the key problems that Athens faces as a tourist destination and to recommend points for improvement from the DMOs perspective.

The aim of this thesis is to identify the role of DMOs in promoting Athens as a tourist destination, as well as to evaluate their effectiveness in terms of marketing and managing the tourist product of Athens, its popularity and imagery. For that purposes, 6 personal interviews were conducted with executives who were working in 6 famous local DMOs operating both generally in Greece and specifically in Athens.

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3 Gathering and Analyzing Quantitative Data

Although the goal of any research study is to gather information to analyze, this process can be a little daunting. Hopefully, you’ve taken the time to plan your approach so that you have a clear plan for the type of information you’ll be gathering and the process by which you will assign meaning and glean an understanding about what you’ve collected. As was the case with designing your approach to your study, a systematic method by which you plan the analysis of your data will make your life a whole lot easier. This chapter will provide a basic overview of how to gather data and begin the analysis of those data with an overview of quantitative statistical approaches.

Chapter 3: Learning Objectives

As you work to understand how best to approach gathering and analyzing quantitative data you will:

• Describe the methodology of developing measurement instruments to gather and quantify data
• Discuss the factors that influence the coding process
• Describe how relationships between dependent and independent variables influence selection of statistical approach
• Compare questions of difference, association, and description
• Identify both basic and complex statistical approaches specific to a research question

Basic Approaches to Gathering and Analyzing Quantitative Data

Developing or selecting measurement instruments.

There are several methods by which data can be gathered. These methods will be directed by the approach you’ve taken as well as the question you’re investigating. Methods by which data may be gathered include, but are not limited to:

• Case or patient specific information
• Questionnaires or surveys
• Structured interviews
• Observations
• Standardized inventories

The method by which you will gather data is hugely important to the validity of your results. Ideally, a researcher would utilize established measurement tools which have been validated through consistent study. However, this is not always feasible. When you cannot utilize a validated instrument (e.g. a common questionnaire or scale) to measure variables in your specific sample, the instrument you either revise or develop should at least be pilot tested. Pilot testing is a procedure by which measurement tools or instruments are implemented on a small scale to evaluate the feasibility and identify adverse events in design prior to implementing the tool in a larger study.

Collecting Data

Again, the type and amount of data will depend on the approach you’ve selected. Regardless of this, however, all data will need to be checked to ensure that it is ‘clean’. This means removing duplicated responses or entries or other erroneous or inconsistent data that may impact your ability to analyze. Note: You should never change or alter data collected. Rather, if there are inconsistencies in how you expected the data to be collected, you must decide how to deal with those issues before moving forward.

Dealing with Data Collection Issues

You have asked respondents how well the training you provided met the objectives outlined at the beginning of the course using a likert scale (1=strongly disagree; 5= strongly agree). You have several respondents who encircled 4 and 5. You now have to decide what to do with this data. You have a few options:

• Create a new category of scores using the average of 4+5 (9/2=4.5)
• You can exclude all respondents who did this

Note: When designing questionnaires or surveys, it is essential to ensure that the questions are clear and concise by utilizing directive language such as, “Select the option most appropriate…”, or “Which ONE of the following”.

Data must then be transferred into a format where it can be sorted and analyzed. The most common approaches to this is to either input or export data to software such as Microsoft Excel (TM) or specialized statistical software such as Statistical Packages for the Social Sciences (SPSS) (TM) .

Data may be entered either manually or electronically imported to analysis software:

Coding may be thought of as the process of translating the information you’ve gathered such that variables can ‘talk’ to one another through analysis. This is done by numbers to the attributes, or layers, within a variable. There are a few rules governing how this is done:

• Each coded level within a variable must be mutually exclusive: Only one value can be used to code each layer within a variable

Mutually Exclusive Coding

In our dataset example, patients may only be in one location, so you can code the each layer exclusively:

• BDT-MICU (Coded as ‘1’)
• BDT-SICU (Coded as ‘2’)
• BMCMH-ICU (Coded as ‘3’)

Similarly, each diagnosis may be coded independently:

• Sepsis (Coded as ‘1’)
• Trauma (Coded as ‘2’)
• Pneumonia (Coded as ‘3’)
• Post-Op (Coded as ‘4’)

There are instances wherein more than one response is indicated or where  there may be a designation within each variable. In these cases, each designation would need to have a separate code (e.g. ‘yes’/’no’). In our example, patients either have ventilator associated pneumonia (VAP), or they did not. Similarly, patients either died, or did not.  You will need to code each layer to indicate whether it was experienced:

• VAP (yes= ‘1’, no= ‘0’)
• Death (yes= ‘1’, no= ‘0’)

2. For each variable entered, there must be a code assigned: Numeric codes need to be applied to all data entries, except for missing responses. In the case of missing data, leave the cell blank. Leaving the cell blank ensures that the item will be counted as ‘missing’ rather than associated with a different code. Depending on the software being used, designating missing data as ‘N/A’ may result in errors during analysis.

