Appendix C: Figure Descriptions
Figure 2.1 image description: This graphic compares the four inquiry paradigms.
Positivist paradigm: 1) Focus is on objective reality. 2) Approach is deductive. Typical methods are experiments, systematic observation. 3) Data obtained is qualitative.
Interpretive paradigm: 1) Focus is on subjective understanding. 2) Approach is inductive. Typical methods are interviews, participant observation. 3) Data obtained is qualitative.
Critical paradigm: 1) Focus is on understanding and emancipation. 2) Approach is mainly inductive but not restricted as such. Typical methods are observations, interviews, action research. 3) Data obtained is mainly qualitative.
Pragmatic paradigm: 1) Focus is on what works. 2) Approach is inductive and/or deductive. Uses a range of methods or mixed methods. 3) Data obtained is qualitative and quantitative data. [Return to Figure 2.1]
Figure 2.2 image description: This graphic illustrates the six stages of the research process based on Deductive Reasoning: It begins with a Theory, 2) then a Hypothesis is developed from the Theory 3) Data Collection is used to test theories 4) Data Analysis 5) Draw Conclusions 6) Report Findings. [Return to Figure 2.2]
Figure 2.3 image description: This graphic illustrates the four stages of the research process based on Inductive Reasoning: It begins with Observations, 2) Data coding and analysis 3) Main findings (e.g., common themes) 4) New theory emerges. [Return to Figure 2.3]
Figure 2.4 image description: This graphic provides two examples of the progression from a general subject to a more specific research question.
The first example considers Relationships, which is then refined in four steps.
1) The topic chosen is: Abusive dating relationships. 2) You find in the literature: The notion of cyber sexual violence (for example, Cripps & Stermac, 2018). 3) You refine the topic more specifically to be: The negative impacts of using online dating apps. 4) Finally, you identify the research question: What is the relationship between online dating abuse and mental health among young adults? (Echevarria et al., 2023).
The second example considers Work and is then refined in four steps.
1) The topic chosen is: Employment of refugee groups. 2) You find in the literature: Refugee women face multifaceted barriers to entering the Canadian workforce (for example, Senthanar et al., 2020). 3) You refine the topic more specifically to be: Policy reforms needed to facilitate employment integration of women refugees. 4) Finally you identify the research question: How do social assistance programs affect employment integration of women refugees? (Senthanar & MacEachen, 2023). [Return to Figure 2.4]
Figure 4.1 image description: This graphic provides an overview of time considerations in relation to units of analysis.
Cross-sectional Research is described as the Same people at a single point in time.
Longitudinal Research has four examples provided. 1) Panel Study: Same people over time. Example, Same dating couples at Time 1 and Time 2. 2) Cohort Study: Similar people over time. Example, Couples who married in 2020 at Time 1 and other couples who married in 2020 at time 2. 3) Time-Series Study: Different people over time. Example, Newly dating couples at Time 1 and different newly dating couples at Time 2. 4) Case Study: One person over time. Example: A regular user of dating sites is studied over the course of a year. [Return to Figure 4.1]
Figure 4.2 image description: This graphic shows how the properties of each level of measurement are summarized.
Nominal: identifies differences/attributes. Examples: gender, religion, ethnicity, hair colour, shoe brand, marital status, blood type, employment status, eye colour, favourite television show, presence of children, gun ownership, smoker, type of dwelling, political orientation, and pet preference. Nominal measurement allows you to classify cases.
Ordinal: makes greater vs, less than, and higher vs. lower distinctions. Can order attributes. Examples: educational level, health status, job satisfaction, fitness level, hotel ratings (out of 5 stars), prestige ratings, student ratings of instruction, and customer service ratings. Ordinal measurement allows you to classify and order categories.
Interval: Makes precise comparisons (e.g. how much more or how many more) Lacks a true zero. Examples: intelligence (IQ), temperature in Celsius. Interval measurement allows you to classify and order categories and examine differences between the categories of the variables of interest.
Ratio: has true measurement. Can use ratios. Examples: test scores, net yearly income (in dollars), age, height, weight, and employment service in years. Ratio measurement allows you to classify and order categories and examine differences between the categories of the variables of interest. Statisticians sometimes make a further distinction between an interval and ratio level of measurement. [Return to Figure 4.2]
Figure 4.3 image description: This image distinguishes among techniques used to assess reliability. Two circles that overlap in the middle. One circle is titled index and contains the text common outcome. The other circle is titled scale and contains the text common cause, patterns and intensity. [Return to Figure 4.3]
Figure 4.4 image description: This image distinguishes among techniques used to assess reliability.
