Question 17.1

Question 17.2

Question 17.3

Question 17.4

Question 17.5

Question 17.6

Question 17.7

Question 17.8

Question 17.9

Question 17.10

Question 17.11

Question 17.12

Question 17.13

Question 17.14

Question 17.15

Question 17.16

Question 17.17

Question 17.18

Question 17.19

Question 17.20

For each of the following, (i) identify the incorrect symbol, (ii) state what the correct symbol should be, and (iii) explain why the initial symbol was incorrect.

1. 02QOpZDF7dRZMLOro4FVPC3jf/40ImZpdm8IeLmmE4mTIAYaybrbvru/wzqtpxjBJIYxJYZWlqJPpd6HVD4F/keto4texi/VuR3eovliWCqcCLbaD5a0VD1FVJ1I/PGumr2vlL26f0JK//9GPWjA3F7W8rBe7FDN7a0u4uBgcmwPf2L1fDmfw47/EbkXr7Xag87Cx7sCXK6m9knZpJvUQf94o90fqVKc278HvQ==
2. lvUyKu/UpYnjpGuTsXCib27c3ZNEIQ5Iws1Fd0Z/t0K+TC3uJuvxVUZTW0oBiVJBlbj6j1Mxy1XcfeCJ67kXmJ6BUXfq3lwyWlsoZR37VBsR/KeoP2Nkg4/DuT0BXf5+fCSOsahIpWurntRvdMqPmwb+iN2ZH54yfRVjA2xxUf65yzj4Vij3r1d8C/8Y80NGmizr2Tq8Grks1OPIzYBf/pXwrSqHvh7voiRd/MsowdzbBqDl/sfmAk4Irh6kVQTAp3UxPEihs8lAPSEbAVR/fYtPrhn0RGT5bo24sTt5sYA=
3. uZzTcR7Jf6FrW5Qfz33qME/IxWvoZtROFFX1Yr9CNNqDNAhdxG2A+/dK9jxqTzlCNJmPADJoA1N/8crqJV7w0eQtJr7ssbqkQ3r7sSux7taYJIOYhRrnHnj2Ksy4zZxLKbETxnv9x5O1xNLndHxzCFOXVZUxyBw5mltOoMJh72X/7lsUIKVrvBH7jUz6L1Rny+XbxuK2zGc=
4. 3YaGm3HeTaxfT3kjrXRI1Deb39azmSYRfAEYc8n9dLQS9u0bYpRN80b0eKzSPUv5GLkkCbkUkUbGKWNrJsncyLqQ3Zm56ZyC3y1qRBgtlzaRAIJUsEebecjQNZoaFzu94IGyi5mjFopAZYZzEWbjpZJjLGcnFdBKiqlrhXgCz2qUTq+jiXHltkwHRFTGWu27d/U/kh5WXBs=
5. 5lIVCZStD4jjvHM9lHNOma/celeiZZfrFKtlfuJ9KeJsvP/RXDeM6q3Bucj/bM3eeAzpY/wJkWX5nc1x7DvPfo0bniJsl+POUrPvqL55xT99zNlcd4l5D8UMLbEP50J7kpvAR+KqYrsJmSu0beuqsqDFYTk0JpENK5MtNmfrvl6XlYrf96JkaTdHmP2S+tfHAP5xyJfzE3I=

Question 17.21

For each of the following, identify the independent variable(s), the dependent variable(s), and the level of measurement (nominal, ordinal, scale).

1. 8JjM5AziN1YBEgrfCDdC9QP0YbZmAoV9PmLx6Og2AHEClqxOuIJ+TuPEv3hHk7EzyhyHMzAeg/qzBdhocEr8b4weRCnomyTM04Q1HZjT2DiCVxlSNUf8Iknhsvy5ociJk9ryhjN9YhcqubfWYVJGk3pd4UU6c6ff
2. ICs8DbMn223kierMmVUbJy/vt0YDw1jEMPSPawGrDG54ko02ov1aQQZYu59Zfw1odGFx3sVDskjOnpKCh2aAw+KrrVfJdleJ4x9G23aOjA1OVaaVkeTA6nnf8pslrVPsjkUQs/RqY5eYXwttWFe3DihsIDvooJY92kO4Zy6B0jML2bAb41kO1JXQW+gpXdFppQXtKBuMUisZtBP1grPGB9dDAJbqo+FvaLew97jihCO3njgnDAYG3p6fqw3x8FdIJpkt/eCmz7tnnnlsfo86tNS4dc3yPh4AVH9jSA==
3. Xc01gstEcnE1w7DHvwJeeM6kiM2hRqPotmTzsQ3SxvDmXgEE1G6+tDK2Kw7u1tJugF6AfkSk8Uw10igBHFLGI14EU58rcv75uz43iDRkRCZKo1vGKEkYTYZROK1+YjEJx4wufpqwpau4NCWwhkLSRyLttUT1+wkfzZjmVUdALsAWQ8gID5K50fBZYLUwx4WzYDZ9PeP6/xSc7BzMweD9BoDTDpsxPgzc/mdl8o9o2snbAdIvSFlHONFE/aqtolPkyZ6n5TqcK2Sh9O137A0UaThkkZIyo63R+hfJrtBzcOIA3ha38FReZw/eXz8lxTMT4lyKzk7TarUs7jTpwut5VMN5yy9W21ZYEeQw5Itwhk2JUse38Xz/ARUSWzLP19e5ndHv0907MvVajN6X8vp4eptGQUBIqfXSRvJC3vZkabfq3g1DSBl7ELhAY+yi33U2dX/1U/Iv6YfR13z8

Question 17.22

Use this calculation table for the chi-square test for goodness of fit to complete this exercise.

Category	Observed (O)	Expected (E)	O − E	(O − E)2
1	48	60
2	46	30
3	6	10

1. A8Iy2UfcPI6QKbZDneL5Sr9aw/ZiPfqjCMZJ6lA8CTKhvvwiNwMnb77W02Zk1ftL6KXqXZ77Ui6+hzC9xOviD1OLYn0n8EAV4p/wVbi+vw0JPTbqCgfFXZcyVIIruvRPhmGEvJ9Mhvq4BR9Gg62usMDEGOsTrPNk/ssxpWrbiUC6ACw7oJzuPBJwx+c=
2. 2737yhYlWnrRHp5UBRxPH84js+r/vhX3rBNRt2voL1n092ioXHAKsqgxjWWzcAozPnBnR7v79I3OD/KrRoOleBilPL32gNJu
3. nx+pN/SnXeYqIrbxbUXILN6dFHskzI2mcueaoXY+zgfvt4ouCxYdYplTreUIgJh2XNB1ZqkokSBvDj1JISOJpvd88eUWfBUSRlndBUYPKHSVB7q1FLG7leANjkHOcb8A

Question 17.23

Use this calculation table for the chi-square test for goodness of fit to complete this exercise.

