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Hungarian Validation of Zimbardo Time
Perspective Inventory
Gábor OROSZ University of Szeged,
Institute of Psychology 07/10/2011
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First of all, the question arises:
„Why is time perspective an interesting topic?”
http://www.youtube.com/watch?v=A3oIiH7BLmg
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Validity and reliability of translated questionnaires
• TP is an interesting topic – but how to measure it? • Criteria of questionnaire validation:
– Internal consistency – Cronbach’s alphas and test-retest reliability (several days –> years)
– Construct validity – firstly: Exploratory Factor Analysis (EFA); later: Confirmatory factor analysis (CFA) (same vs. different samples)
– Convergent validity: correlation with other variables
– Discriminant validity: Sensation seeking vs. Present hedonism
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Criteria of validity and reliability
• EFA criteria: minimal loading .32 and no cross-loading above .32 (Tabachnik & Fidell, 2001)
• Cronbach’s alpha at least .7 but .8 is better (Nunnally, 1978) (however, on the basis on my experiences .9 or above alpha measures a very narrow psychological construct)
• CFA criteria: RMSEA ≤ .06, CFI ≥ .95, TLI ≥ .95 Hu & Bentler (1999)
• Inter-item correlation: between .15 and .50 (Clark & Watson, 1995)
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Some trade-offs of validation 1. Cronbach’s alphas ↑ with the number of
items and ↓ with the number of factors 2. Generally, EFA factor structure becomes
clearer by dropping items 3. CFA is very sensitive to cross-loadings*
and lower factor loadings (↓ than .5) → only a few items meet this criteria
4. CFA is sensitive to the high N of factors → higher possibility of cross-loadings*
* Inappropriate covariances
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The original questionnaire: ZTPI (Zimbardo & Boyd, 1999, JPSP)
Sample: NEFA = 606, NCFA = 361, Age = 19.9 Nitems = 56 Total explained variance = 36%
EFA & α PAST - PAST + PRES HEDON
PRES FATAL
FUTURE
N° OF ITEMS
9 10 15 9 13
EXP VAR. 4,5% 12,3% 8,9% 3,9% 6,3%
Cr α .80 .82 .79 .74 .77
CFA: χ2/df = 2.3 RMSEA = ?, CFI = ?, TLI = ?
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ZTPI validations in different cultures I.
Nation Sample Exp. var.
EFA and reliability
Past pos.
Past neg
Pres hed
Pres fat
Fut. χ2/df RMSEA
CFI TLI Authors
USA N = 606 Age = 19.9
N = 56 36%
N of items: Exp. var.: Alpha:
9 4.5% .80
10 12.3% .82
15 8.9% .79
9 3.9% .74
13 6.3% .77
2.3 ? ? ? Zimbardo & Boyd (1999)
Italy N = 1507 Age = 34.7
N = 21 31%
N of items:
Exp. Var.: Alpha:
― ― 8
8.4%
.54
5 7.3%
.49
9 15.1%
.67 ? ? ? ? D’Alessio et al.
(2003)
France N = 419 Age = 21.5
N = 20 33%
N of items: Exp. var.: Alpha:
8 4.4% .70
9 8.1% .72
18 10.5% .79
7 3.7% .70
12 6.1% .74
2.04 .055 ? ? Apostolidis & Fieulaine (2004)
Mexico N = 300 Age = 31.8
N = 56 ―
N of items: Exp. var.: Alpha:
20 ?
.77
25 ?
.80
11 ?
.75 ? ? ? ? Corral-Verdugo
et al. (2006)
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ZTPI validations in different cultures II.
Nation Sample Exp. var.
EFA and reliability
Past pos.
Past neg
Pres hed
Pres fat
Fut χ2/df RMSEA
CFI TLI Authors
Spain N = 756 Age = 40,1
N = 56 34%
N of items: Exp. var.: Alpha:
8 4.4% .70
14 11.2% .80
14 7.7% .79
9 4.0% .64
11 6.5% .74
? ? ? ? Diaz-Morales (2006)
Brazil N = 247 Age = 22.5
N = 38 31%
N of items:
Exp. Var.: Alpha:
6 ?
.60
7 ?
.60
9 ?
.55
6 ?
.46
10 ?
.67 ? ? ? ? Milfont et al.
(2008)
Lith. N = 1244 Age = 30,9
N = 56 33%
N of items: Exp. var.: Alpha:
8 3.3%
.70
10 7.7%
.72
14 7.3%
.79
10 4.4%
.70
13 12%
.74 2.22 .044 .67 .65 Liniauskaite &
Kairys (2009)
Taiwan N = 420 Age = 20,5
N = 20
N of items: Exp. var.: Alpha:
4 ?
