4. Extensions (HL only) Questionbank

Source 1: Researchers surveyed 800 university students (200 from each of four cultural groups: Sweden, Japan, Brazil, and Kenya). Students rated the question “How much do you trust a close friend?” on a 10-point scale. Responses of 8–10 were categorized as indicating “high trust.”

Source 2: Researchers were interested in investigating how adults from different cultural backgrounds experience and describe interpersonal trust. They conducted semi-structured interviews with 15 participants (8 from collectivist backgrounds and 7 from individualist backgrounds). The interviews were recorded and transcribed, and the transcripts were subjected to thematic analysis by two independent researchers. The researchers first identified categories (themes) separately, then compared their coding, and finally re-coded the transcripts together.

Source 3: An undergraduate student was interested in exploring differences in reported trust levels between students from individualist and collectivist cultural backgrounds. They recruited 50 participants from their own university (aged 18–22). Participants were asked two questions: “Rate your general trust in others on a scale from 1 to 10.” “State your cultural background.” Based on self-reported backgrounds, 28 participants were categorized as individualist and 22 as collectivist. The researcher summarized the data as follows:

Source 4: In a survey of 120 adult participants, aged between 30 and 50, (40 each from the USA, Japan, and Mexico), researchers administered two standardized scales: Collectivism Scale (1–7), wherein higher scores indicate stronger collectivist orientation, and the General Trust Scale (1–10) where higher scores indicate greater general trust in others. r(118) = 0.47, p < 0.01

Source 5: Researchers tested 120 participants (60 collectivist background, 60 individualist background) in a trust game with real money. Each participant received £10 and could choose how much to share with a partner.

Condition 1: Partner described as an “in-group” member.

Condition 2: Partner described as randomly assigned.

The researchers found that collectivist participants shared significantly more with in-group members (mean = £4.80). On the other hand, individualist participants shared more with randomly assigned partners (mean = £5.10). Overall sharing did not differ significantly across groups, but it is suggested that cultural framing influenced the trigger of trust.

Source 1: Graph showing results of a study in which students from different countries were asked to complete an anxiety scale (scores converted to percentage of students showing high anxiety symptoms). All participants used social media for at least 3 hours per day over the past month.

Source 2: Researchers conducted semi-structured interviews with 12 university students who reported struggling with mental health. Interviews focused on their experiences with social media. Responses were transcribed and analysed using thematic analysis.

Source 3: A psychology undergraduate wanted to explore the relationship between social media use and depression. They surveyed 100 students from their university, all of whom they personally knew to be frequent users of Instagram or TikTok. Students self-reported their average hours of social media use per day and completed a self-rated depression scale.

Source 4: Researchers investigated the relationship between daily social media use (in hours) and self-reported loneliness (score from 1 to 100) among 30 university students. r(28) = 0.854, p = 0.007

Source 1: Researchers at an institute studying youth conducted a large-scale survey of 400 adolescents. The participants came from a variety of socioeconomic and cultural backgrounds, and were all between the ages of 15-17. Participants were assured that the data would be anonymised and not shared with anyone outside the team of researchers. Using data and findings from previous studies on adolescents and risk-taking, the researchers generated a list of activities qualified as risk-taking or unsafe. This included, but is not limited to: speeding on a bicycle, car or similar vehicle; climbing or playing in unsafe places; engaging in the consumption of alcohol or recreational drugs; engaging in violent fights or altercations; and skipping school. The researchers administered this list, asking adolescents to mark the activities they had engaged in and to what frequency within the last 6 months. The researchers also asked participants to self-report sensation-seeking motivation (measured on a 1-7 scale).

Source 2: A qualitative study sought to explore what motivated adolescents, aged 16 to 18, to take risks. The sample consisted of 20 individuals, who took part in semi-structured interviews wherein they were questioned about what encouraged them to partake in “risky” behaviour. Researchers recorded and transcribed the interviews, before analysing the transcripts thematically.