3. Coding needs to be consistent: As you make decisions about how to proceed with your data, it is imperative that your decisions are consistent across the entire data set. For example, if you decide that you will be excluding a participant with missing data, you must exclude all missing data.

4. Data relating to specific cases or responses must be organized: Each variable relating to a participant must be organized in a way which relates to that participant. For our example above, patients are indicated in column ‘A’. Subsequent columns are variables which relate to that patient; however, each piece of data relating to the specific patient is indicated in the row associated with that specific patient. This is important to ensure that data is correctly attributed during analyses.

5.  Variables need to be labeled appropriately: It’s common to need to abbreviate the names of variables. Do this in a way which helps you remain organized. If you want to keep the data one one sheet, you can simply add a coded column next to the original variable. Or, you can begin an entirely new spreadsheet of coded variables. Regardless of ;approach, you’ll need to keep a record of how you’ve changed variable names and assigned codes. Creating a codebook will help you recall which codes were assigned to which attributes so that you can correctly interpret analyses.

Creating a Codebook

Creating a codebook is imperative to ensuring you can correctly interpret the analyses you perform on the data

Selecting the Appropriate Statistical Method

In chapter 2, we discussed three types of general statistical approaches:

• Experimental: Has an active independent variable. The purpose of the study is to manipulate the independent variable to evaluate the impact of that variable on the dependent variable; may experimental (randomized sampling) or quasi-experimental (sample not randomized)
• Non-Experimental: Has an attribute independent variable rather than an active independent variable. The purpose of the study is to explore relationships among variables. Survey and observational research fit into this category.
• Descriptive: Does not have an independent variable. The purpose of descriptive research is to describe a selected sample rather than make inferences about that sample to the population.

We also discussed five types of specific approaches:

• Randomized experimental
• Quasi-experimental
• Comparative
• Associational/Correlational
•  Descriptive

If you recall, we underscored that the type of question central to your work guides the selection of your research approach. Similarly, the type of question you’re asking will help to guide your selection of appropriate statistical approaches to analyze the data you’ve collected. There are three main types of questions and it’s important to note that a research project may incorporate several different statistical approaches, depending on the type of question(s) asked.

Adding to the chart introduced in chapter two, information relating to the purpose of the research question, we can identify the next step in identifying the most appropriate statistical approach.

Descriptive questions

Descriptive questions seek to describe a specific sample. Descriptive statistics include measures of central tendency and variability.

Examples of Descriptive Statistics

• This is a description of the number of participants who fit into any one attribute or variable. This number may include the percentage of the sample that this number represents

• Mean= Average (sum of the measurements divided by the number of entries) of all numerical data included for a specific variable

• Median= The middle number when numeric data are arranged in either ascending or descending order

• Mode: The measurement that occurs most frequently in a set of data

• Standard deviation is the most common when the data is normally distributed

Questions of difference

Answers to these questions center on the comparison of groups and the difference between those groups. Randomized experimental, quasi-experimental, and comparative approaches support questions of difference and therefore, use similar statistical approaches. Questions of difference utilize difference inferential statistics because the goal is to compare groups’ average scores on a dependent variable.

The selection of either basic or complex difference statistics will depend on how many independent and/or independent variables you are comparing:

One dependent and/or independent variable

As we’ve discussed, understanding the relationships between or among your dependent and independent variable(s) is extremely important. If you identify only one dependent and/or independent variable, Gliner, Morgan and Leech (2017) indicate that there are a few considerations you’ll need to investigate:

• Independent t-test : Used to compare means of independent samples or groups with one independent variable with two categories.
• Paired t-test : Used to compare means of repeated measures within the same group with one independent variable with two categories.
• One way ANOVA : Used to compare means of independent samples or groups with one independent variable that has two or more categories.
• Repeated Measures ANOVA : Used to compare means of repeated measures or related samples with one independent variable that has two or more categories.
• Mann-Whitney can be used to compare medians or ranks of groups with one independent variable that has two categories.
• Wilcoxon or Sign test can be used to compare medians or ranks within groups which have one independent variable with two categories
• Kruskal-Wallis can be used to compare medians or ranks of independent samples with one independent variable but that has two or more categories.
• Friedman test can be used to compare medians or ranks for repeated measures or related samples with one independent variable that has two or more categories.
• Chi Square or Fisher’s exact test can be used to compare counts within groups which have one independent variable with two categories. Chi square is a nonparametric test (used when sample is NOT normally distributed)
• Mcnemar can be used to compare counts within groups which have one independent variable with two categories.
• Chi Square can also be used to compare counts of independent samples with one independent variable but with that has two or more categories.
• Cochran Q Test can be used to compare counts for repeated measures or related samples with one independent variable that has two or more categories.