Category	Observed (O)	Expected (E)	O − E	(O − E)2
1	750	625
2	650	625
3	600	625
4	500	625

1. A8Iy2UfcPI6QKbZDneL5Sr9aw/ZiPfqjCMZJ6lA8CTKhvvwiNwMnb77W02Zk1ftL6KXqXZ77Ui6+hzC9xOviD1OLYn0n8EAV4p/wVbi+vw0JPTbqCgfFXZcyVIIruvRPhmGEvJ9Mhvq4BR9Gg62usMDEGOsTrPNk/ssxpWrbiUC6ACw7oJzuPBJwx+c=
2. 2737yhYlWnrRHp5UBRxPH84js+r/vhX3rBNRt2voL1n092ioXHAKsqgxjWWzcAozPnBnR7v79I3OD/KrRoOleBilPL32gNJu
3. nx+pN/SnXeYqIrbxbUXILN6dFHskzI2mcueaoXY+zgfvt4ouCxYdYplTreUIgJh2XNB1ZqkokSBvDj1JISOJpvd88eUWfBUSRlndBUYPKHSVB7q1FLG7leANjkHOcb8A

Question 17.24

Below are some data to use in a chi-square test for independence.

1. EO9pz6LHE6U4ShbQdLtNG3LeSVdgIWMN/QQPtRI9x2uJReHKFyTKAcLOeHAGLyFoEOREffuIyzF9jh+lz/5nAQ==
2. 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
3. sCZxDB1Q/RhnfcADkukdEYYSsVMDR6aWfbVmtDcZ70QAeaERd/lIQhSZMw7K770z
4. 1h2jTZTUEGzQs/w50FEOMx3utjBvCQAwTkthSHFvRQ3qz8EJAv6zEW4w8ENXbaDE/6W6ByCxVGv4E1H7V38FfhIWzzs=
5. 0AFpHuhqorWpdHyWEMupidEtp3+R5k7iVy1e3aLSzZ/dZ88XMcyEOD14K66pZrTIE1omr/NDp1KY84jlsG1yiutwoS87yWIA5pX5QVQI0UMgtTW8dpCqlCdI+o0=

Question 17.25

Question 17.26

Question 17.27

Gender, salary negotiation, and chi square: Researchers investigated whether or not language in job postings affected the likelihood that women and men would negotiate regarding salary (Leibbrandt & List, 2012). Some job postings clearly indicated that the salary was negotiable, and others contained no such statement. The postings were otherwise identical. The researchers examined the behavior of almost 2500 applicants for one of the jobs in these advertisements. The graph on the next page shows the proportions of women and men who negotiated in response to either type of listing.

1. 663r2cq/KWRQiMA/Hd7crr5RLPTFC6OqQjkSSkkQT9+mRn5vnWbiduL/trcXzLlqpKfiV7bgR2E0c5iBPokP/FtBfTnoJsoHs6tpswR3+zNjv4LNwbXqqXaeQSysWsDyQLYIpKxbpK3QFup8geOKo8s2L+c=
2. RuXDqF7WjWhBvk8Ba54fiakv93MjYbNBDpzKcWeMjE57HYKi8ON7H3rcboTsoevnZicdWUV44ZFlkhCfa0uiPSk4niDwggnbkpIPZfcFFLWn6KiUtAv/JQ5Fs4VCYDR8UODziav4upWmxT44FXZeXFJaIkmU4s8LPJH6vU67Z+LCAonMeoo00MsUReiDUqPWsA7NtbK9jQpksRqiqVUjG34we8LL1EI8gr4VvT9i1DPKd5SLcKcdTnnMWJeKLbUZmB+QUUAz8ILRqxzs
3. k/moKEkmJR2nF1m8UeGkS1oWpz5vPidcr/yHuYSNnDuUUuHrRIhCK4Nd4nAacREjUfQLJ/dNQUIJ0xaBfqsHQrFG9aCC0kuOeEvl4gURF4EkLrfPT0aS+JjUVmY=

Question 17.28

Gender, the Oscars, and nonparametric tests: In 2010, Sandra Bullock won an Academy Award for best actress. Shortly thereafter, she discovered that her husband was cheating on her. Headlines erupted about a supposed Oscar curse that befalls women, and many in the media wondered whether ambitious women— whether actors or corporate leaders—are more likely than ambitious men to run the risk of ruining their family lives. Reporters breathlessly listed female actors who were divorced within a couple of years of winning an Oscar—Julia Roberts, Helen Hunt, Kate Winslet, Halle Berry, and Reese Witherspoon among them.

1. W69kiAwhjLER1NXxG1oZtPJvhgv3CK+5B3958aWvD9wuLoMrU41wCaD2qB2cQ2BIiOlD0aUKEvudrvaKlRvhRPgihWr9dcPzyS9dxOZunhEYKXyuQvyt9fqZt7JD+ZZAtfnHRR3F+Wc4XaXAFoB3acsfJrmUJBvkAc3PB2e1ym3CS0dpebRz0O3I/9BY9hufdfaYh2IWKbYeDRQounWu7FOUXHm4BTI+sC4W5POVWbhc9j0dZGkLX8pJtIle6ZUH7gKUy0vjWyOs83AGQrxnLsqhW1e3hsydiX9GpLSUl1cgI0YH7T/gEh7dP370w+vsJkgxwaswzGXK+DXyuz0Of1P9iKa8BdSd0Sl29eC4rZ9es32g
2. KgxHJBrpQrRjZg4k+mtfvnksQXGmtJ4KYQ26alSSwMrco0WlK+GCvlQt8xNFJPxXxkYaALOXGN6077V88dd4LuPlShDjPJAB+d/T62ylf4QiGa3JrwD8MAGr/teqk1eI7mjF5TA/eWXCU6WyCn5oW0Sy/iExTe2gmkihS3QCNjDcKXA9M48qWAL5iUiDaxYF0XqM5lgCF1tlsiQusXHn8cshwmI0P6wAKz9ewZeumWhtZPJhubxgFBUocMtLB/kGIfYTQQcuDEX717VpbBgPO5OZ/MSeSUT3FgohDE1Tm4S+hi1sRyAl/4BoAWjHyEvB8Hq/ee0weRmMv6yq7HnQGoB+vOf5yjPTvfNy5LECc2jPvDJCl1k5fnkhYVTR0W7p8Fgp1VtR1ZLBaoiFe3XNCIDnC6RCGLsFQJdA5SqSAMPNYZZ/P28WDl69stQhWJ/JIRU8NsHnQrxudWY85sGSKOWjYU0Gf0Vs0mxzY4fWYtyfmFsHRupSK8FMMhaAiBfPud3BrL7KPZJ+IutaPRF7ZST0rlALPV9tlvP5eXdVnEp0bGupYguXSi3doopaMDdAN0p12JicW2ns8QUWNLuhnlAyC+hqU0KPHKDifMdCiLcF94jWmuzWjfiTLaLHAs2R/1BTy3MCCNtfq4xsTDRfZ3pjdxvI3rNbY1Am7A==
3. 5WSNRRaCeDeH+YGb720JjfcarMMdG93SdYP+pcIw7q/uWiY5yRYMo4trFfmLKh2HCKsnEXqf5652kwUA/5xmLFT6b+ckuCO8azBVzdm6q1dOWRor34ntX7hkOZKWFVrn7BFBftIVusnJHGGaC7wVDs5WqAPyhympcB4nbDsswgx+J2Ru9WiF+9GIbo0RfTS3FhBSh+oxw5I=