1) Test-retest: renders the same findings at two different times using the same instrument. 2) Split-half: renders the same findings provided by two-halves of the same instrument. 3) Inter-rater: renders the same findings as provided by two different observers. 4) Inter-item: renders the same findings as provided by two or more indicators. [Return to Figure 4.4]
Figure 4.5 image description: This image distinguishes among techniques used to assess validity
1) Face: has face value/appears to be a good measure. 2) Content: captures the full range of content. 3) Construct: compares well to other relevant measures. 4) Criterion: holds up to an external standard. [Return to Figure 4.5]
Figure 5.1 image description: This image is a random number table using Stat Trek. The figure contains one hundred random five-digit numbers. [Return to Figure 5.1]
Figure 5.2 image description: This image illustrates stratified sampling. The image consists of two circles. The first circle is labelled population and is divided into three equal segments labelled strata 1, strata 2 and strata 3. Each strata segment has an arrow pointing to the second smaller circle labelled sample. [Return to Figure 5.2]
Figure 5.3 image description: This image illustrates cluster sampling. There is a collection of different coloured hexagons, each assigned a number from one to ten. The text reads sample equals randomly selected clusters. Example: two and three. [Return to Figure 5.3]
Figure 5.4 image description: The image provides an overview of probability-based sampling techniques.
Simple Random
1) Used when there is a known sampling frame. 2) Equal chance of selection (unbiased). 3) Considered the ideal since the sample will be highly representative of the population. 4) There is still some sampling error but this can be reduced by increasing the sample size.
Systematic
1) Used when there is a known sampling frame. 2) Every nth element after a random start. 3) Is more straightforward to carry out than simple random sampling. 4) Could be problematic if there is any special order to the sampling frame.
Stratified
1) Used when there is a known sampling frame. 2) Based on subgroups that contain important attributes. 3) Good for ensuring certain strata are represented in the sample. 4) Used with simple or systematic sampling. 5) May not be representative depending on the distribution of the strata in the population.
Cluster
1) Used when the complete sampling frame is not readily available or accessible. 2) Practical when there are readily identifiable subgroups spread across diverse geographic locations. 3) Results in a fairly large overall sample size which could be costly to study. [Return to Figure 5.4]
Figure 6.1 image description: The image illustrates random sampling and random assignment. Random sampling is used to obtain a sample from a population. Random assignment is used to place participants into groups. [Return to Figure 6.1]
Figure 6.2 image description: The image displays the steps in a basic experimental design.
(1) random assignment, (2) Identical groups are assumed: an experimental and a control group, (3) Manipulate independent variable: the manipulation of an independent variable experienced by the experimental group, and (4) measure dependent variable: the measurement of a dependent variable (i.e., the outcome) (5) Draw conclusions: see what (if any) effect the independent variable had. [Return to Figure 6.2]
Figure 6.3 image description: The image displays the steps in a classic experimental design. Classic experimental design includes the four basic features from the basic design (random assignment, an experimental and a control group, the manipulation of an independent variable, and a post-test measurement of the dependent variable), along with a pre-test of the dependent measure. This design is also commonly called a pre-test–post-test design. [Return to Figure 6.3]
Figure 6.4 image description: The image illustrates the Static Group Comparison. In a static group comparison, there are two groups and a post-test measure, but random assignment was not an option for the placement of participants into the two groups. Instead, participants typically end up in the two groups as a function of self-selection. [Return to Figure 6.4]
Figure 6.5 image description: The image illustrates the One-Shot Case Study. In a one-shot case study, a group receives exposure to an independent variable and then is measured on a dependent variable. This design lacks a control group. [Return to Figure 6.5]
Figure 7.1 image description: The image displays the advantages and disadvantages of survey data collection methods.
- Face-to-Face advantages include high response rate, good rapport, ability to clarify questions, detailed responses.
Face-to-Face disadvantages include being expensive, time consuming, interviewer bias, not great for sensitive topics. - Mail-Out advantages include efficiency, sensitive topics, no interviewer bias, ability to include visual aids, anonymity.
Mail-Out disadvantages include slow and modest return rate, being expensive, inability to clarify questions or instructions. - Telephone advantages include modest time and cost, is computer-assisted, ability to clarify instructions and questions, likely to answer sensitive questions.
Telephone disadvantages include trust is lacking, unlisted numbers, and blocked calls. - Internet advantages include being inexpensive and efficient, software can aid data coding and analyses, detailed responses to open-ended questions.
Internet disadvantages include low response rate, not a random sample, requires internet access. [Return to Figure 7.1]
Figure 7.2 image description: The image displays a contingency question based on a previous item.
There is a question that reads: Have you ever tailgated another driver (i.e., driven close to the vehicle) because you were angry at the driver?
There are two possible selections, yes and no. If no is selected, the question is complete. If yes is selected, the following additional question is presented: If yes: Thinking about the last time you tailgated another driver, were you travelling alone, with a passenger, or with more than one passenger? [Return to Figure 7.2]
Figure 8.1 image description: The image displays the main steps for conducting a content analysis.
1) Clarify research objectives 2) Identify an appropriate archival source 3) Employ sampling procedures 4) Code the data as guided by the conceptual framework 5) Summarize and disseminate the findings. [Return to Figure 8.1]
Figure 9.1 image description: The image displays a comparison of interview structures.
Structured: Quantitative, survey, questions are prepared ahead of time, standardized, often relies on closed-ended response formats, no flexibility (that is, rewording of questions or order of questions), no clarification or follow up.
Semi-Structured: Quantitative or qualitative, survey or in-depth interview, main questions are prepared ahead of time, some flexibility (that is, questions can be clarified, additional questions can included, or earlier items can be omitted), can include follow-up/probing questions designed to learn more about a topic raised by the respondent.