More than one independent and/or dependent variable

The inclusion of more than one independent and/or dependent variable will require the use of fairly complex statistical tests such as:

• Factorial ANOVA can be used to measure two or more independent variables between groups when you have one dependent variable; assuming normal distribution.
• Factorial ANOVA with repeated measures can be used to explore means of groups that are related, have one dependent variable, and two or more independent variables
• Log linear should only be used with a dependent variable is nominal and you are looking at differences between groups with more than two independent variables.
• MANOVA can be used in when looking at differences between groups with several dependent variables and two or more independent variables.
• MANOVA with repeated measures can be used in when looking at differences within a group and has several dependent variables as well as two or more independent variables.

Questions of association

Answers to these questions seek to identify whether there is an association or correlation between at least two variables. Associational inferential statistics can also be used to help predict associations between variables.

The selection of either basic or complex difference statistics will depend on how many independent variables are included in your work (Gliner, Morgan, & Leech, 2017):

Only one independent variable

• Pearson r or Bivariate regression to investigate the relationship between two variables for the same subject
• Spearman (Rho) or Kendall’s Tau can be used to explore the relationship between the ranks of two variables for the same subject
• Phi or Cramer's V can be used to identify relationships between the counts of two variables for the same subject.

Several independent variables

One continuous dependent variable?

• Multiple Regression : Used to predict the value of a variable based on the value of two or more other variables

One dichotomous dependent variable?

• Discriminant analysis : Use with normally distributed independent variables
• Logistic regression : Use when some independent variables are normal and some dichotomous or when ALL independent variables are dichotomous

Making it as easy as possible:

Although there are several factors that influence the selection of a statistical test, there are general questions you can ask to help guide your decision. We’ve covered several of those steps throughout this chapter; however, the figure below, adapted from Salkind and Frey (2020), can be thought of as a quick reference:

Additional how-to:

Now that you understand the basic concepts of choosing a statistical approach, you will be able to move forward. Although there are several conditions specific to your work which will impact your choices, understanding the general approach and considerations is the focus of this text. Additional information about which statistical tests are most appropriate for your specific design, as well as information about how to perform and interpret specific statistics can be found here: Choosing the correct statistical test

Key Takeaways

• Data must be collected systematically and prepared for analysis
• Mutually exclusive
• Numeric and applied to each entry
• The statistical approach you select will be guided by the study approach you selected and the question you’re asking
• Questions can either be of difference, association, or descriptive
• There are both basic and complex statistical approaches for both questions of difference and association
• You must understand the relationship between your dependent and independent variable(s) to identify the most appropriate statistical test
• The selection of statistical approaches is not ‘one-size-fits all’ and requires specific attention to be paid to the variables specific to the work

Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices. https://open.umn.edu/opentextbooks/textbooks/social-science-research-principles-methods-and-practices

Gliner, J.A., Morgan, G.A., & Leech, N.L. (2017). Research methods in applied settings: An integrated approach to design and analysis . Routledge

Salkind, N.J. & Frey, B.B. (2020). Statistics for people who think they hate statistics . Sage Publications

An initial study performed prior to implementing a large scale study to evaluate the feasibility of the approach on a larger scale

Compares means of at least two different samples or groups with one independent variables with two categories

Compare the means of the same group with two different data points (e.g. pre and post test scores).

Compares the means of different samples with one independent variable which has two or more categories.

Compares means of related or the same group with one independent variable that has two or more categories

Compares medians or ranks within groups which have one independent variable with two categories

Compares medians within a group that has one independent variable with two categories

Compares medians of different samples with one independent variable with two or more categories

Compares counts within groups with have one independent variable with two categories

Can be used when looking at differences either within or between groups with several dependent variables and two or more independent variables

Evaluates the relationship between two variables for the same subject

Explores relationship between ranks of two variables on the same subject

Identifies relationships between the counts of two variables with for the same subject

Used to predict the value of a variable based on the value of two or more other variables

Used to classify observations into non-overlapping groups, based on scores on one or more quantitative predictor variables

Uses a logistic function to model a binary dependent variable

Practical Research: A Basic Guide to Planning, Doing, and Writing Copyright © by megankoster. All Rights Reserved.

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17.3 Preparations for the data gathering process

Learning objectives.

Learners will be able to…

• Explain important influences to account for in qualitative data gathering
• Organize and document preparatory steps to plan data gathering activities for your qualitative proposal

As you may have guessed from our discussion regarding qualitative research planning and sampling, you have a number of options available for qualitative data gathering, and consequently, a number of choices to make. Your decisions should be driven by your research question and research design, including the resources that are at your disposal for conducting your study. Remember, qualitative research is a labor-intensive venture. While it may not require lots of fancy equipment, it requires a significant investment of people’s time and potentially other resources (e.g. space, incentives for participants, transportation). Each source of data (interviews, focus groups, observations, other artifacts), will require separate planning as you approach data gathering.