Question 17.29

Parametric or nonparametric test? For each of the following research questions, state whether a parametric or nonparametric hypothesis test is more appropriate. Explain your answers.

1. moDfKMPH4a+a1H6CaboSyAYFpREoiCNJzMj8oqJahkTJhDtXZoXZHajFzb6kXNZrqrQDEyPxeyypXRi5GClbUHX6+brNzh8QAn3p50xDL7o=
2. /J+F8jRy6Hu3JnZu2dF/VKKtYig6mrS1lcSyBSYTQVUVefj/q23DwlWhGKJh6Nh7jQj5hOHvf7E62kW8uFZq/PpklQpdd8F7RoCaQCxyHMQSHjfwEYP9DYXCSe7qkOJGrP/caHdCINZ79krsK2L9zRWKo6URDITN6W6k4XMZxiAfprDs1ZIvgj1Bv+04EOJM5ftOZtm1QAyhE7mI4Al5xc+gXmCRcjiC
3. IBmiLnrgm0D0YW4XeAPBtPkzza0yr5rmSi62tjIQcQBA1CvBQ+ERDPODyQ1KbYxKOPNJYTtdQxQj5uTGNc3WmiHWqnnhdi8J3fzyQ2drbU61EFIv1n0hfKQ1ZMvC/BTiQIZ3UQ+JvZfSFSR/
4. ul1CxvUzLHalQmnAoLWMCuJMSeKsZhaoNEWAPULdR6cBOR1okI2fOOaAne+j3gV9bzndtR5cWLk8vC2RblInw4s4/gzlV4GhIponJViAufFO9+pKc8RcvUupCoPCoc48olko5w==
5. OtONCNpksYrfsuxFu67FLwSrWi9O/297rZ/C5rDMOgDNyU/MSVDc3paCPSO4+JJ9Uf1pMIPIPHAgepjR8Lqz5b+/2UxkDubV4PGaDP1A8yHfK5vShwLuhh+IP5NuY6Hfk1UaPg1gUDXUcDhX5frSPhj0INu2u5eQ5c5rN1rBaJrFvEzFds9/NbrwKYvT1s1sT8Cl7DZXlZTMscu5ijfNYGw8RAWB2FoTUCGudEAZjQNn9Q5+o0NU3DG/GaFk48Fhqi2LkN67NTptVebMuJS6VViLWyZVNpSrxvof9ApRmz1mZ5Y2mBi4HDUc3rS6Kk+L4f715EeMr2YRSvRbqqLwIIFJX+cKJEJ1wQC2hKPd0wbRJu5UUZIcFMDKhBY=
6. 5RL+2uvtThy7okSpdet1DTZmAdLxHLbWWb491L8dX4L5v6mLU4LfT1oMSzdLHgM+5EW5EQGmOSs/SCb+daKBZ0yJ0aonCjjVBKgBDbKPmNjzaq3J3FbCkbbSf0/Q6x8DDcRfrvbowpnDZbGt0p1XIZw7gBaZCe8VjQ6oIfDdTPIBPvc5NKcQrJuCY0O5muFcuIvWz8CRxra8LN4Vm3bnBJT9hx2IBaFnl+A9m8Esx4dRe2EeWS5/pM2OedJmNjzFOw5yjyxme9kyhQ7IQZxpzi3l2aGjvj+YfaRcvQEg3tz5M0uQainP0ORzJxSJ1SVQvQSM0CJYo8vohyl1EwBPeQ==

Question 17.30

Types of variables and student evaluations of professors: Weinberg, Fleisher, and Hashimoto (2007) studied almost 50,000 students’ evaluations of their professors in nearly 400 economics courses at the Ohio State University over a 10-year period. For each of their findings, outlined below, state (i) the independent variable or variables, and, where appropriate, their levels; (ii) the dependent variable(s); and (iii) which category of research design is being used:

Explain your answer to part (iii).

