Master thesis:

Master thesis:

Table of content

Appendix 1: Survey experiment ... 20

2. SPSS Output replication part ... 33

3. SPSS Output extension part ... 35

Abstract

Introduction

Obesity is a growing problem in the world (Flegal, Carroll, Kit & Ogden, 2012) and it is a risk factor for many negative health outcomes including heart disease, type 2 diabetes,

hypertension, liver disease, cancer, and stroke (Maner, Dittmann, Meltzer & McNulty, 2017). One of the predictors of obesity for adults are the childhood social economic status (SES) and the life history strategy (LHS) (Maner et al, 2017; Laran & Salerno, 2013). LHS can be distinguished in fast and slow strategies where fast LHS is linked to impulsivity and instant gratification so logically more prone for circumstances. Slow LHS is linked with more conscious deliberate decision making and long-term focus (Griskevicius et al. 2011a; Mittal and Griskevicius 2016; Mittal, Griskevicius, Simpson, Sung & Young, 2015). Moreover, research showed that daily hassles further foster the amount of food someone with fast LHS consumes (Hill, Prokosch, DelPriore, Griskevicius & Kramer, 2016; Fennis, Gineikiene, Barauskaite, Koningsbruggen, 2020). While the relevance of LHS for consumer behavior is clear, less is known about the underlying process responsible for the food-related decision making of fast LHS consumers. This is important because consumers make over 200 food-related decisions per week (Wansink & Chandon, 2012) and most if not all, are made on routine, intuitive or impulse. Therefore, this study aims to replicate the first study of Fennis et al. (2020) to contribute to the gap he filled. Although Fennis et al. (2020) provided a new perspective on LHS, follow-up research is needed to further expand the insights on the relationship between food-related decision making, daily encountered stress, and as a result preventing consumers of eating too much hedonic products. Formally we propose:

Furthermore, we propose that that by providing information about the healthiness of the product, consumers who grew up in low-SES environments and face a mild stressor will be more likely to be influenced by the information and thus will seek for more utilitarian

products (Chocolate, ice cream, chips, potatoes, Yogurt). Knowing how to prevent consumers with fast LHS from making mild stressor influenced food-related decisions will have a big impact on their health and lifestyle. In short, this paper has 2 objectives, replicating the study of Fennis et al. (2020) and extending it with investigating the following hypothesis:

H2: Information about the healthiness of the product positively moderates the behavior of consumers with fast LHS on the tendency for hedonic consumption, as indicated by the willingness to pay for hedonic products.

Up until now, previous research towards encountered stress and adversity focused on the influence of relatively ‘dramatic’, atypical, and impactful sources of stress and adversity. Sources used in research were unemployment lines, economic crisis or the salience of violence and death in one’s life and environment (Griskevicius et al. 2011a; Griskevicius et al. 2011b; Mittal and Griskevicius 2014). However, it is unclear to what extent this ‘dramatic’ sources are representative for the stress encountered during food-related decision making. Therefore, Fennis et al. (2020) focused on the influence of the mild stress factor on food-related decision making as it seems that the mild stressor is a more representative predictor for consumers with fast LHS.

be influenced by the more mild stressor during food-related decision making than consumers with so-called slow LHS (Hammen et al. 2000; Harkness, Bruce, and Lumley 2006; Monroe and Harkness 2005; Slavich, Monroe, and Gotlib 2011).

This all means that consumers which encountered stress and adversity during their childhood also have a higher sensitivity to imminent stressors. The result is that these fast LHS

consumers are more vulnerable and faster triggered by mild stressors. Consequently, these consumers are more likely to devaluate the future and instead promote short-term

opportunism to take advantage of immediate benefits (Mittal et al, 2015). Based on this, we can assume fast LHS consumers have a lower sense of control (Fishbach & Shah, 2006; Fennis, 2017) and are more likely to be influenced when making food-related decisions in store.