.68
4 ?
.76
4 ?
.68
4 ?
.49
4 ?
.68 2.01 .05 .93 - Gao (2011)
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Hungarian validation of ZTPI I.
• Main goals: 1. Achieving an appropriate EFA factor
structure (e.i. the rule of .32) 2. Achieving appropriate internal consistency in
terms of higher alphas than .7 3. Achieving appropriate CFA (RMSEA, CFI, TLI)
• Not goals (yet) 1. Test-retest reliability 2. Convergent validity & discriminant validity
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Hungarian validation of ZTPI II. • Translation:
– Five persons translated independently the original ZTPI to Hungarian (3 psychology MA students who knew the topic and have at least advanced language exams, and 2 English teachers who did not know the topic and who have MA in English)
– Then all of us discussed the inconsistencies until finding the best solution
– The final solution was given to a bilingual psychology student for backtranslation to Hungarian
– Finally, the seven persons discussed the final solution
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Sample • 1364 persons
– 924 women and 405 men – Age between 14 and 86 (M = 32.19, SD = 14.70)
• Education: – 142 primary school – 53 vocational school – 399 high school degree – 712 higher education (BA, BSC, MA, MSC) – 51 postgraduate degree
• Place of residence: – 170 villages, 553 towns, 326 county towns, 309 capital
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Measures • The translated version of ZTPI (56 items), 5-point Likert
scale (1 = very uncharacteristic; 5 = very characteristic) • Gender • Age • Marital status • Place of residence • Perceived financial status • Expected financial situation in 5-10 years • Level of education • Perceived health
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Results 1: EFA & CFA • Principal Axis Factoring (PAF) extraction • Promax rotation (Kappa = 0) (Brown, 2006)
– Oblique solution instead of orthogonal: (1) it provides better results for the CFA, (2) TP subscales correlated in previous studies (Anagnostopoloulos & Griva, 2011)
– Respecting the rule of .32 of Tabachnik & Fidell (2001)
A 36 item solution emerged: Strong points • Five factors: scree test • Exp. Var: 45.9% • Bartlett and KMO: OK • Alphas are higher than .7
Weak points CFA results:
• RMSEA = .068 !!! (X < .06) • CFI = .75 !!! (X > .95) • TLI = .72 !!! (X > .95)
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The final 17-item version
RMSEA = .039 CFI = .963 TLI = .954
36 items ↓
17 items
OK, but how the hell can I carry out a confirmatory factor analysis?
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CFA – how to do it?
• The most important message of this presentation:
„USE YOUTUBE IF YOU DON’T KNOW HOW TO CARRY OUT A STATISTICAL ANALYSIS!”
http://www.youtube.com/watch?v=JkZGWUUjdLg
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Potential problems… • It is not the best to use ML method if we don’t have the normal
distribution of our variables (generally it is the case…) • Modification indices can be very useful in the item selection, do
not overdose it (follow common sense as well) • Error covariances can be useful, however, we have to explain
why we put such error covariances (case of Vallerand) • It is very difficult to achieve a good model fit if we have 4-5
factors and 4-5 items per factor • This method pushes authors to create scales with few items
which can measure efficiently narrow psychological constructs – TP is a multidimensional phenomena and NOT a narrow
construct…
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Are Competition and Extrinsic Motivation Reliable Predictors
of Academic Cheating?
