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Does the Love of Money Cause Pay Dissatisfaction?

Does the Love of Money Cause Pay Dissatisfaction?. Thomas Li-Ping Tang Middle Tennessee State University, the USA Fernando Arias-Galicia Universidad Autonoma del Estado de Morelos, Mexico Ilya Garger Saratov State Social-Economic University, Russia Theresa Li-Na Tang

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Does the Love of Money Cause Pay Dissatisfaction?

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  1. Does the Love of Money Cause Pay Dissatisfaction? Thomas Li-Ping Tang Middle Tennessee State University, the USA Fernando Arias-Galicia Universidad Autonoma del Estado de Morelos, Mexico Ilya Garger Saratov State Social-Economic University, Russia Theresa Li-Na Tang Affinion Group, the USA The 26th International Congress of Applied Psychology Athens, Greece, July 16-21, 2006

  2. TOTO SUTARSO, Middle Tennessee State University, USA, ADEBOWALE AKANDE, International Institute of Research,South Africa, MICHAEL W. ALLEN, Griffith University, Australia, ABDULGAWI SALIM ALZUBAIDI, Sultan Qaboos University, Oman, MAHFOOZ A. ANSARI, University Science Malaysia,Malaysia, FERNANDO ARIAS-GALICIA, National University of Mexico, Mexico, MARK G. BORG, University of Malta,Malta, LUIGINA CANOVA, University of Padua,, Italy, BRIGITTE CHARLES-PAUVERS, University of Nantes, France, BOR-SHIUAN CHENG, National Taiwan University,Taiwan, RANDY K. CHIU, Hong Kong Baptist University, Hong Kong, IOANA CODOBAN, Babes-Bolyai University, Romania, LINZHI DU, Nanjing University, China, ILIA GARBER, Saratov State Social-Economic University,Russia, CONSUELO GARCIA DE LA TORRE, Technological Institute of Monterrey, Mexico, ROSARIO CORREIA HIGGS,Polytechnic Institute of Lisbon – Portugal, Portugal, CHIN-KANG JEN, National Sun-Yat-Sen University,Taiwan, ALI MAHDI KAZEM, Sultan Qaboos University,Oman, KILSUN KIM, Sogang University, South Korea,

  3. VIVIEN KIM GEOK LIM, National University of Singapore, Singapore, ROBERTO LUNA-AROCAS, University of Valencia, Spain, EVA MALOVICS, University of Szeged,Hungary, ANNA MARIA MANGANELLI, University of Padua, Italy, ALICE S. MOREIRA, Federal University of Pará, Brazil, ANTHONY UGOCHUKWU O. NNEDUM,Nnamdi Azikiwe University, Nigeria, JOHNSTO E. OSAGIE, Florida A & M University, USA, FRANCISCO COSTA PEREIRA,Polytechnic Institute of Lisbon – Portugal, Portugal, RUJA PHOLSWARD, University of the Thai Chamber of Commerce, Thailand, HORIA D. PITARIU, Babes-Bolyai University, Romania, MARKO POLIC, University of Ljubljana, Slovenia, ELISAVETA SARDZOSKA,University St. Cyril and Methodius,Macedonia, PETAR SKOBIC, Middle Tennessee State University, Croatia, ALLEN F. STEMBRIDGE, Southwestern Adventist University, USA, THERESA LI-NA TANG, Cendant Marketing Group, Brentwood, TN, USA, THOMPSON SIAN HIN TEO,National University of Singapore,Singapore, MARCO TOMBOLANI, University of Padua,Italy, MARTINA TRONTELJ, University of Ljubljana, Slovenia, CAROLINE URBAIN, University of Nantes, France, PETER VLERICK,Ghent University, Belgium

  4. Outline • The Meaning of Money • The Love of Money Scale • The Pay Level Satisfaction Scale • Method • Results • Discussion, Implications, Limitations

  5. Money • The instrument of commerce and the measure of value(Smith, 1776/1937). • Attract, retain, and motivate employees and achieve organizational goals(Chiu, Luk, & Tang, 2002; Milkovich & Newman, 2005; Tang, Kim, & Tang, 2000).

  6. The Meaning of Money is “in the eye of the beholder”(McClelland, 1967, p. 10) and can be used as the “frame of reference”(Tang, 1992)in which people examine their everyday lives(Tang & Chiu, 2003; Tang, Luna-Arocas, & Sutarso, 2005).