Unstructured: Qualitative, in-depth interview, questions are developed based on respondent answers, highly flexible (that is, there is no pre-arranged item wording), relies on open-ended questions, questions can be clarified, modified, and even answered by the interviewer, incorporates follow-up questions that emerge as a function of responses provided. [Return to Figure 9.1]
Figure 10.1 image description: The image displays the four roles of an ethnographer during fieldwork.
Complete Observer is covert, unobtrusively observes, no interaction, brief.
Observer as Participant is overt, is there only to observe, formal interactions if they take place, succinct.
Participant as Observer is overt, joins the group to study it, establishes social interactions, limited duration.
Complete Participation is covert, is a member of the group studied, intimate interactions, extended duration (for example, 2 to 3 years). [Return to Figure 10.1]
Figure 11.1 image description: The image compares quantitative and qualitative approaches.
Quantitative: Positivist, objective, deductive, specific hypotheses, for example, experiments and surveys, it has high reliability as viewed by a quantitative perspective, it has low validity as viewed by a qualitative perspective.
Qualitative: Interpretive, subjective, inductive, broad questions, for example, interviews and participant observation, it has low reliability as viewed by a quantitative perspective, it has high validity as viewed by a qualitative perspective. [Return to Figure 11.1]
Figure 11.2 image description: The image details the process of convergent design. Both qualitative and quantitative methods are used for data collection and data analysis. Then the data is integrated, and the findings can be disseminated. [Return to Figure 11.2]
Figure 11.3 image description: The image details the process of explanatory design. A quantitative method is employed in data collection and data analysis and then the findings are followed up on. After the survey data is collected and analyzed, researchers might conduct in-depth qualitative interviews with a few respondents to better understand and explain results obtained in the earlier, prioritized quantitative phase. [Return to Figure 11.3]
Figure 11.4 image description: The image details the process of exploratory design. Exploratory design (also known as an exploratory sequential design) has a qualitative method that is prioritized. Then, based on the findings from the qualitative study, a quantitative method is developed and subsequently employed. [Return to Figure 11.4]
Figure 11.5 image description: The image offers a system’s model of an educational program for sex-trade offenders.
The model consists of four consecutive parts: 1) inputs 2) program activities 3) outputs and 4) outcomes.
- Inputs can be resources such as grants, agencies, an executive director, police officers, health professionals, community representatives, session facilitators, and community volunteers.
- Program activities can be sessions on prostitution, pimps, sexual addictions, vice laws and street facts, community impact, and health information.
- Outputs can include the following: majority of sex-trade offenders charged completed the program, prostitution viewed as a social problem, exposure to a range of perspectives on prostitution, and changed views of prostitution.
- Outcomes can include the following: meduction in the number of repeat offenders, assists prostitutes in leaving prostitution and finding alternative means of employment, and reduced demand of the program through educational efforts. [Return to Figure 11.5]
Figure 11.6 image description: The image details the steps for carrying out evaluation research.
- Engage stakeholders
- Clarify the problem
- Establish the criteria
- Assess the program
- Provide the outcome [Return to Figure 11.6]
Figure 11.7 image description: The image details the underlying logic of action research.
- What is the concern?
- What can be done about it?
- Action
- How can we tell this helped?
- What did we learn? [Return to Figure 11.7]
Figure 11.8 image description: The image details action research cycles. Action research is a continuous reflective, cyclical process that begins with observation, is followed by reflection, action, evaluation, modification, and then subsequent observation (McNiff, 2017). In this sense, action research generally involves a series of cycles as opposed to one phase of research. Going back to the earlier example, a teacher might try a learning strategy and then discover that it was only effective for certain students. In this case, another action cycle will begin. The second cycle will retain successes of the first phase and add in a new course of action to try to further improve upon the learning environment. For instructors who strive to continuously improve upon their teaching, the action cycle can continue indefinitely. [Return to Figure 11.8]
Figure 12.1 image description: The image is a sample pie chart detailing marital status.
Forty-seven percent of people are married, twenty-two percent are single, fourteen percent are common law, twelve percent are divorced, and 5 percent are separated. [Return to Figure 12.1]
Figure 12.2 image description: The image is a sample bar graph showing frequencies for treatment completion by participants based on marital status. There are one hundred and eighty-one participants.
Married group: eighty-six completed treatment, eleven failed to complete treatment
Common law group: twenty-five completed treatment, four failed to complete treatment
Single group: twenty-three completed treatment, twenty failed to complete treatment
Divorced group: eight completed treatment, six failed to complete treatment [Return to Figure 12.2]
Figure 12.3 image description: The image is a sample bar graph detailing the percentage of treatment completed by participants based on marital status.
Married group: eighty-nine percent completed treatment, eleven failed to complete treatment
Common law group: eighty-six percent completed treatment, fourteen percent failed to complete treatment
Single group: fifty-three percent completed treatment, forty-seven percent failed to complete treatment
Divorced group: fifty-seven percent completed treatment, forty-three percent failed to complete treatment [Return to Figure 12.3]