1. LLDjTMAX9K5hGq9SryFL4Z/Z1VDVlhVGcM8CilUi2Vfqin1/j4mPwrcsxAd108mUzcCk4UJsAvNte3KNdlins2XRBORCFCixHyKg8Yf3SumDP9rnDR65cS6X08d66oi43MH7UuL9yU1n8Od1a8c/9353YRvrTHh9TRcO3YO9jQuUWsJBrkZz1M/OOrx3/AlDZ3Yoe1/Hh3/nLMcXZvX6RQ==
2. 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
3. kxzAbbpFMo2DF65HLzuUeIrjLQFfV6MdzxV8rEl530jqg5tjxxa80xKzzAKUwdYpdmu4hwxjH2IY/hqfd/Fihg/SQVdKJgdREFpKIBYFNDlojqlAlAw3LCVkBFVMmGuguUvC0whhGsMFnhnOziKOaSjrMEnGwSXJ/WZKrdN8gwmFs1QBbTX08xj7iTLEqtZ1fwKZxmOT/kr4e7u9ARFmqQ==
4. XwBDXyebs14jjq6rHuwI77JwoNyclaIZKsHaSbK/LNDorazykEIy5oLMGLMosiC8X3pduCHUetYbJnhElFKvurlRoXD7GhFFw274xy35klcm73tvuicPeLfuaxaOIp6Cjlnj2M+7y3+/6q2GtDrR86UuxFXJfjPhDPCJGI0P/0dNdC18lOs45+/CrC8ekH6ooqA40MTMWBWOTFpNzmkznt0Kc84DyiGmdqfdMbMASsXealyvuFxX1OVbQDIipI3t+PwATnt/A+20JdsQmWNjKsoRy6SkvKJDH9bCjyZ0la3/6Fy8yYF3lG6UZqC3owKYgAe164RIE3vpTiWc
5. 152GHD3ymd6Wq/4+u6bvL/MnJMMfLIH4uUD3dM9F/mT36Fwahe0F7xRq0N6td42JIFhytqOs+d0wBY/q1QWvlZJGCtZ5ZlC3SkmcnwYTWCUiEikCiCHe6AxKSW0GFJwPISmUIvAmtvtJ1jo6n2RqeeC1hLc48qSv1oUsG1tsOxxaNTU7majG2K+Kh6nEVtfUi1Hcst42iqEssGxNLSvyqqfeBWyM9KxycLytjvanaNkTrqDtRvHJrG3mYwAfDLN8vLBAYq+qbiBbc3ry/KXS8ZXJc1CLIELxuY8pa64CjmbvfUx8srwU5hX9wUN0Cx2hCIxS0qeMYvPs3yZeJledyfI+ELK8j6NewvivEjKN0DllyKCZno+xZLAaMM/a6nr9LKBYovT2LtwtMoB7LakHjQh7HKbAGy7/BE07zwWpQoEmhaCFdjVZ5b1ueZSdaNj7BxeTDuJ9T1FebJrt++PN6xND4uXtHRwCEAOzok100sXxPrW1ij4GIymLspTdmza6v2esJUTQPNtHaxVAav53Vedh+nYVQ/7p/ghvJ/R842N+1kIf2acvlkAC2HLmIHZwR4Dt4VuINTO0zWUl18HkbTUcZe9QjCvhXPqxFteKEBDC3QH367rMOjxCuboj82l14KbuG+wyTwaFeM+V/ZSBlTi0S22HSPQ740WTJO9R4PU=
6. xoX6nmvKGQdUwUsJIr4E5818AReJmSlipX+iFzxHu9lzYQ/8xVl1UObUB8QXbAJ6RoMd43ju3b6Tw2iVthD5Aq3PRo/0X8aKx4e/P1PEG1Q7NssT88FcjwikeLmUdUO53y3/LlLNfDW+GO7mYOllxdVodPqdNIc/UBaxRm6n506n1uXRrjaeraFGOLQLM+p2WoShvhLlTIgh7jthVcRd3LGI6DHhsVoUPaP2qFIfTIfwV4kryvsizL1QsQDnpjiYbB8rvFN+pz7povSr1iqEFvfBHJEgmQNJj37jO7oePG0MSlqwgRUGUulUdZLzMS/ZtQpy/FQb7/8+92kTzeM18zAG8QrKygz+YI3nokVEpVXjZEgf7lQc+OoUUcExZ2XMLWHL/kx8QqwRcCdhSoJDk5eqTzlVNmNDP4ArRGCbG6DGFr6LMrPy+k5MxzDpI54+0QzZoCDyqbIPkYVjpGH1fuHwQNM=

Question 17.31

Grade inflation and types of variables: A New York Times article on grade inflation reported several findings related to a tendency for average grades to rise over the years and a tendency for the top-ranked institutions to give the highest average grades (Archibold, 1998). For each of the findings outlined below, state (i) the independent variable or variables, and, where appropriate, their levels; (ii) the dependent variable(s); and (iii) which category of research design is being used:

Explain your answer to part (iii).

1. DH3UrMpnj0UPxMP8gOyhKjfyi+e6+6PQS/dMv9ev0tFzkK0oTr/lsk5s5+SYOGmevyDKZ1xnwoUd1KMfve07TvwjKUGL9yG+VJuYhau1kNcXrwONnKPLeFqPZwbNz7W6GAIY9ZMQSntT5vO2aaHTh5JJwgXpOitUXHBDhCL9h//1Elt4shtOH8uX7xR934VffheTKXzqGi8UVEG6k1Y7dWpDN1fhgRW9
2. dt3ydI82OsIW24JfPpQs6MM0Xah4P+iXqPHXOV2BQn32aptsam7uHxF6r1POy7HHPp9d9iuSfn1+/eLC/YQDQo/wFlq1dibnUSYr18WgEO/uSepQrEP6Ax+0UypLbDSeWgfK/cDudtlEB4wA+uT30VKfQQMLOX3qWq8OAJMYOISV2IpQim0g8fQRJgtKf/9X8A7CYkHDnGel5MKwxTFBtP0y90NnI2+FPgkoRwOj6tF8IyZ3/YRl4if8NA8TsllrHbcgFbGiWRmoFsn9TZvgPth0pxlt19JdOUKjA8veG1VPilEP138KMc8LTmUVSnjqfde+iptqgFBfgI8vpZvlEfOY+mk=
3. jaKX3/wzfxfnKF8vBgm7XqgvkVzlysmVSREjaDWH4KYYkIqQ7GV/gzQLuHvXpfIoU7uRYk5a/xn9eOpwb08nD4KOvUjM6S0ITXMzVcuFFQm6Rtgc56crmF5/Q2aCoANkll8EE77Ii/6HBsvHZFZoHy/XZjAGydZdu8TDLWcnktWbTafCam8G5SoWPIFQq0ySFm/HkVCXCyFpkCquUVTSb1kk5ncP8Lslz9OxmZm4IWeKIstyZn3MJPiaiPkwpfBY

Question 17.32

High school academic performance and types of variables: Here are three ways to assess one’s performance in high school: (1) GPA at graduation, (2) whether one graduated with honors (as indicated by graduating with a GPA of at least 3.5), and (3) class rank at graduation. For example, Abdul had a 3.98 GPA, graduated with honors, and was ranked 10th in his class.

1. TH+PIm/Ov/G4OqsP39FipF4M2qR8stC4/xHoZTePRL29P7g2I6yl5FuhIoAkaq6PPle/GSzjt+vzYhnb3HfRdSrfvmrb8XbN7+PNCT2Qwpri7dCrbh0HGPI9Xcc=
2. ySv6Gwv4YqtX1h+mAFeVan8srN+E5oWJ+cuv/s4q8vVhwIrVNZy3HQgpy6FkmAPArXnEMUAivubAlBSIXvgkH2cVX2JVjHnBrJY05OPKVs7DbTKLeuat7w==
3. KC1nJ/cdDCKnN7L9u8Tys2q/DDh1YNyhRpNrHSOsNP8O9gAgFu5J7+/l1whxNwbI/FwpHDkj0CtXariAShUqR82W/4yJkGrT
4. tLxcMvpKTtJ1mA2sjjXERcZuY8LdUuGRhxxbee20SWgg/awemxrtfpOHmOuIq7rbhyzGLIcQ/FTIV/AqT0yG9lHXJozShXBsKZjs6F10tuXHuNW+ozXNAcOF5c9oE21VpJLxeg==
5. f1YPG/88FyLxGUHGZ9XoRLmxUqyWMHn13XDQ8xmbmru2NF4sbm3eabFvbt5ESek2J5ZuA8KLvN7vjF+D4XxW+bCUzMwxB1XXfVrEhBtxnd09nZ7zlFBL7M739YZ8Ti5ZLsQuk4SMslUzN2w/U1V3o60hx5XSLqQHO6sxJ04ptl/gTiRkXu1oJRiVRkkDP37ZA3+B59A/0DM6/Qzv2lQDDu03cu8N3/HX/MQmgF3gJgfi2nMH

Question 17.33

Immigration, crime, and research design: “Do Immigrants Make Us Safer?” asked the title of a New York Times Magazine article (Press, 2006). The article reported findings from several U.S.-based studies, including several conducted by Harvard sociologist Robert Sampson in Chicago. For each of the following findings, draw the table of cells that would comprise the research design. Include the labels for each row and column.