Gábor OROSZ University of Szeged
Institute of Psychology 07/10/2011
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Theoretical roots of this question
• “Competition is perhaps the single most toxic ingredient in a classroom, and it is also a reliable predictor of cheating” (Anderman & Murdock, 2007, p. XIII)
• Competition has an overall negative impact on performance, problem solving, and personal relationships in comparison with cooperation (Deutsch, 1949, Johnson & Johnson, 1974, 1979, 1982; Johnson, Maruyama, Johnson, Nelson, Skon, 1981; Lewis, 1944a, 1944b; Qin, Johnson & Johnson, 1995)
• Competition undermines intrinsic motivation (Deci, Betley, Kahle, Adrams & Porac, 1981; Vallerand, Gauvin, Hallivell, 1986a, 1986b)
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Paradigm shift of competition • Solid theoretical basis regarding competition’s positive
consequences: – It can improve performance, interpersonal
relationships, resource control and intrinsic motivation (Bornstein, Erev & Rosen, 1990; Carnavale & Probst, 1997; Charlesworth, 1996; Epstein & Harackiewicz, 1992; Erev et al., 1993; Fülöp, 1997, 1999, 2001, 2004; Harackiewicz, 1998; Hawley, 2003, 2006; Hurlock, 1927; Moede, 1914; Reeve & Deci, 1999; Ryckman, Hammer, Kaczor & Gold 1996; Sims, 1927; Tauer & Harackiewicz, 2004; Tassi & Schneider, 1997; Tjosvold, Johnson, Johnson & Sun, 2003, 2006; Young, Fisher & Lindquist, 1993; Wentzel, 1991; Whittemore, 1924; Julian, Bishop, & Fiedler, 1966; Rabbie, & Wilkens 1971; Reeve, Cole & Olson, 1986; Reeve & Vallerand, 1984; Vallerand & Reid, 1984)
– Furthermore, perceived classroom competition was positively related to self-reported cheating behavior (Smith, Ryan & Diggins, Taylor, 1972; Pogrebin & Dodge, 2002 Whitley, 1998)
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The nature of paradigm shift of competition
New paradigm Competition is not
opposed to cooperation
Competition is a heterogenous phenomena
Competition is a situational and personality variable
Old paradigm Competition is opposed
to cooperation
Competition is a homogenous phenomena
Competition is a situational variable
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First goal of the present study
• To assess the impact of individual-level and situation-level competition-related variables on academic cheating.
– Aggressively competing students will cheat more, than those students who have positive attitudes towards competition.
– Such classroom atmosphere in which the goal is achieving the recognition of the teachers will induce more cheating, than such competitive classroom atmosphere which promotes skill development.
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Second goal of the study
– Previous studies found that mastery and intrinsic motivations reduce cheating, whereas performance goals and extrinsic motivations enhance it. (Anderman, Griesinger, & Westerfield, 1998; Anderman & Midgley, 2004; Anderman & Murdock, 2007; Murdock & Anderman, 2006)
– In these studies performance goal orientation and competitive pressures are interpreted as overlapping concepts – NO DISTINCTION BETWEEN THE TWO
• Distinction between the effects of motivation- and competition-related factors’ of cheating.
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Third goal of the study • Comparing the relative importance of motivational
and competition-related variables with proximal variables of cheating behavior such as:
(1) attitudes towards cheating (3) risk of detection (2) guilt (4) possible punishments
We hypothesize that the importance of motivational and competition-related factors is overrated in the literature of academic cheating
More proximal factors have vastly larger impact of cheating than extrinsic motivation and competition
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Factors that have effect on cheating I.
• Grade point average – negative (Kerkvliet, 1994; Kerkvliet & Sigmund, 1999; Leming, 1980; Newstead, Franklyn-Stokes & Armstead, 1996; Whitley, 1998; Straw, 2002)
• Attitudes towards cheating – positive (Bolin, 2004; Jordan, 2001; Jensen, Arnett, Feldman & Cauffman, 2001; Whitley, 1998)
• Guilt – negative (Diekhoff, LaBeff, Shinohara & Yasukawa,1999; Malinowski & Smith, 1985)
• Classroom competition – positive (Anderman & Murdock, 2007; Smith, Ryan & Diggins, 1972; Taylor et al., 2002)
• Self-developmental competition – negative (Orosz, 2010)
• Hypercompetitive traits – positive (Orosz, Jánvári, Salamon, 2011)
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Factors that have effect on cheating II.
• Motivation: Vallerand et al., 1992 – Academic Motivation Scale – Intrinsic motivation to know – negative – Extrinsic motivation of external regulation – positive – Amotivation - positive
• Risk of detection – negative (Heisler, 1974; Leming, 1978; Corcoran & Rotter, 1987; Covey, Saladin & Killen, 1989; Whitley, 1998)
• Expected punishments – negative (Bunn, Caudill & Gropper, 1992; Cohran, Chamlin, Wood & Sellers, 1999)
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Hypotheses I. H1a: Attitude of aggressive competition will be positively
correlated with academic cheating H1b: Attitude of self-developmental competition will be
negatively correlated with cheating H1c: Positive attitude towards competition will be unrelated
to cheating H1d: Such classroom climate, in which the goal of
competition is recognition by the teachers, will be linked with academic dishonesties
H1e: Competitive climate, which promotes self-development, will lead to lower prevalence of cheating
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Hypotheses II.