  7. The Importance of Money *10 Job Preferences, Pay was ranked: (Jurgensen, 1978) No. 5 by Men No. 7 by Women *11 work goals, Pay was ranked: (Harpaz, 1990). No. 1 in Germany No. 2 in Belgium, the UK, and the US

  8. Why Do Students Go to College? In 1971, 49.9 % of freshman said: They want “to make more money”. In 1993, 75.1 % (The American Freshman, 1994).

  9. Major Cause of Dissatisfaction Among University Students The Lack of Money  1981-1987: No. 2 1990-1996: No. 3 1997-2003: No. 1(Bryan,2004).

  10. Pay Dissatisfaction Has “numerous undesirable consequences” (Heneman & Judge, 2000: 77): Turnover(Hom & Griffeth, 1995; Tang, Kim, & Tang, 2000), Counterproductive Behavior(Cohen-Charash, & Spector, 2001; Luna-Arocas & Tang, 2004), and Unethical Behavior(e.g., Chen & Tang, 2006; Tang & Chen, 2005; Tang & Chiu, 2003).

  11. The Love of Money Pay Level Satisfaction Some Oldest References “Poverty consists, not in the decrease of one’s possessions, but in the increase of one’s greed” (Plato, 427-347 BC).

  12. Some Oldest References “Whoever loves money never has money enough; whoever loves wealth is never satisfied with his income” (Ecclesiastes, 5: 10, New International Version). The Love of Money  Pay Level Satisfaction

  13. Some Oldest References • “People who want to get rich fall into temptation and a trap and into many foolish and harmful desires that plunge men into ruin and destruction. For the love of money is a root of all kinds of evil” (Bible: 1 Timothy, 6: 9-10; Tang & Chiu, 2003). •  The Love of Money Scale

  14. The ABCs of Money Attitudes Affective: Do you “love or hate” money? Behavioral: What do you “do” with your money? Cognitive: What does money “mean” to you?

  15. Money Is a Motivator (+) • 4 Methods: Improvement in Productivity • Participation: 0% • Job design: 9% • Goal setting: 16% • Contingent Pay: 30% • “No other incentive or motivational technique comes even close to money” (Locke, Feren, McCaleb, Shaw, & Denny, 1980: 381) • Money is a motivator (Stajkovic & Luthans, 2001). • Money is NOT a motivator (Herzberg, 1987).

  16. The Love of Money Scale Factor 1: Rich (Affective) 1. I want to be rich. 2. It would be nice to be rich. 3. Having a lot of money (being rich) is good. Factor 2: Motivator (Behavior) 4. I am motivated to work hard for money. 5. Money reinforces me to work harder. 6. I am highly motivated by money. Factor 2: Importance (Cognitive) 7. Money is good. 8. Money is important. 9. Money is valuable.

  17. Pay Level Satisfaction The 18-item-4-factor Pay Satisfaction Questionnaire (PSQ, Heneman and Schwab, 1985) Pay, Bonus, Pay Raise, Administration One of the most well-known multidimensional measures of Pay Satisfaction (e.g., Williams, McDaniel, & Nguyen, 2006).

  18. Measurement Invariance It does little good to test a theoretical and conceptual relationship across cultures “unless there is confidence that the measures operationalizing the constructs of that relationship exhibit both conceptual and measurement equivalence across the comparison groups” (Riordan & Vandenberg, 1994, p. 645).

  19. Measurement Invariance: 9 Steps • an omnibus test of equality of covariance matrices across groups, • a test of “configural invariance”, • a test of “metric invariance”, • a test of “scalar invariance”, • a test of the null hypothesis that like items unique variances are invariant across groups, • a test of the null hypothesis that factor variances were invariant across groups, • a test of the null hypothesis that factor covariances were invariant across groups, • a test of the null hypothesis of invariant factor means across groups, and • other more specific test.

  20. Measurement Invariance “Tests for configural and metric invariance were most often reported” (Vandenberg and Lance (2000) p. 35). Configural invariance—factor structures Metric invariance—factor loadings

  21. Method Researchers collected data from 200 full-time white-collar employees and managers in large organizations. Translation-back translation The Love of Money Scale The Pay Level Satisfaction Scale

  22. 32 Samples, N = 6,659 1. Australia (n = 262), 11. Hungry (100) 2. Belgium (201), 12. Italy (204) 3. Brazil (201), 13. Macedonia (204) 4. Bulgaria (162), 14. Malaysia (200) 5. China-1 (319 students), 15. Malta (200) 6. China-2 (204 employees), 16. Mexico (295) 7. Croatia (165), 17. Nigeria 8. Egypt (200), 18. Oman 9. France (135), 19. Peru 10. Hong Kong (211), 20. Philippines (200)