1. H5aTVRwHNZtg8LPs+xMRq+BiXfWQ6KJitnSfIXlylazukGREvijwbW8Ai9ZRZQe/GGFmg0XhO38rqCz5ZKdD9kOZBW/Y+Hl15yFJpoLvkhYLk2BpSqt27cE/mut+qYSHYMgtMin1uiI=
2. zyew0XBz9j8vdt5x9eC9jyDrQ3n7F9KG0hs/0fS1pF0WO3kac/IIW6XQXSWA+d1pf02lmK6QOpHlzgZThMZ23TEL0Fv1j/UP5H2/TqcCOnp+NbwWf+ySI6zgR5uOoZFfPIht9QDabhA/r8kODG2uhkU8fvj6W0AbmmsbUKmAZJNU5mKPK0XhzT4sR3Ox5848QS2GmVhr1nMt+nZRKhbBEa8kNCZJ5NdkxGJn9JO9h+k0SpZyy2ioBf2+n7YEPaXjJWR065ho97asETC1UuQ018VXo+eLKB8YLC/cHOrdPM5HOdjDEOTJdkfdTN+ctRdsNP0R0vBMHwkNy9yTGi1bQoePKWIXMIfp3V6K/k+QBK1MuIyLg2SJN/OTKx4wPns3G76yglTvmWlEtXKE
3. 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

Question 17.34

Sex selection and hypothesis testing: Across all of India, there are only 933 girls for every 1000 boys (Lloyd, 2006), evidence of a bias that leads many parents to illegally select for boys or to kill their infant girls. (Note that this translates into a proportion of girls of 0.483.) In Punjab, a region of India in which residents tend to be more educated than in other regions, there are only 798 girls for every 1000 boys. Assume that you are a researcher interested in whether sex selection is more or less prevalent in educated regions of India, and that 1798 children from Punjab constitute the entire sample. (Hint: You will use the proportions from the national database for comparison.)

1. naRl9pKBR9eacAoNbP8KIK8P+1nZJlN9jAjXDH47Tjcit82bzlzNhSKqGG2BHWQhwsLdRyM+DRHgHjtuhpRM79a/qDNhTCWdwEfE6O8k8cgHHCC7TxgCy0RiPozYa8a5JU4kvTk80vphm6BTOMe+Ww==
2. zoXcTNicX8QeE3BMwHS9uRdpbR5UhaCaD23INGFmT3syN/SjVoXi3J7dU2mtusdls6pxbZ0NGrjcUYDgUrZ/D125z8d/trL34xUzH27lr27c71Yd1hlJCQqfxvAW2pek
3. rP8wUTmW78S7SRMg6f0a2991kMxNd5z4zZlbgdGoO0P671DHXik6O8YSNa03nyzM82ABNcwVZREa40Df+Z5w+ZW26Ymvn1NzDmx7SJKRlem1t44bNrtotOo5fLJ5+YYbSOLsj1Za5+pePNfe6lO14Ix095V8avAv6oIDgQSvDn3Gf89uUM9xyKraTzklfBpKdVcGF0WESdMhmJEy814YtoDf2ozPc/PydY9OMujtFUlSe5C52RYh+KyVUXkQiJtKGfbF3AMkRKn4CGuC
4. DZKfNPtlKJ0+BroVpfah5gIaC2uZBYxw43AgSr8NNS6QCEMw6XbZ5Q1P5Wdyq40wgC02mHVpADrYmYLwBmZnx2XtwuyTEmCE

Question 17.35

Gender, op-ed writers, and hypothesis testing: Richards (2006) reported data from a study by the American Prospect on the genders of op-ed writers who addressed the topic of abortion in the New York Times. Over a 2-year period, the American Prospect counted 124 articles that discussed abortion (from a wide range of political and ideological perspectives). Of these, just 21 were written by women.

1. naRl9pKBR9eacAoNbP8KIK8P+1nZJlN9jAjXDH47Tjcit82bzlzNhSKqGG2BHWQhwsLdRyM+DRHgHjtuhpRM79a/qDNhTCWdwEfE6O8k8cgHHCC7TxgCy0RiPozYa8a5JU4kvTk80vphm6BTOMe+Ww==
2. zoXcTNicX8QeE3BMwHS9uRdpbR5UhaCaD23INGFmT3syN/SjVoXi3J7dU2mtusdls6pxbZ0NGrjcUYDgUrZ/D125z8d/trL34xUzH27lr27c71Yd1hlJCQqfxvAW2pek
3. CV4Q1X/e+ZG/u+0wy4QJ+NROpCFePDRoS1F/X05NL29D8bKdFfoIh/6fVAExcNZ4GpZpv6J0rZ6PZAA/6Noydq8s8CB5jBVQA3izpReER18=
4. DZKfNPtlKJ0+BroVpfah5gIaC2uZBYxw43AgSr8NNS6QCEMw6XbZ5Q1P5Wdyq40wgC02mHVpADrYmYLwBmZnx2XtwuyTEmCE

490

Question 17.36

Romantic music, behavior, chi square, and effect size: Guéguen, Jacob, and Lamy (2010) investigated whether exposure to romantic music affects dating behavior. The participants, young, single French women, waited for the experiment to start in a room in which songs with either romantic lyrics or neutral lyrics were playing. After a few minutes, each woman who participated completed a marketing survey administered by a young male confederate. During a break, the confederate asked the participant for her phone number. Of the women who listened to romantic music, 52.2% (23 out of 44) gave him her phone number, whereas 27.9% (12 out of 43) of the women who listened to neutral music did so. The researchers conducted a chi-square test for independence, and found the following results: (χ² (1, N = 83) = 5.37, p < .02)

1. OXuVZQAlpz7NGToN+B2p3qY2dqgd6ShlG74VuVG7YnzCIw8X50IrTVIl6eVIvfU+r+HMYTrOUiDmiG1OwXn3x2rbczB7qoAC+7kAlA==
2. LWFnk2wLUGkxESJT+gG2lvLoUkWHtGlk2OFyHah0kGoptVMxmEYPzi1rSrthc9nnWWLw//+D7n9xV+dp1gBtIovu5joDzyWRfMiCnWYlr/ZIDBuHIQzdXUlbCYMhc9/OEoZ9h3aVtZjyRUycpvwfWKHbcbSJDgALoTLHbpLSzu8v/R4Bxw7LvHH5XtSZgz0eiHKBc8Wda0mIz/Cfia1hivA5HhlCSj61AnUC2hBEdMZ/v9KBdMFPcXqstc4ZmbuO

Question 17.37

The General Social Survey, an exciting life, and relative risk: In How It Works 17.2, we walked through a chi-square test for independence using two items from the General Social Survey (GSS)—LIFE and MOBILE16. Use these data to answer the following questions.