Source 3: An undergraduate student studying psychology conducted a survey, using their own classmates as the sample. They wanted to understand why older adolescents (age 18 to 19) would engage in risky behaviour and why. The final sample included 50 classmates. The student researcher asked two open-ended questions: “Why do you engage in risky behaviours (if at all)?” and “Do you see these behaviours as harmful or helpful?”. The researcher then coded responses into three categories:

Source 4: An experiment tested how motivational framing influenced risk-taking in a simulated driving task. Ninety adolescents (ages 15 to 16) were randomly assigned to one of three conditions:

Competition framing: “Try to beat others’ scores on the driving task.”

Curiosity framing: “See how far you can push your driving skills.”

Control: No framing.

The researchers then recorded the average number of risky decisions (e.g., speeding through yellow lights) made:

Source 1: A team of researchers who were interested in prosocial behaviour wanted to investigate whether intrinsic vs. extrinsic motivation influenced helping behaviour. They recruited 180 participants from an ad in the newspaper, the adults were randomly assigned to three conditions (N = 60 per group):

Intrinsic prime: Participants wrote about values that made them feel connected to others.

Extrinsic prime: Participants wrote about rewards they hoped to earn in the future.

Control: Participants began the experiment with an unrelated writing task.

The participants were not informed about the actual aim of the study, they believed the writing task was the extent of the experiment. As the participants left the room, they observed a staged event in which a confederate “accidentally” dropped a stack of books. The key measure was whether participants helped pick them up.

Source 2: Psychologists at a certain research institute were interested in exploring what motivates long-term caregiving in familial relationships. The researching team reached out to a local medical center to create a sample of adults who were either: a) parents of disabled children, or b) adult children caring for elderly children. The sample was comprised of 20 participants. Researchers carried out semi-structured interviews with each participant, recording and transcribing the interactions. The transcripts underwent thematic analysis.

Source 3: An undergraduate student studying psychology conducted a survey as part of an assignment. They wanted to examine why students help their fellow peers. The researcher chose to survey students outside their own psychology class. The final sample included 60 classmates, aged between 18-20. The student researcher administered a survey asking students the open-ended question: “Why do you help your friends?” The researcher then coded the responses into three categories of motivation:

Intrinsic motivation: Responses that suggest individuals help because they want to.

Reciprocity: Responses that suggest individuals help expecting help in return.

Emotional reward: Responses that suggest individuals help because it feels good.

Source 4: Researchers in a university psychology department were in the process of putting together a book about altruism and prosocial behaviour. As part of their publication, they included a study wherein they tested whether intrinsic motivation predicted real-world social behaviour. The sample of the study included 250 university students, spread across years and ranging from 17 to 23 years old. The researchers determined the intrinsic motivation of individuals by administering a standardised test that quantified the level of intrinsic motivation on a scale (1-7). Researchers also asked individuals to report the average number of hours they spent volunteering per month. r(248) = 0.623, p < 0.05

Source 5: Researchers wanted to conduct an experiment to test whether extrinsic social recognition influenced charitable giving. Unfortunately, the sample chosen by the researchers consisted of second-year psychology students, as these were the only individuals who had time to participate in the study. The participants were randomly assigned two one of two conditions:

Public condition: Participants told that names of donors displayed on a visible donor list.

Private condition: Participants told that donations were made anonymously.

On tablet screens, participants in each condition were asked if they would donate money to the cause, and how much (scale of £1 to £10)

Source 1: A clinic that specialised in couples therapy enlisted a team of researchers to explore predictors of relationship satisfaction in young adult couples. The researcher team was curious about the connection between the frequency of daily texting and/or instant messaging and relationship satisfaction. The participants included over 400 young adults in long-term romantic relationships, enrolled through an online ad; the age of participants varied between 18 and 30. Individuals filled out a virtual questionnaire that asked them to report the average number of messages exchanged daily with their partner, and relationship satisfaction on a 1–10 scale (scale was on a slider that allowed decimal values). Based on message frequency, participants were grouped into three categories:

Source 2: The same team of researchers mentioned in Source 1 also conducted a qualitative study on how couples experience technology in their relationships. The researchers randomly picked 15 couples from online volunteers, ages of the individuals varied from 25 to 40. Each couple participated in semi-structed interviews with a researcher. Interviews were audio-recorded, transcribed and analysed between researchers for recurring themes.