  23. 32 Samples, N = 6,659 21. Portugal (200), 31. Thailand (202) 22. Romania (200), 32. the USA (274) 23. Russia (200), 24. Singapore-1 (203), 25. Singapore-2 (336), 26. Slovenia (200), 27. South Africa (211), 28. South Korea (203), 29. Spain (183), 30. Taiwan (200),

  24. Measurement Invariance: 8 Steps 1. Configural invariance (Factor Structures, Form): • χ2, df , • TLI > .95, • CFI > .95, • SRMSR < .08, • RMSEA < .08 and 2. Metric invariance (Factor Loadings, Unit): • chi-square change (Δχ2/Δdf) • fit index changeΔCFI

  25. 3. Item-level metric invariance The Z statistic for all pair-wise comparisons can be calculated from the parameter estimates, standard errors, and the asymptotic covariance matrix of the unconstrained model. 4. First-order scalar invariance (Intercept, Origin)

  26. 5. First-order latent mean comparison 6. Second-order metric invariance 7. Second-order scalar invariance 8. Second-order latent mean comparison

  27. Table 1. Major Variables of the Study across 29 Geopolitical Entities ______________________________________________________________________________________________________ Sample N Age Sex Education Rich Motivator Important LOM Pay Level (% Male) (Year) M SD M SD M SD M SD M SD ______________________________________________________________________________________________________ 1. Australia 262 26.81 29 12.50 3.73 .81 3.23 .90 3.79 .73 3.58 .66 3.14 .94 2. Belgium 201 38.97 57 16.09 3.40 .79 3.04 .84 3.68 .72 3.37 .61 3.30 .85 3. Brazil 201 37.71 45 16.92 3.59 .91 3.05 .98 3.73 .81 3.45 .63 2.68 .95 4. Bulgaria 162 27.36 64 16.91 3.92 .71 3.57 .85 3.82 .65 3.78 .61 2.65 .84 5. China 204 31.57 60 15.38 3.69 .80 3.28 .85 3.79 .76 3.59 .66 2.72 .81 6. Egypt 200 40.26 50 14.88 3.75 1.05 2.90 1.04 4.08 .74 3.57 .70 3.37 1.08 7. France135 32.30 56 16.19 3.79 .78 3.38 .92 3.61 .70 3.59 .66 2.86 1.04 8. HK 211 30.68 49 15.67 4.06 .69 3.33 .90 4.07 .59 3.82 .58 3.00 .83 9. Hungary 100 34.06 55 15.96 3.83 .73 3.55 .90 3.98 .71 3.79 .67 3.05 1.08 10. Italy 204 37.88 39 14.12 3.37 .96 2.86 .93 3.43 .73 3.22 .72 3.04 .88 11. Macedonia 204 41.60 44 13.31 3.97 .81 3.54 .88 4.07 .71 3.86 .61 2.87 .97 12. Malaysia 200 31.80 53 15.23 3.99 .68 3.64 .84 4.17 .56 3.93 .54 3.12 .89 13. Malta 200 36.91 51 16.47 3.95 .85 3.13 .98 4.33 .57 3.81 .66 2.56 1.02 14. Mexico 295 30.79 54 14.31 3.42 .89 3.26 .97 3.80 .72 3.49 .71 2.97 .93 15. Nigeria 200 34.80 61 15.74 4.48 .60 3.24 .99 4.57 .49 4.09 .42 3.45 .84 16. Oman 204 29.74 64 14.67 3.81 .80 2.82 .95 4.15 .60 3.59 .61 3.56 .94 17. Peru 190 31.89 64 17.30 3.62 .74 3.27 .97 3.77 .81 3.55 .65 3.07 .87 18. Philippines 200 33.45 51 17.13 3.80 .81 3.26 1.00 4.08 .66 3.71 .65 3.44 .74 19. Portugal 200 35.18 40 15.44 3.50 .84 2.78 .84 3.81 .62 3.36 .61 2.70 .90 20. Romania 200 38.02 27 16.69 3.83 .77 3.56 .85 3.85 .74 3.75 .63 2.56 .94 21. Russia 200 35.92 42 17.58 3.96 .78 3.34 .84 3.88 .70 3.73 .61 2.76 .92 22. Singapore 336 33.23 57 15.01 3.95 .69 3.52 .89 4.07 .67 3.85 .59 3.26 .82 23. Slovenia 200 38.72 43 13.68 3.37 .80 3.00 .89 3.66 .66 3.34 .57 2.93 1.00 24. S. Africa 203 46.52 46 15.76 3.88 .67 3.16 .75 4.03 .58 3.69 .44 2.28 .56 25. S. Korea 203 37.21 73 15.92 4.21 .62 3.67 .78 4.24 .58 3.97 .52 3.03 .82 26. Spain 183 33.81 59 14.15 3.56 .89 2.91 .94 3.72 .77 3.40 .72 3.12 .86 27. Taiwan 201 34.95 48 16.56 4.10 .68 3.81 .80 4.15 .62 4.02 .56 3.03 .86 28. Thailand 200 33.29 54 16.98 3.88 .86 3.30 .84 3.87 .68 3.68 .65 3.19 .63 29. US 274 35.04 45 15.08 3.85 .79 3.59 .98 4.10 .65 3.85 .65 2.83 1.00 ____________________________________________________________________________________________________ Whole Sample 5,973 34.70 50 15.46 3.80 .83 3.27 .94 3.95 .72 3.67 .66 2.99 .94 ____________________________________________________________________________________________________ Note. Age and education were expressed in years. Sex was expressed in % male.