1. Sb3QYoAhwRmxOzp4l7knFOXyF720+wobu5fc6auuitrs3Z8fw2OPsnHph0RFyokdLHiFQeo/6vPtBf6IU8DW2LIo11xHMT21G4efS/uuk2XxVeGE4KlchoKCTJ5EhgbyPqsNn3vkyjvmpiz3UIWFAIrz6KQANiLFvd9nw3YCBzc0yCASc6k+JVFp8BBVdVAXUBxTD8gkQw6FZIfr54kfy/9vOXG1kw5Q08Bh1feCfMA8pgeoqu/wdyo5Gt1DN2kL74ElkWMCkETv2hu3uOXmDa1VC5yj+iMIVQcg7HpIcQQAEdpbUOzPi8H6FdhGYJajZTpdhtKWbJ55lPFNqLSbsQ==
2. mJnwyU3WpMRL548xA8EsAy50d/GUwGWj4tlF9jwqgtrp6OAYZDMFsJnOskUqd7b5mXDb6LTRVkTIPETCI2+qL/axzsqBVCuhs0S5kNgd3GI=
3. pECiqO+7zybSvDGdZsn5fO+dqgzKOtSyT3Do30DiTSecFGLnVKsUKgXj2aMzN82equTqYwxyUrx8b6LGd+HmSxicrP026OiPMk/Dy8u0mNOF+70IRr4tAV5fMQcg7c3AVgCNj57Rbh+j6Frsh2LMVayVx6lRAs6+krsK2dEILf16Y4FrrqQVsDbougfimpI/YjfYqXXvjNNfdq98APJ2aaIJEcorZW56s4m6HEzKFRowMZVAiO2Ad6Wt9u4=

Question 17.38

Gender, ESPN, and chi square: Many of the numbers we see in the news could be analyzed with chi square. The feminist blog Culturally Disoriented examined the photos in the 2012 “body issue” of ESPN The Magazine—the publication’s annual spread of photographs of nude athletes. The blogger reported: “Female athlete after female athlete was photographed not as a talented, powerful sportswoman, but as…eye candy” (http://culturallydisoriented.wordpress.com/2012/07/12/the-bodies-we-want-female-athletes-in-espn-magazines-body-issue/). The blogger reported that there were 19 photos of male athletes and 17 of female athletes. Of these, 15 of the men were in active poses and 9 of the women were in active poses. Active poses were typically those in which they were engaged in their sport, whereas the passive photos looked more like a modeling shoot—“where they’re just looking hot for the camera,” in the words of Culturally Disoriented.

1. 7FmZF0jTxfC/NVd/z3NY8cB0xlnRiDV3YLxY65cMkhKqAjDM5jRshtQHnGGW75WG20xX4nbDl0IQUfOlM5scn1NRjHqO61bDLJCDUlsPE1m2gRWuQeqW8KnFrami600TxkJ8mdu3Z2lqN6Gf85Isq2s97/JayWKv4IqREgMdOtMigbnhNdi2VgkqZ69tLAWRBGlOzdQV+UNXCazRBdfKZJmbuSBYri1ZN52Ee3g+tugDRfn3qmrSx9I7+P703gSG7r7hisZRAknEpu3yPEJDHAuLJRA87M0otWBr8YOQ3c1Csrix7Ft6Ts+3SaAf7OvUrvzf7cT4bZ4=
2. CV4Q1X/e+ZG/u+0wy4QJ+NROpCFePDRoS1F/X05NL29D8bKdFfoIh/6fVAExcNZ4GpZpv6J0rZ6PZAA/6Noydq8s8CB5jBVQA3izpReER18=
3. PrV6rUBJ5r7lcFXd89yIsbXoXlNs7DF5ErF7JtaY63C88Vy5fIlEpTn0TB1s2jduVCzXQaBaCmutDGvBrBFvmB616XW2E/Ko1hDmt6HCI2xPzOeglAhQ/wo3X9m1X5d+aIjPIIZItvb7QU5EbTEJLpFeIUOOZe/LjgRRtYzoqHhQDQfB/FiShiS7S4jTQqglrXST27GsL3BkJaCjuknbdGtg1rQ=
4. DZKfNPtlKJ0+BroVpfah5gIaC2uZBYxw43AgSr8NNS6QCEMw6XbZ5Q1P5Wdyq40wgC02mHVpADrYmYLwBmZnx2XtwuyTEmCE
5. RQsj+fnq6Ilpagk63itcbAXABQTUC7DywbU1g9doNJsKL5LE7km+jyESkZy1sD/L0hgtlJ8FgjxfGgiHLUOYcV7lo8D3wEC6uKEfbQlMJX4te7yYqaJu0hSOr8ywhoGjI+aPELJHRa0ZxhcHlYuY3dmWgLPIde8jF0H36ohYvQ1XYZarxRrlMen7fCBiAiW2I1gkN7227GV4VSOvOHXgMsrisq+wQ3EvFwr5c7AuPmbz7BvwY0+J1D/kd3dn5KtxmwfVm6AlvBUJRmkJLfBKOGFI8K7t1Wp+Xz/4fRU+BwmPG+C0qTXSKizCmzH8sXTK
6. mJnwyU3WpMRL548xA8EsAy50d/GUwGWj4tlF9jwqgtrp6OAYZDMFsJnOskUqd7b5mXDb6LTRVkTIPETCI2+qL/axzsqBVCuhs0S5kNgd3GI=
7. eiGTR2uqVCsV3zvrIrXktKct9DIETNFXqp3HaJyNPYIiTSv9L5Nw0vyH8fhXu+/P3jqVrd37Iuhr2PepEkIbWKleZndeReE0iUuNpbQ1voh5obVceS0sd1qt4x3Iwy3o8eDt0urv2IGXgOUiCSOxLHVN0FiowQ/XhHVVp7E/C3KfFUVMs/dclF3NvGATNUO9J+Gf0MFMYh8JgJih

Question 17.39

Premarital doubts, divorce, and chi square: In an article titled “Do Cold Feet Warn of Trouble Ahead?”, researchers studied 464 married heterosexual spouses to determine whether or not doubts before marriage were predictive of marital troubles, and divorce, later on (Lavner, Karney, & Bradbury, 2012). The following is an excerpt from the results section of their paper: “For husbands, 9% of those who reported not having premarital doubts divorced by four years (n = 10 of 117) compared with 14% of those who did report premarital doubts (n = 15 of 106); these groups did not differ significantly, χ² (1, n = 223) = 1.76, p > .10. Among wives, 8% of those who reported not having premarital doubts divorced by four years (n = 11 of 141) compared with 19% of those who did report premarital doubts (n = 16 of 84). Chi-square analyses indicated that these rates differed significantly, χ² (1, n = 225) = 6.31, p < .05.”