Past research already tried to manipulate the decision making of consumers following fast LHS (Mittal et al, 2016) by providing information about the likelihood of getting the disease or other consequences when eating the product. The result was that there was a shift from risk propensity towards risk perception and choices were made more deliberate, even for

Another reason we assume that consumers with fast LHS will use the information is because consumers with fast LHS are more pessimistic about their own future (Harris 1996; McKenna 1993; Weinstein 1980) and believe that they have a higher likelihood of getting a disease, i.e. lower self-control about circumstances (Larsen & Shepperd 2001; Klein & Helweg-Larsen 2002). Therefore, we propose that when consumers are in the grocery store and see the traffic-light labels, they will become more aware of the fact that they increase the likelihood of eating too much calories when a product has a red label. Consequently, they are willing to shift towards a better alternative. Moreover, we predict that the tendency of spending more grocery-budget on healthy products is higher for fast-LHS consumers in the mild-stress condition. This because the experienced stress further increases the risk perception of eating too much calories and thus will lead to a higher willingness to pay for utilitarian products instead of hedonic products.

Graphically this means,

H1: Acutely experienced mild stress boosts the tendency for hedonic consumption, as indicated by the willingness to pay for hedonic food products, particularly among consumers with a fast, rather than slow LHS

Method section

This research, including the extension, test our notion that acutely experienced stress due to a mild stressor may boost the tendency for hedonic consumption, as indicated by the

willingness to pay for hedonic food products, particularly among consumers with fast, rather than slow LHS. The experiment aims to replicate the findings by Fennis et al. (2020) and come up with a solution to prevent consumers with fast, rather than slow LHS, from hedonic consumption.

Participants

This online study (Appendix 1) used stress as a between subjects’ factor and individual

differences in LHS as a continuous measured independent variable. We tested whether acutely experienced stress boosts the tendency for hedonic consumption, particularly for consumers with fast LHS, and if the presence of traffic light labels could diminish the tendency for hedonic consumption. The participants will be relatives and connections on Facebook. The final sample consisted of 103 participants (after deleting 79 observations for multiple missing datapoints) of various ages (M=33.63, SD=11.93, 65.7% female).

Design and procedure

The 2 (stress vs. no stress) by 2 (color-coded traffic light labeled products vs non color-coded traffic light labeled products) is used as a between subjects’ factor as part of a larger project using LHS as a continuous measured independent predictor.

imagine a supermarket visit and where given the option to spend as much or as little from their grocery budget on a range of both utilitarian (e.g. milk, oat flakes, detergent) and hedonic (e.g. chocolate, cookies) food and non-food products. For the extension part, participants were also asked to imagine a supermarket visit but half of them saw products including a color-coded traffic light label varying from green (utilitarian), orange (neutral) and red (hedonic). After the supermarket task, respondents were asked to give their opinion about 20 statements to measure individual differences in LHS ((7-point scale, strongly disagree – strongly agree). Finally, the survey closed with asking how difficult, stressful, easy, fun, demanding and enjoyable the mathematical reasoning test with the question: “Please use the scale (7-point scale, Strongly disagree – strongly agree) below to tell us how you experienced the game” and ended with asking the demographics (gender, age, nationality) of the respondent.

Independent variable

For this research, three independent variables are used. Namely the mathematical reasoning task, the introduction of color-coded traffic lights and the 20 statements to measure individual differences in LHS.

The mathematical reasoning test is adapted from the Montreal Imaging Stress Task (Acar-Burkay, 2017) and had the aim to create acute mild stress among half of the participants. To vary the level of experienced stress, the test had to be solved under time pressure (10 sec. per problem) or without time pressure for the no stress condition.

orange as neutral and red as unhealthy. The labels were manipulated among respondents to calculate the influence of the color-coded traffic light labels on the tendency for hedonic consumption. The use of color-coded traffic lights has been shown to manipulate the

preferences of consumers for hedonic consumption (Grankvist, Dahlstrand & Briel, 2004; van Dam & de Jonge, 2015) and demonstrated that having to choose between products which have a color-coded traffic light label increase the willingness to pay for utilitarian products and decrease experienced stress.

Thirdly, LHS was used a continuous measured independent predictor. We measured the individual differences of participants in LHS using the 20-item Mini-K, short form

(Figueredo, Wolf, Olderbak, Gladden, Fernandes & Wenner, 2014), and averaged the scores to arrive at an overall LHS index with lower scores indicating a faster LHS (and higher scores indicating a slower LHS; M = 4.58, SD = 1.46; Cronbach’s alpha = 0.914).

Dependent variable

The most important dependent variable of this study for both replication and extension part is participants’ tendency to buy hedonic products. For the replication part, the tendency to buy hedonic products will be measured by calculating the proportion spent, of their total amount spent, on the hedonic food products. For the extension part, the effect of color-coded traffic light labeled products on participants’ tendency to buy hedonic products will be measured following the same procedure (M = 2.19, SD = 2.08; Cronbach’s alpha = 0.886). Afterwards, this score will be compared to the proportion spent on hedonic food products by the non-color-coded traffic light products design.