Source 3 An undergraduate student with an interest in technology and the psychology of human relationships wanted to explore whether friendships formed online would be considered as important as offline ones. The sample included 50 university students, all peers or friends, that were aged between 18 and 22. The student researcher sent the participants a short survey that asked: a) “Do you have any “close” online friends?” and b) “Are your online friendships as important as your offline ones?” The open-ended responses were coded by the student researcher into categories like “Yes” and “No”. The researcher discovered the following: 62% of the sample reported having at least one close online friend, and of those, 48% said their online friendships were as important as offline ones.

Source 4 A large-scale correlational study was conducted with the intention of examining whether time spent on social media was related to loneliness. The study consisted of 600 individuals, spanning between 16 and 30; the sample consisted of individuals who frequented the campus wherein this study took place. Participants self-reported the daily hours spent on social media, and also completed the UCLA Loneliness Scale (higher scores = greater loneliness, scale of 20-80). r= 0.598, p < 0.05

Source 5 An experiment investigated how the presence of a mobile phone affected interpersonal conversation. The sample consisted of 120 adults (over the age of 18) who were strangers to each other and paired randomly. Each pair engaged in 10 minutes of conversation before being asked to rate the quality of the conversation on a scale of 1-10. Pairs were randomly assigned to one of two conditions:

Phone present: Participants were allowed to have phones on the table, and look at them at any time if they wanted to.

No phone: No phones were present.

Researchers found that in the “phone present” condition, the mean rating of conversations was 6.1. In the “no phone” condition, the mean rating was 7.8.

Source 1: A research team interested in looking at dieting behaviour in young people conducted a survey of nearly 450 university students, all aged between 18 and 24. The students came from the USA, Japan, and France. The survey was simple, participants simply had to indicate how frequently they engaged in dieting behaviour on a Likert scale; lower scores indicated less dieting behaviour. In the table below, “mean dieting frequency” refers to the number of new diets begun in the last 5 years; “% Reporting Restrictive Dieting in Past Year” refers to the number of individuals self-reporting a calorie consumption below the recommended daily intake, at least several days a month:

Source 2: The research team from Source 1 decided that they would like to do some qualitative research to gain further insight into the findings. They chose 15 volunteers, 5 from each of the countries referenced in Source 1. The researchers then conducted focus groups with each of the three groups. During the focus groups, researchers gave all volunteers an opportunity to share their thoughts. The individuals were asked questions like, “Why do you diet?”, “How long have you been dieting?”, among others. Each focus group had two researchers who took note of what participants said. The researcher teams then came up with short conclusions that summarise the main reasons for dieting in each country group:

Source 3: A correlational study of 200 adolescents (ages 15–18) measured cultural orientation (1–7, higher = more collectivist attitudes) and attitudes toward restrictive dieting (1–10, higher = stronger approval of dieting). r(198) = 0.54, p < 0.01

Source 4: An experiment tested whether cultural framing influenced food choice. 120 female university students were randomly assigned to one of two conditions: Individualist frame: “Choosing healthy foods helps you achieve your personal health goals.” Collectivist frame: “Choosing healthy foods helps you stay lean; it’s important for you friends and family that you are in-shape and healthy. Participants were then asked to select lunch items, choosing between a low-calorie option and a higher-calorie option.

Source 1: Researchers at an institute for child development were interested in investigating the relationship between parenting style and academic outcomes. In particular, the researchers were curious whether this would differ across cultural contexts. The sample consisted of 600 adolescents, between the ages of 15 and 17, who attended the local high school. The researchers had already pre-determined whether the children came from collectivist (N = 130) or individualist backgrounds (N = 170) before conducting the survey. The adolescents self-reported the parenting style of their guardians, classifying it as authoritarian, authoritative, or permissive. The academic performance of each participant was sourced from the school, the performance was measured using Grade Point Average (GPA) on a 100 point scale.