1. X1qagsWZV8b4oO9n7TaiqRtiRYa+m6UL0/d4ylcJu8eCxSkkt2maZF6gA53IqB/Ytg1Krdh0DfEFK5Yi2jrP+Yy6Ea5447psqgcZDO7cI7w=
2. Q/71Rd8+mHvH0ARmfBlX5iUtjE/Px31m6nQVhNMULSIED0pJrvy1/2pnYIlphXkpw/6vK1ZwNrabo6BMInCROcPve5zUmRzai/XdIrj4qf8jMUxw8xik4W1atrVvCTyAKXZwvySavWdmeSJP5/OGNTwNcb6pNEbLcnBT69kinkQlN6ZfEEK4fPaiswO3Zv8dhh3wqn0fzOmcdzZQ1RM8JUYGU9YOByBmE8NmWU5e4O8oS10P5ptfhPwvqiWY9wV+Ol1pvjWGYC+LMCOJn7uL4QxFak50k7eLOXHNzQ==
3. GkgdMHFt12QjYS8fmBZXxf9KVmSV9B7OoSVuLLrtfnz48xF78vT792Ftbgc8jvtJzZ/Wk+kOiLRDqP45D6J9wcR689TmaZwNXZP3BtzfpOqJly9ScVpc7oEx2BY=
4. Tw/mOxBLP6U3wzOCtGmMc9UlE2h2T8KvKFNbnlFVheHPGiOsd2N2ycRM1q8f3KVfI+pysoGAZ6XGdtzgBou0ZioNa2pnie4/

Question 17.40

Police lineups, SPSS, and adjusted standardized residual: In Check Your Learning 17-8, we introduced the example of the Chicago Police Department’s study of lineups. Below is a printout from SPSS software that depicts the data for the six cells.

1. i8nlg9ogx6ZChNQtchZvSuUuLtXF6rQ250URYpJrNeA6e2w/Y/TVt3n1DOjQoZZD/dWNM34ZN9sXZ/FOVks3X0UdUMZp6Ibak3F5RUNJkBowtlNrN6Ko7wcVqqNt1gjAImy4XOepllesSvsg
2. uqTtX1fla0bsaswp8wie1mQWRAQ/sya3zQguIhul58yNKVe4DxjNKjPE34Risf2/L3EwF9c8sDBBp/ZhEAdye1IYlXotQVRvFVsvcRGOX+cHNWaiovwhnsHRCMe/yywxyvk1K+Uak84t2bv/3SVcAhd7faJYMJCNhbrQHPH056cnxYE8
3. g0MitYgJEjL7Rd/tZdY2A4ZmxanLsVW7TQzCMlFiIBYahxiXXWMJgOiRb9yGob5Vtzzl/yp3MS2CfxfoUEEzczh/feDb6+17qQBFEz0NbUsOjgcXqy80Jm50mIVJoDi4RRISSS3I3JOFJHUB0en/9N7oDgNiQgaCimDRfO6cYpSArZdRSabBNuH9eutMoxMDPKgmF2H6pmKzKzbnLwwUSPlscv4=
4. QvqJDJwLjbQrS9/jR0nv/3tuOHQzQH9qovbOIk7k5ZnOgkkW/WwAN/y+GW5rwlYPcW+AH9jm/ZqS6myEZNLzcUlogzhgqjj9l5/MJEqPWShHMNyFdEaTossHqCwqdZXJTuxz4CgXaIJF92qouFBovj0UE0sM6+IVA/du7ZsYEt2pw365Wzis9P6LiuWizYLNCLTbTpaOdP+Y/UYlqNqRprwXh30I5ZLkOFa3kfup85VOG1B7os4R+xHQrl7uo/cnfyR3ybBvdcreHvIC6EYvTWm9aCYI2KhVANJWQwLviJbaPqYo62mRy+rda/XDcUPUYpK97zBzu3mfr2F8b/a+wAPJ+LrrdAFuK0SyJUKik8BLgzKrqQ6UyDcmgBwgT6cF
5. Mx8fg7G06MW9szkuuVVZrc6cQjVCu+2kvvZSX32Rwf0JphdOQbdKjUP3j4rnbC/hk9jKjylHB6stgfhOqGkupyL1sIvP2Yj35dmPJlanfJfPLpaz0ou6k2dVvHmcCrSNQwUq68a5W8HCYIQGfenBPg==

Question 17.41

Gender bias, poor growth, and hypothesis testing: Grimberg, Kutikov, and Cucchiara (2005) wondered whether gender biases were evident in referrals of children for poor growth. They believed that boys were more likely to be referred even when there was no problem—which is bad for boys because families of short boys might falsely view their height as a medical problem. They also believed that girls were less likely to be referred even when there was a problem—which is bad for girls because real problems might not be diagnosed and treated. They studied all new patients at the Children’s Hospital of Philadelphia Diagnostic and Research Growth Center who were referred for potential problems related to short stature. Of the 182 boys who were referred, 27 had an underlying medical problem, 86 did not but were below norms for their age, and 69 were of normal height according to growth charts. Of the 96 girls who were referred, 39 had an underlying medical problem, 38 did not but were below norms for their age, and 19 were of normal height according to growth charts.