The second DV, participants’ acutely mild stress level, was measured as a function of the task using a 7-point scale asking how stressed the task made them feel with higher scores

Analysis plan

For the replication of study 1 from the paper by Fennis et al. (2020) we will follow the

original procedure and hence we will use Process model 14. For the extension, we have added the color-coded traffic light labels as moderator and hence we will use Process model 3.

Results

To check whether we can accept our hypothesis, we first have a look at the manipulation checks. It is important to know if the manipulations, creating acutely experienced stress due to a mild stressor and influencing the decision making of consumers by introducing color-coded traffic light labels, worked so it is clear whether they are responsible for the effect found.

Manipulation check

The first manipulation was done during the mathematical reasoning test (Acar-Burkay, 2017). 13 mathematical questions had to be filled in with or without time pressure (10 sec. per problem) with the aim to vary acutely experienced mild stress among participants. After recoding 3 of the 6 responses (easy, fun, enjoyable) on the acutely experienced mild stressor questions, which participants had to fill in after the mathematical reasoning test, we ran an ANOVA. With 95% confidence interval, we can conclude that there is a significant difference in the scores for the no stress (M=3.77, SD=1.07) and the stress (M=4.48, SD=0.86)

conditions on the acutely experienced stress measure task.; F(1, 103)=12,72, p = 0.001. This means that the manipulation for creating acute stress due to a mild stressor worked. The second manipulation, introducing color-coded traffic light labels, had the aim to influence the decision making of consumers and decrease the proportion spent on hedonic products. After running an ANOVA, we can conclude, with 95% confidence interval, that there is a

label (M=15,20, SD=8,25) and the no color-coded traffic light label (M=19,07, SD=7,74) conditions; F(1, 102)=6,03, p=0.016. This means that the introduction of color-coded traffic light labels successfully manipulated the decision making of consumers.

Target analysis: Replication

We used PROCESS, model 14 to replicate the findings from the paper by Fennis et al. (2020) and investigated whether we can confirm the findings. Namely, if task induced stress

promotes the tendency to consume hedonic products. Results of our test (Appendix 2)

indicated that there is no significant interaction between experienced levels of stress and LHS (B=.3024, t(102) = .2404, p = .8105). Moreover, the results do not provide support for the moderated mediation suggested by Fennis et al. (2020) because the confidence interval with 5,000 bootstraps did include 0 (95% CI [-2.19; 2.80]). Hence, the results do not have support for our first hypothesis. Moreover, all other effects and relationships were not significant (appendix 2).

Target analysis: Extension

lights. When looking at other relationships, we found the following results. The main effect of stress on hedonic consumption is significant (B = 25.30, t(102) = 2.05, p = .0436). Moreover, interaction 1 (Stress x LHS) on hedonic consumption was almost significant (B = 0.2861,

t(102) = 0.1833, p = .0780). Thirdly, interaction 2 (Stress x Color-coded traffic light label) (B

= -29.3071, t(102) = -1.7112, p = .0903) and interaction 4 (Stress x LHS x Color-coded traffic light label) (B = 5.9898, t(102) = 1.6552, p = .1012) on hedonic consumption were also almost significant. Finally, two results were not significant, namely the effect of LHS (p = 0.8549) and color-coded traffic light label (p = 0.5151) on hedonic consumption.

Discussion

Previous research has shown that someone’s LHS can be used as predictor for their willingness to pay for hedonic products when influenced by an acutely experienced mild stressor. To test this notion, we replicated the study of Fennis et al. (2020) and investigated whether and to what extent the LHS is an important predictor for the tendency for hedonic consumption. Moreover, we tested how the expected tendency for hedonic consumption after experiencing an acutely mild stressor can be diminished. Both replication and extension were tested in a full-scale experiment conducted online using a 2 by 2 design.

The results, consisting of both the replication and the extension part, yielded no support for the hypothesis that LHS can be used as a predictor for the willingness to pay for hedonic products when influenced by an acutely experienced mild stressor. Moreover, we did not find support for the notion that this willingness to pay for hedonic products can be diminished with using color-coded traffic light labels. Nevertheless, we found support that color-coded traffic light labels influenced the tendency to consume hedonic products but that there is no

between stress and LHS. The other effects and relationships of the extension part showed that, in line with the theory, stress positively influences hedonic consumption and when color-coded traffic lights are introduced, this effect will almost significantly diminish.