Source 2: A qualitative study explored the values and goals parents hold for their children’s development. The sample consisted of the same adolescents as in Source 1, but in this study, their parents were interviewed. The sample consisted of 20 parents, 10 from a collectivist background and 10 from an individualist background. The parents took part in semi-structured interviews about their aspirations for their children. Researchers recorded and transcribed the interviews, and analysed the transcripts thematically.

Source 3: An undergraduate conducted a small-scale survey project on childhood discipline. The sample consisted of 60 university classmates, aged between 18-22. The self-reported survey was simple, and included the following items, among others: a) “How often were you disciplined physically?” (scale 1–5) and b) “How strict were your parents?” (scale 1–10). The student grouped responses into categories of “strict” and “lenient” parenting. Within these groups, the researcher then examined the participants’ self-reported GPAs (grade point averages). The student researcher concluded, based on their analysis, that strict parenting led to academic success.

Source 4: A correlational study examined whether collectivist values were associated with a stronger sense of filial responsibility in adolescents. The sample consisted of 250 adolescent participants, between ages 14-18, who originated from a variety of diverse cultural backgrounds. As part of the study, participants completed two researcher-created, standardised tests: a) Collectivism scale, measured on a scale of 1-7, b) Filial Responsibility scale, measured on a scale of 1-10. Higher scores on the collectivism scale suggested strong collectivist background, higher scores on the filial responsibility scale suggested a greater duty to one’s family. r= 0.672, p < 0.01

Source 5: Researchers at an institute for research on education conducted an experiment to investigate whether cultural framing of achievement influenced persistence in children. The researchers’ sample consisted of 120 children, aged 13-15, who had volunteered to participate in the study with their parents’ permission. The sample was randomly assigned to one of two conditions:

Group-framed task: Puzzle framed as “helping your class succeed.”

Individual-framed task: Puzzle framed as “doing your best.”

The researchers then measured the time, in minutes, by children in either condition persisting on a difficult puzzle.

Source 1: A study investigated the relationship between different types of motivation and academic performance. The participants included over 200 secondary school students, all aged between 15 and 16. Each student was given a standardized motivation questionnaire and a standardized mathematics test (0–100 scale) to complete. Based on questionnaire scores, students were categorized into three groups:

Source 2: Researchers conducted semi-structured interviews with 20 students at an international school to gain information on what motivated students to study. Of these 20 students, 10 had been identified as teachers as “high-achieving” in academics. The other 10 had been identified as “struggling students.” Researchers audio-recorded and transcribed the interviews. They use thematic analysis to identify recurring themes in how students described their motivation.

Source 3: A student in a secondary school psychology class was interested in carrying out a study to research the connection between engagement in class and performance on assessments. The student observed their own psychology class (N = 25, students aged 17 to 18) over the course of one semester. Every week, all the students would complete weekly quizzes covering content taught during the class; the student collected scores from their peers. In addition, the student researcher maintained a classroom diary which they used to record student behaviours like asking questions or participating in discussions. Upon analysis of the observations and comparisons with quiz scores, the student discovered that, in general, those individuals who asked more questions and engaged more often with lectures and discussions consistently scored higher on the weekly quizzes.

Source 4: A group of researchers were curious as to whether a student’s mindset (beliefs about intelligence) was associated with academic achievement. The sample included 250 secondary school students. Each student completed a standardized growth mindset scale (1–10, where higher scores indicated more of a growth mindset), and the student’s end-of-year Grade Point Average (0-4.0, where a higher value indicated better academic performance) was recorded from school records. r(248) = 0.68, p < 0.001

Source 5: Researchers at a university were interested in conducting an experiment to test how motivational framing influenced persistence on a challenging task. The sample was made up of 120 university students who signed up to take part in the study; researchers paid students $5 to participate. The participants were randomly assigned to three groups (N = 40 per group). Researchers gave the students an unsolvable puzzle task. Each group was told different things beforehand:

Reward framing: “Top scores get prizes.”

Mastery framing: “Focus on learning and improving your skills.”

Control: No motivational statement.

The researchers then timed the duration, in minutes, that students spent on the puzzle before giving up.