H2: Motivational pattern (IM, EM, AM) are separate from those of the competition-related individual and contextual variables
H3: the magnitude of the effects of motivational and competition-related variables on academic dishonesty is lower than those of the proximal individual (GPA, attitudes towards cheating, guilt) and situational variables (risk of detection, expected punishments)
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Participants • 620 high school students (M = 264, F = 356)
• 19 classes from 7 schools – 2 schools upper third, 3 middle third, 2 lower third section of Hungarian high school ranking
• Age: 13-20 years, M = 16.66 years (SD = 1.51)
• Teachers were not present during data gathering
• 381 students filled in the questionnaire concerning competitive climate, 236 students did not fill in this scale
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Measures - individual • Individual differences of competition:
– Aggressive competition scale: „I can be aggressive with my rivals” or “I’m often in conflict with my opponents”
– Self-developmental competition scale: “Competition helps me to improve my skills” or “Competition brings the best out of me”
– Positive attitudes towards competition scale: “I like the challenge of competition” or “Competition inspires me”
• Individual differences of motivation Vallerand et al. AMS: – IM to know – motivation to acquire knowledge – EM external regulation – learning due to only external pressures
and obligations – AM – the absence of motivation
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Measures – proximal & situational • Two cheating vignettes: cheating sheet & copying
– self-reported cheating – acceptance – punishments – feeling of guilt – Perceived risk of detection
• Competition Climate Scale (CCS): – Constructive competition (CC) - it has positive impact on
students performance, creativity, interpersonal relationship – Destructive competition (DC) – it has negative impact on
relationships, the goal is achieving the recognition of teachers
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Results - Descriptives Self-
reported cheating
Accept. Guilt Risk of
det. Exp.
punish.
Cheating sheets
no 24.6%
1 4.4% 40.5% 2.4% 14.4%
2 33.8% 37.1% 14.7% 82.6%
yes 75.4% 3 47.1% 17.5% 72.0% 2.5%
4 14.8% 4.9% 10.9% .5%
Copying no 38.1%
1 7.6% 35.3% 1.3% 12.1%
2 39.3% 33.2% 7.8% 86.2%
yes 61.9% 3 41.5% 21.1% 59.3% 1.2%
4 11.5% 10.4% 31.6% .5%
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χ2/df = 1.786, CFI = .959, TLI = .952, RMSEA = .036
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Results on the basis of the model I.
H1a: Aggressive competition has a positive indirect effect on SR cheating – proved
H1b: Self-developmental competition has a negative indirect effect on SR cheating – proved
H1c: Positive attitude towards competition is unrelated to cheating – proved
H1d: Such classroom climate, in which the goal of competition is recognition by the teachers, will be linked with academic dishonesties – not proved: DC climate is unrelated
H1e: Competitive climate, which promotes self-development, will lead to lower prevalence of cheating – not proved: CC climate is unrelated
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Results on the basis of the model II.
H2: Motivational pattern (IM, EM, AM) are separate from those of the competition-related individual and contextual variables – proved: both CFAs and the model indicates that
H3: the magnitude of the effects of motivational and competition-related variables on academic dishonesty is lower than those of the proximal individual (GPA, attitudes towards cheating, guilt) and situational variables (risk of detection, expected punishments) – proved: (1) competitive climate (CC & DC) has no effect, (2) SD Comp & Aggr. Comp has small indirect effect, (3) extrinsic motivation no effect, BUT IM & AM have a serious effect!
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Discussion “Competition is perhaps the single most toxic ingredient in a classroom, and it is also a reliable predictor of cheating” (Anderman & Murdock, 2007, p. XIII) – probably not true!
1. It is not the extrinsic motivation but the amotivation which counts!
2. Intrinsic motivation can prevent cheating!
3. Both motivational and competition-related effects are less important than proximal variables such as acceptance, GPA and guilt
− Are Competition and Extrinsic Motivation Reliable Predictors of Academic Cheating?
− Not really!!!
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Practical implications • Eliminating competition from classroom is not the
best way to prevent cheating! (Tjosvold, Fülöp, etc) • Eliminating extrinsic motivation is not the best way
either in order to prevent cheating! (see Haraczkiewicz, Pintrich, etc.)
• Eliminating amotivation and increasing intrinsic motivation can be more useful! (enthusiasm studies)
• As teachers we have to create such environment during exams in which we prevent students from cheating: Risk of detection is more important than serious punishments (Houston)
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Limitations & further directions • Hungarian educational context: in other contexts high
school competition can be more destructive which creates more conflicts, which induce individual cheating and prevent from collaborative cheating
• In Hungary a pretty large proportion of students cheated. It is surely different in other countries (i.e. France )
• It would be important to measure SR cheating in a more gradual way (occurrence of cheating per semester)