  28. Table 2. Configural Invariance of the 9-Item-3-Factor Love of Money Scale (LOMS) ____________________________________________________________________________ χ2 df p TLI CFI SRMSR RMSEA ____________________________________________________________________________ 1. Australia 74.47 24 .00 .9874 .9933 .0561 .0898 2. Belgium 27.41 24 .29 .9988 .9994 .0416 .0266 3. Brazil 26.49 24 .33 .9992 .9996 .0412 .0228 4. Bulgaria 34.37 24 .08 .9973 .9986 .0386 .0428 5. China 34.82 24 .07 .9965 .9981 .0337 .0471 6. Egypt 29.64 24 .20 .9979 .9989 .0369 .0344 7. France 37.98 24 .03 .9929 .9962 .0480 .0659 8. HK 46.43 24 .00 .9939 .9968 .0437 .0667 9. Hungary 107.09 24 .00 .9501 .9734 .0760 .1870 10. Italy 51.98 24 .00 .9905 .9950 .0424 .0758 11. Macedonia 60.84 24 .00 .9885 .9939 .0518 .0870 12. Malaysia 106.90 24 .00 .9772 .9879 .0520 .1317 13. Malta 445.66 24 .00 .8931 .9430.1197 .2971 14. Mexico 79.35 24 .00 .9873 .9932 .0506 .0886 15. Nigeria 92.67 24 .00 .9802 .9938 .1201 .1228 16. Oman 15.26 24 .91 1.0000 1.0000 .0255 .0000 17. Peru 60.03 24 .00 .9881 .9937 .0485 .0891 18. Philippines 73.16 24 .00 .9852 .9921 .0477 .1015 19. Portugal 30.39 24 .17 .9979 .9989 .0345 .0366 20. Romania 60.24 24 .00 .9883 .9938 .0471 .0871 21. Russia 33.59 24 .09 .9969 .9983 .0356 .0448 22. Singapore 95.95 24 .00 .9877 .9934 .0454 .0946 23. Slovenia 41.30 24 .02 .9940 .9968 .0593 .0602 24. S. Africa 37.64 24 .04 .9948 .9973 .0582 .0530 25. S. Korea 43.74 24 .01 .9951 .9974 .0415 .0638 26. Spain 41.08 24 .02 .9936 .9966 .0463 .0625 27. Taiwan 72.01 24 .00 .9874 .9933 .0450 .1000 28. Thailand 30.64 24 .16 .9980 .9989 .0284 .0373 29. US 56.46 24 .00 .9927 .9961 .0427 .0704 ____________________________________________________________________________ Note. We retained a sample if it satisfied all of the following four rigorous criteria (i.e., TLI > .95, CFI > .95, SRMSR < .08, and RMSEA < .08). In this analysis, we eliminated 12 samples (printed in bold) and retained 17 samples.