Our impact on the data gathering process

In the last chapter, you were introduced to the tool of reflexive journaling as a means of encouraging you to reflect on and document your role in the research process. Since qualitative researchers generally play a very active and involved role in the data gathering process (e.g. conducting interviews, facilitating focus groups, selecting artifacts), we need to consider ways to capture our influence on this part of the qualitative process. Let’s say you are conducting interviews. As you head into the interview, you might be bringing in thoughts about a previous interview, a conversation you just had with your research professor, or worries about finishing all your assignments by the end of the semester! During the interview, you are likely to be surprised by some things that are said or some parts may evoke strong emotions. These responses may lead you to consider pursuing a slightly different line of questioning, and potentially highlighting or de-emphasizing certain aspects. Understanding and being aware of your personal reactions during the data collection process is very important. As part of your design and planning, you may specify that you will reflexively journal before and after each interview in an attempt to capture pre- and post-interview thoughts and feelings. This can help us to consider how we influence and are influenced by the research process. Towards the end of this chapter, after we have had a chance to talk about some of these data gathering strategies, there is a reflexive journal prompt to help you consider how to begin to reflect on the way you as a researcher might impact your work and how you work might impact you.

Decision Point

How will you account for your role in the research process?

• This may be your reflexive journal or you may have other thoughts about how you can account for this.
• Whatever you choose, how will you develop a routine/habit around this to ensure that you are regularly implementing this?

Reflexive Journal Entry Prompt

This is going to be a bit meta, but for this prompt, I want you reflect on the reflecting you are doing for your reflexive journaling.

• Do you see this as a potentially helpful tool for tracking your influence and reactions? What appeals to you? What puts you off?
• If so, how did you develop this mindset?
• If not, how can you strengthen this skill?

When are we done

Finally, as you plan for your data collection you need to consider when to stop. As suggested previously in our discussion on sampling, the concept of saturation is important here. As a reminder, saturation is the point at which no new ideas or concepts are being presented as you continue to collect new pieces of data. Again, as qualitative researchers, we are often collecting and analyzing our data simultaneously. This is what enables us to continue screening for the point of saturation. Of course, not all studies utilize the point of saturation as their determining factor for the amount of data they will collect. This may be predetermined by other factors, such as restricted access or other limitations to the scope of the investigation. While there is no hard and fast rule for the quantity of data you gather, the quality is important; you want to be comprehensive, consistent, and systematic in your approach.

Next, we will discuss some of the different approaches to gathering qualitative data. I’m going to start out with Table 18.1 that allows us to compare these different approaches, providing you with a general framework that will allow us to dive a bit deeper into each one. After you finish reading this chapter, it might be helpful to come back to this table as you continue with your proposal planning.

Key Takeaways

• As you are preparing to initiate data collection, make sure that you have a plan for how you will capture and document your influence on the process. Reflexive journaling can be a useful tool to accomplish this.
• Be sure to take some time to think about when you will end your data collection. Make this an intentional, justified decisions, rather than a haphazard one.

A research journal that helps the researcher to reflect on and consider their thoughts and reactions to the research process and how it may be shaping the study

The point where gathering more data doesn't offer any new ideas or perspectives on the issue you are studying.  Reaching saturation is an indication that we can stop qualitative data collection.

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• Data Collection Methods | Step-by-Step Guide & Examples

Data Collection Methods | Step-by-Step Guide & Examples

Published on 4 May 2022 by Pritha Bhandari .

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental, or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

• The  aim of the research
• The type of data that you will collect
• The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address, and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

• Quantitative data is expressed in numbers and graphs and is analysed through statistical methods .
• Qualitative data is expressed in words and analysed through interpretations and categorisations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data.

If you have several aims, you can use a mixed methods approach that collects both types of data.

• Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
• Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

• Experimental research is primarily a quantitative method.
• Interviews , focus groups , and ethnographies are qualitative methods.
• Surveys , observations, archival research, and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design .

Operationalisation

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalisation means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

• You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness, and dependability.
• You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and time frame of the data collection.

Standardising procedures

If multiple researchers are involved, write a detailed manual to standardise data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorise observations.

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organise and store your data.

• If you are collecting data from people, you will likely need to anonymise and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
• If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimise distortion.
• You can prevent loss of data by having an organisation system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1 to 5. The data produced is numerical and can be statistically analysed for averages and patterns.

To ensure that high-quality data is recorded in a systematic way, here are some best practices:

• Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
• Double-check manual data entry for errors.
• If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

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