Contribution and implication

With this research we aimed to expand the insights on the relationship between food-related decision making, daily encountered stress, and as a result preventing consumers of eating too much hedonic products. The understanding of underlying relationships between the three aspects provide new insights for both governments and companies in the field of healthiness and related decision making. This is important because consumers make over 200 food-related decisions per week (Wansink et al, 2012) and most if not all, are made on routine, intuitive or impulse. Our research pointed out that, although not all results were fully

supported, the tendency for hedonic consumption because of an acutely mild stressor can be diminished with the introduction of color-coded traffic light labels. This insight will have scientific and practical implications.

Namely, with taking the not fully significant results into account, we still advance the understanding of the relationship between acutely experienced stress and the tendency of hedonic consumption. Specifically, while we haven’t found direct support for the findings of Fennis et al. (2020), we showed with our extension part that no dramatic life events (showing mortality rates or information about obesity e.g. Mittal et al, 2016) are needed to shift from hedonic consumption towards more utilitarian consumption and thus from risk propensity to risk perception (Hogarth et al, 1995; Loewenstein et al, 2001). Moreover, we almost

considered. In short, this study contribute to the research of Fennis et al. (2020) by confirming some of his findings and most important, extending his work by showing ways how the effects he found could be diminished and how consumers could be ‘nudged’ away from hedonic consumption.

A more practical implication, governments could insist the introduction of color-coded traffic light labels in grocery stores and other places where consumers experience a lot of acutely mild stress. This will very likely influence the tendency to buy hedonic products for a lot of consumers and in turn will make them healthier. Important is that this is put forward by the government and that it should not be determined by every store itself. This because grocery stores could use different criteria or use different ways to ‘nudge’ the consumer towards a healthier option. In the end, the introduction of color-coded traffic light labels will lead to more healthy consumers, less obesity among people and lower risk of fatal diseases.

Secondly, companies like grocery stores, cafeteria and other food selling stores could benefit from the promotion of healthier products. Due to the fact that this research showed that the introduction of color-coded traffic light labels decrease the tendency for hedonic

consumption, food selling stores could introduce this color-coded traffic light labels in times that fruit is just before its best-before date. The result is that relatively more fruit is sold and therefore less spoiled food must be thrown away.

Limitations

more direct and added value. Further, the original study consisted of 182 respondents but 79 cases had to be deleted because of incomplete responses. This potentially influenced the results because it is likely to assume that one ‘type’ of people did not complete the survey. With having their responses incorporated, it might be that other insights or results will have come forward than it is the case now.

Future Research

As shown in this research and discussed in both the introduction and the theoretical framework, the life history theory has a great potential for studying and explaining food related decisions, made by consumers. Therefore, future research might focus on replicating this study with a more complete dataset and see if it finds similar significant behavioral effects on willingness to pay for hedonic products when being under mild stress. Important is that respondents are informed that they might receive a reward after participation because the completion rate of this study was only 57%. When there might be a reward, the participants are more likely to complete the survey. Future research could also test whether the

introduction of color-coded traffic light labels decrease or overrule the amount of experienced stress among participants. This will give even more insight in the underlying process

responsible for the food-related decision making of fast LHS consumers. With knowing this, different intervention strategies could be tested and afterwards introduced in the real world.

In conclusion, this research shows that experiences during your childhood effect your day-to-day decision making and whether you buy more hedonic or utilitarian products when doing grocery shopping. However, and luckily for consumers, there are possible strategies

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Appendix 1: Survey experiment

Exhibit 1. Introduction/ Cover story

Dear participant,

Welcome to a short survey (5-10 minutes) composed of two parts.

My name is Chris Willems and I am master student at the University of Groningen. In order to fulfill my master’s degree, I am interested in two different topics: Human Cognition and Spending

Behavior.

• The first study is about the consequences of quantitative reasoning, which includes a mathematical test.

• The second study is about purchase behavior, in which a specific scenario will be given.

Your participation in this study will remain confidential and there will be no attempt to link your responses and your identity. Also, your participation in this study is entirely voluntary, and you may withdraw at any time by closing the survey platform.