  29. Table 3 Summary of Fit Statistics _____________________________________________________________________________________________________________________________________________ Model Model χ2dfp TLI CFI SRMSR RMSEA Comparison Δχ2 Δdf ΔCFI _____________________________________________________________________________________________________________________________________________ Step 1. Testing Measurement Invariance of Second-Order Factor Model of the Love of Money Model 1 Configural invariance (see results for each geopolitical entity (sample) in Table 2) Model 2 Construct-level metric invariance A. Unconstrained 615.95 408 .01 .9960 .9979 .0416 .0123 B. Constrained (first-order factor loading) 982.98 504 .01 .9926 .9951 .0478 .0168 2A vs. 2B 367.02* 96 .0034 Model 3 Item-level metric invariance (Item 1 constrained) 716.89 424 .01 .9946 .9970 .0486 .0143 3 vs. 2A 100.94* 16 .0014 Model 4 Scalar invariance 2835.35 648 .01 .9736 .9776 .0488 .0317 4 vs. 2B 1852.37* 176 .0175 2B + intercepts of measured variables invariance Model 5 First-order latent mean comparison C. Baseline (with only first-order factor) 2835.35 648 .01 .9736 .9776 .0488 .0317 D. Estimated latent mean 2790.17 645 .01 .9740 .9781 .0461 .0314 5C vs. 5D 45.18* 3 .0005 Model 6 Second-order metric invariance E. Baseline (with second-order factor) 2835.35 648 .01 .9736 .9776 .0488 .0317 F. E + second-order factor loading invariance 2932.97 680 .01 .9741 .9770 .0634 .0314 6E vs. 6F 97.62* 32 .0006 Model 7 Second-order scalar invariance F + second-order intercepts invariance 3810.82 697 .01 .9651 .9662 .0557 .0364 6F vs. 7 877.85* 17 .0108 Model 8 Second-order latent mean comparison 3880.99 698 .01 .9644 .9675 .1190 .0368 8 vs. 7 70.17* 1 .0013 ____________________________________________________________________________________________________________________________________________

  30. Table 3 Summary of Fit Statistics _______________________________________________________________________________________________________________________________________ Model Model χ2dfp TLI CFI SRMSR RMSEA Comparison Δχ2 Δdf ΔCFI _______________________________________________________________________________________________________________________________________ Step 2: Testing Measurement Invariance of First -Order Factor Model of the Pay Level Satisfaction Model 1 Configural invariance (see results for each geopolitical entity (sample) in Table 4) Model 2 Construct-level metric invariance A. Unconstrained 19.73 18 .35 .9997 .9999 .0030 .0067 B. Constrained (first-order factor loading) 89.46 42 .01 .9959 .9981 .0113 .0229 2A vs. 2B 69.73* 24 .0018 Model 3 Item-level metric invariance (Item 1 constrained) 49.01 26 .01 .9968 .9991 .0032 .0203 3 vs. 2A 29.28* 8 .0010 Model 4 Scalar invariance 443.64 74 .01 .9820 .9852 .0147 .0482 4 vs. 2B 354.18* 32 .0129 2B + intercepts of measured variables invariance Model 5 First-order latent mean comparison 916.61 75 .01 .9595 .9663 .0968 .0722 4 vs. 5 472.97* 1 .0189 _______________________________________________________________________________________________________________________________________ Step 3: Testing Common Method Variance Model 1 First-order factor model without latent CMV 197.21 62 .01 .9949 .9965 .0402 .0453 Model 2 First-order factor model with latent CMV 150.53 49 .00 .9952 .9974 .0342 .0441 2 vs. 1 186.68* 13 .0009 _______________________________________________________________________________________________________________________________________ Step 4: The Love of Money to Pay Level Satisfaction Relationship Model 1 Constrained the Love of Money Scale 651.30 337 .01 .9896 .9923 .0981 .0297 Model 2 Model 1 + Constrained Pay Level Satisfaction 702.48 349 .01 .9887 .9914 .0982 .0309 2 vs. 1 51.18* 12 .0009 Model 3 Model 2 + Constrained the LOM  PLS Path 715.42 353 .01 .9886 .9911 .1224 .0311 3 vs. 2 12.94* 4 .0003 _______________________________________________________________________________________________________________________________________ Note. *p < .05.