If you have questions about this research, you can send an email message to j.c.willems@student.rug.nl

Exhibit 2. Start quantitative reasoning test

Welcome to the first part of the study! This part is about quantitative reasoning and includes a short math test.

Exhibit 3a. High Stress Condition

The Mental Game

This intelligence test serves as an important indicator of the quantitative mind of a person.

You will now be presented with a set of quantitative reasoning questions based on arithmetic tasks such as addition (+), subtraction (-), multiplication (*), and division (/).

There is one correct answer for each question. The correct answer is a number between 0 and 9. You will have 10 seconds to answer each question.

We are interested in the response that you can arrive at through mental calculation alone. As such, please complete these without the use of a pencil and paper or a calculator.

At the end of the game, you will be given feedback about how you performed compared to other participants.

Exhibit 3b. Low Stress Condition

The Mental Game

This intelligence test serves as an important indicator of the quantitative mind of a person.

You will now be presented with a set of quantitative reasoning questions based on arithmetic tasks such as addition (+), subtraction (-), multiplication (*), and division (/).

There is one correct answer for each question. The correct answer is a number between 0 and 9.

I am interested in the response that you can arrive at through mental calculation alone. As such, please complete these without the use of a pencil and paper or a calculator.

For each question, select the option that you think is the correct answer.

Q1. EXAMPLE: 4 - 1 + 2 = ?  0  1  2  3  4  5  6  7  8  9

Q2. Here is another EXAMPLE 7 + 2 * 1 = ?

 0  1  2  3  4  5  6  7  8 9 Please continue when you are ready to start!

This is the end of the Mental Game Thank you!

HIGH STRESS CONDITION: Your performance was 10% worse compared to the average

of other participants' performance.

LOW STRESS CONDITION: Your performance was similar compared to the other

participants' performance.

Exhibit 4. Second part study

Welcome to the second part of this study.

Imagine you are visiting your local grocery store and you have the option to spend as little or as much of your budget on the next products. Please indicate how much you want to spend on

each product.

o CONTINUE

Exhibit 5b. Willingness to buy Hedonic products – Study 1 condition 1

In your visit to the supermarket you encounter different product on the shelves.

Please indicate for each of the following products what you would be willing to spend on each of them. The colors of the product indicate the healthiness of the product where green is healthy, yellow

neutral and red is unhealthy.

Biscuits Blueberries Apples Eggs Toilet paper

Chocolate Potato chips

Onions Crackers Broccoli Sweets

€ € € € € € € € € € €

Lemon Cookies Nuts Chewing gum

Oat flakes

Salad Ice cream Ginger Chips Avocado

Bread Milk Lemon Radish Detergent Potatoes Yogurt Cereals

€ € € € € € € €

Exhibit 5c. Willingness to buy Hedonic products – Study 1 condition 2

In your visit to the supermarket you encounter different product on the shelves.

Please indicate for each of the following products what you would be willing to spend on each of them.

Biscuits Blueberries Apples Eggs Toilet paper

Chocolate Potato chips

Onions Crackers Broccoli Sweets

€ € € € € € € € € € €

Lemon Cookies Nuts Chewing gum

Oat flakes

Salad Ice cream Ginger Chips Avocado

€ € € € € € € € € €

Bread Milk Lemon Radish Detergent Potatoes Yogurt Cereals

Exhibit 6. Manipulation Check/ Mood measure (Acar-Burkay, et al. 2014)

Back to the Mental Game. Please use the scale below to tell us how you experienced the game

Strongly disagree Disagree Somewhat disagree Neither agree nor disagree Somewhat agree Agree Strongly agree DIFFICULT        STRESSFUL        EASY        FUN        DEMANDING        ENJOYABLE       

Exhibit 7. LHS: Mini-short K-form (Figueredo, 2004)

Please indicate how strongly you agree or disagree with the following statements. Use the scale below.

Exhibit 8. Socioeconomic status (Griskevicius et al. 2011)

Exhibit 9. Demographic questions.

You are at the end of the questionnaire. Please finalize answering some demographic questions.

What is your gender? Male Female Other

What is your age?

Currently I am….

 Studying  Working

 Looking for a job  Not applicable

What is your nationality?

 Dutch

Exhibit 10. Debriefing section

THANK YOU!

This is the end of the questionnaire.

Thank you for taking your time to complete all questions.

If you are interested in the results of the study, please fill in your e-mail address. Your information will be kept confidential and will only be used to keep you updated about

the results of the research.

E-mail Address:………

2. SPSS Output replication part

Run MATRIX procedure:

***************** PROCESS Procedure for SPSS Version 3.5 ***************** Written by Andrew F. Hayes, Ph.D. www.afhayes.com

Documentation available in Hayes (2018). www.guilford.com/p/hayes3 ************************************************************************** Model : 14 Y : Only_red X : Stress M : StressMe W : LHS Sample Size: 103 ************************************************************************** OUTCOME VARIABLE: StressMe Model Summary R R-sq MSE F df1 df2 p ,3219 ,1036 ,9844 11,6750 1,0000 101,0000 ,0009 Model

coeff se t p LLCI ULCI constant 3,7688 ,1260 29,9102 ,0000 3,5189 4,0188 Stress ,6824 ,1997 3,4169 ,0009 ,2862 1,0786 ************************************************************************** OUTCOME VARIABLE: Only_red Model Summary R R-sq MSE F df1 df2 p ,1845 ,0340 67,4704 ,8632 4,0000 98,0000 ,4889 Model

coeff se t p LLCI ULCI constant 27,5546 26,2874 1,0482 ,2971 -24,6121 79,7213 Stress 2,1577 1,7529 1,2309 ,2213 -1,3209 5,6363 StressMe -2,8083 6,0248 -,4661 ,6422 -14,7644 9,1479 LHS -1,2020 5,4510 -,2205 ,8259 -12,0192 9,6153 Int_1 ,3024 1,2578 ,2404 ,8105 -2,1936 2,7985 Product terms key:

Int_1 : StressMe x LHS

Test(s) of highest order unconditional interaction(s): R2-chng F df1 df2 p M*W ,0006 ,0578 1,0000 98,0000 ,8105

Direct effect of X on Y

Effect se t p LLCI ULCI 2,1577 1,7529 1,2309 ,2213 -1,3209 5,6363 Conditional indirect effects of X on Y:

INDIRECT EFFECT:

Stress -> StressMe -> Only_red

LHS Effect BootSE BootLLCI BootULCI 3,5000 -1,1941 1,8591 -4,4786 3,2006 4,7000 -,9464 ,7060 -2,3734 ,4527 5,5680 -,7673 ,9825 -2,9969 ,8586 Index of moderated mediation:

Index BootSE BootLLCI BootULCI LHS ,2064 1,2049 -2,6435 2,2691 ---

*********************** ANALYSIS NOTES AND ERRORS ************************ Level of confidence for all confidence intervals in output:

95,0000

Number of bootstrap samples for percentile bootstrap confidence intervals: 5000

3. SPSS Output extension part

Run MATRIX procedure:

***************** PROCESS Procedure for SPSS Version 3.5 ***************** Written by Andrew F. Hayes, Ph.D. www.afhayes.com

Documentation available in Hayes (2018). www.guilford.com/p/hayes3 ************************************************************************** Model : 3 Y : Only_red X : Stress W : LHS Z : Label Sample Size: 103 ************************************************************************** OUTCOME VARIABLE: Only_red Model Summary R R-sq MSE F df1 df2 p ,3739 ,1398 61,9819 2,2052 7,0000 95,0000 ,0404 Model

coeff se t p LLCI ULCI constant 16,8140 7,1041 2,3668 ,0200 2,7107 30,9174 Stress 25,3007 12,3725 2,0449 ,0436 ,7382 49,8632 LHS ,2861 1,5607 ,1833 ,8549 -2,8122 3,3844 Int_1 -4,6530 2,6113 -1,7819 ,0780 -9,8371 ,5310 Label -7,6831 11,7590 -,6534 ,5151 -31,0278 15,6616 Int_2 -29,3071 17,1263 -1,7112 ,0903 -63,3071 4,6929 Int_3 ,8381 2,4620 ,3404 ,7343 -4,0496 5,7259 Int_4 5,9898 3,6189 1,6552 ,1012 -1,1945 13,1742 Product terms key:

Int_1 : Stress x LHS Int_2 : Stress x Label Int_3 : LHS x Label

Int_4 : Stress x LHS x Label Test(s) of highest order unconditional interaction(s):

R2-chng F df1 df2 p X*W*Z ,0248 2,7396 1,0000 95,0000 ,1012

*********************** ANALYSIS NOTES AND ERRORS ************************ Level of confidence for all confidence intervals in output:

95,0000