  31. Table 4 Configural Invariance of the 4-Item-1-Factor Pay Level SatisfactionScale(PLSS) ____________________________________________________________________________ χ2 df p TLI CFI SRMSR RMSEA ____________________________________________________________________________ 1. Australia .31 2 .86 1.0000 1.0000 .0030 .0000 2. Belgium 4.82 2 .00 .9951 .9990 .0090 .0839 3. Brazil 2.25 2 .33 .9994 .9999 .0104 .0250 4. Bulgaria 13.35 2 .00 .9697 .9939 .0233 .1878 5. China 2.84 2 .24 .9981 .9996 .0156 .0455 6. Egypt 5.06 2 .08 .9925 .9985 .0210 .0877 7. France 13.23 2 .00 .9681 .9936 .0169 .2047 8. HK 5.49 2 .06 .9933 .9987 .0151 .0912 9. Hungary 11.46 2 .00 .9657 .9931 .0140 .2186 10. Italy 13.11 2 .00 .9793 .9959 .0191 .1654 11. Macedonia 13.52 2 .00 .9722 .9944 .0382 .1684 12. Malaysia 17.00 2 .00 .9717 .9943 .0207 .1941 13. Malta 25.48 2 .00 .9545 .9909 .0178 .2429 14. Mexico 4.04 2 .13 .9972 .9994 .0087 .0589 15. Nigeria 30.86 2 .00 .9419 .9884 .0920 .2693 16. Oman 40.27 2 .00 .9296 .9859 .0370 .3070 17. Peru 5.87 2 .05 .9919 .9984 .0143 .1012 18. Philippines 10.21 2 .01 .9842 .9968 .0316 .1436 19. Portugal 5.92 2 .05 .9921 .9984 .0112 .9992 20. Romania 9.11 2 .01 .9843 .9969 .0144 .1337 21. Russia 5.53 2 .06 .9908 .9982 .0235 .0942 22. Singapore 2.23 2 .33 .9989 1.0000 .0063 .0184 23. Slovenia 7.33 2 .03 .9897 .9979 .0092 .1158 24. S. Africa .05 2 .07 1.0000 1.0000 .0049 .0000 25. S. Korea 5.53 2 .06 .9940 .9988 .0089 .0934 26. Spain 4.01 2 .13 .9957 .9991 .0136 .0743 27. Taiwan 2.17 2 .34 .9996 .9999 .0102 .0207 28. Thailand 5.24 2 .07 .9936 .9987 .0243 .0902 29. US 1.82 2 .40 1.0000 1.0000 .0068 .0000 ____________________________________________________________________________ Note. We retained a sample if it satisfied the following four rigorous criteria (i.e., TLI > .95, CFI > .95, SRMSR < .08, and RMSEA < .08). In this analysis, we eliminated 19 samples (printed in bold) and retained 10 samples.

  32. Implications--1 This study is an initial step in the measurement and functional invariance of the Love of Money Scale. Using the most rigorous criteria, 17 samples (out of 29) pass the configural invariance. Using a less rigorous criteria (RMSEA < .10), *25 samples pass the configural invariance test *4 Samples fail: Hungary, Malaysia, Malta, & Nigeria.

  33. Implications--2 Most samples fail the RMSEA (root mean square error of approximation) criterion that is sensitive to (1) sample size and (2) model complexity. Most samples that fail have several ethnic groups (e.g., Nigeria has Igbo, Yoruba, Housa, and others, Malaysia has Chinese, Indian, Malays, Caucasian, and others)

  34. Remedies: Configural Invariance • Increase sample size (> 300) • Use a simple model • Analyze sub-samples using MGCFA • Use EFA to identify the causes • Revise the model • One model does not fit all samples

  35. Non-Metric Invariant Items Four strategies (the unit of the measurement): 1. Ignore the non-invariance because the comparison of data is not meaningful, 2. Eliminate non-invariant items from the scale, 3. Invoke partial metric invariance that allows the factor loading of non-invariant items to vary, and 4. Interpret the source of non-invariance (Cheung, 2002).

  36. Eliminate non-invariant items? Not only metric non-invariance is desirable but also is “a source of potentially interesting and valuable information about how different groups view the world” (Cheung and Rensvold, 2002, p. 252).

  37. “I” Orientation When the “individual self” is the center of the respondents’ psychological field for items of a scale (I want to be rich), people in individualistic cultures (Hofstede and Bond, 1988; Yu and Yang, 1994) may have different perceptions than those in collectivistic cultures (Riordan and Vandenberg, 1994; Tang et al., 2002).

  38. “I” Orientation People in high collectivistic cultures (e.g., China, South Korea) may consider “I want to be rich” not acceptable in their cultures Researchers have to examine the wording or phrasing of items carefully when they design future measurement instruments (Riordan & Vandenberg, 1994: 667).

  39. Limitations • Translation equivalence • Sample equivalence • Common method biases • Extraneous or nuisance variables (size or org. economy of the region, unemployment rate, etc.) • Non-random samples, we cannot generalize the findings to the whole population with full confidence.

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