1. naRl9pKBR9eacAoNbP8KIK8P+1nZJlN9jAjXDH47Tjcit82bzlzNhSKqGG2BHWQhwsLdRyM+DRHgHjtuhpRM79a/qDNhTCWdwEfE6O8k8cgHHCC7TxgCy0RiPozYa8a5JU4kvTk80vphm6BTOMe+Ww==
2. zoXcTNicX8QeE3BMwHS9uRdpbR5UhaCaD23INGFmT3syN/SjVoXi3J7dU2mtusdls6pxbZ0NGrjcUYDgUrZ/D125z8d/trL34xUzH27lr27c71Yd1hlJCQqfxvAW2pek
3. CV4Q1X/e+ZG/u+0wy4QJ+NROpCFePDRoS1F/X05NL29D8bKdFfoIh/6fVAExcNZ4GpZpv6J0rZ6PZAA/6Noydq8s8CB5jBVQA3izpReER18=
4. 8appu4Gx8v+3cp/QaSi+ge3RBWrM/CQ833YK2v28lQntUcDofRvk0jH9b4SmwgK2WrjI/nzXCLx5+nFGAG9qe7LaRcNW2z9AQ6WHF845HRzVI9oWeYQgl+hXlue1Kh1BKzyiDvYbau2iux9YaozBSA1GIfQLFvXtAXE89DrVhbg=
5. DZKfNPtlKJ0+BroVpfah5gIaC2uZBYxw43AgSr8NNS6QCEMw6XbZ5Q1P5Wdyq40wgC02mHVpADrYmYLwBmZnx2XtwuyTEmCE
6. lkYYmEfSZN2+6oSUiXYXBKPi71ZO7Q7Rf5KC4Kd0nHJJlOityK/FbodS54JPJJn21gl3bYh1P3L/VWDQYlD5WzMyzRd3ruomuV7CZqN87AIkMgOk+Nncs0J+BM8vYp9a
7. /dQrvtMGoEA70+VJx+QL1GWDrtJtXE0I1Oh9WCLQKglTzxcMXBU7V1xb7HUin9ocWFrPw5IE37EYWGfxpeefkm+nC3+axvexIQsJcSh57K9/JYO5eUbtKQ==
8. yln/IVoAtAsgjRQzfHLT70n1llM5nSXo9SALtKCyVEjZn7kw/9cu2/x2OE753bWTMp/r66QkXIvEIJfmQwsrjRLCvAe+oKJgkzhfmnQX6QCz7ZcjemawaLJYi726kJhuv+n8kMnkgjijGpPgORiTbuhXFcafIP6b78grwxS9F5QJ0/762rwCGni5XLtXCPWb+rt8bGF6CYLcRTYr5A4L4csgWUDKYYXwZDsHdGyn0Q5qjkbPneiaV0ylASwM1X7ByrhqIA==
9. dC4JqLyBfMUx+AGEIH6+maPxSkhOStt6kdnB+H8zwiNVG4/Oa5P+SRW+ZzZj+Dv1afK7szFOz8OueLC6p+RR1A==
10. 4IWcgNcMTDYGPrlVzga37p6h5brk0HtWj7gHngB5zUC1HX6fYlo7quqUqmVGUn0uUSd0oNiTYbHS/tmFdo/uWGKBmo4/nHE0EfEUDrGmOrhsFji4o/JsVpZEfkvg7KSdQwaJOENaumkmeVtY2wLSX48Q9x+1t8KbIQD18abHxJG9zsgJ
11. dC4JqLyBfMUx+AGEIH6+maPxSkhOStt6kdnB+H8zwiNVG4/Oa5P+SRW+ZzZj+Dv1afK7szFOz8OueLC6p+RR1A==
12. VpW2EAR+QVHZK2ARVZobaQ5nOS4zZA2yU2tK9ybM/hIR8s7PfDU9TAjSJSZOi2yCbzNZxAfmrSz7NTPeotjyxMxBzLAXMJV2tNRWSLL6Coj6r3ETRUIx8L15UtOYaY5JUM9kg6XZhzIn1Wm9LFuw547LGSsVH6uDX7oZfhD4CF0=
13. iR71/yjfIs0bGAHDadPoOw+S9WFAxF4xIDfL1MfdImRKOGwWMd/VSofgR4T60/4UClgRCnoQTUs4VBatEQrqQ+EGd4F40+wrCtjHArdn88QInLLWwUTSkacBX14XGBlfK4Zl+bjt61AlOdjwu9cuGyjoyasbdPVKIU7Kqmeray2JipQSrpJNwnFejTqxjVs+/cR1pRSkC/o5cYVY6GMWdOzfRPB1TF0m59cIUSbgM1aM4Ij1uWhTroLIm/yFw0RZihmDKhKW623rOcpzZpggBt8NqcomPZhSvWBTOWROppZCIcRZy2h1XjsvErkM0ZgHOELT1cawmrZW5QZ41SKay9QUcK/d7o2DdwBh66njZj4s3qZmDIF+KC8dwMR7aIwzyDKCIgFALzjRKViPZaFkFSAwWER0ZbWnkYvAqOBDtdfcVJ+pbrH1WvLsbYkv2VTxeaBmq3a7eVQ=
14. NSIpmmgArGXvVKnwefN8rteQIMl5WqkMSkYD5jLBD/BFKifr/WO1QAroEV+6KQj0R+wc7amiNwoasVHn5Wq8L2XjD0uP6x5sszXnW06vW2Sr1f7vIg3HIdC7yjWtVZ9VWTIjhSvUfr6vgtQsXi1xz3GW+wGWoDdHPH6Q6dhoixTeKq1GtMW0gcgXguUaG7MgsDBwxDSxvU6iLBhlQiRzj2ez2IRtDOCexfLT+c9Gw4ZqMg5s39ES1Fc5x42J0231049mN/4dayDXkAwOZFLz3C4ofK8x1qvDw05eqXXOFqMn4skJn2Rpp3wsIDJy4ulix0gAl6BIr2dX0AgFenKsMdh5WH60NRw8NhHXRkRubQVckPH3VFSO+X4XsRiwYuMvknu9tcXq9rfL+NKX18eVNSz9cgErFW2v7QKAX0FeGkXOYBkYa8acSM+Q4kGciiaqNm8kt7lM1r+shrfGuvZAqvQLGVkdSDjKBtHRqIS8WmNgJwdor+g9KNgkZd/gvW63nmZMhO3V1Gu5KftRCRKdjO0WhuwLTUGUfFsJs6ZYaHSK957/f9zWrOrlNhdsJf3XFoRfLf3NslQlBs0Oz+Pp3ELyw7/si7a5zx/7ew3aDzAecuVpvk9XFJ/WaKrLDMKidiNqgmL2TlYm9uZh7VtMUruSz/P1lyyyVvEz4GM9KxYtT0z5gxZRUl70wAwWESllc+quDrjYitXmfrwrRCNPqHEG/fj5s4OMNm0+uujWVNbp0mXk82kB6QCKPgJsPLc8BR7QFHE20jqG528tUcxQw2tEiGfgHxCwRzJf3l38spU=

492

Question 17.42

The prisoner’s dilemma, cross-cultural research, and hypothesis testing: In a classic prisoner’s dilemma game with money for prizes, players who cooperate with each other both earn good prizes. If, however, your opposing player cooperates but you do not (the term used is defect), you receive an even bigger payout and your opponent receives nothing. If you cooperate but your opposing player defects, he or she receives that bigger payout and you receive nothing. If you both defect, you each get a small prize. Because of this, most players of such games choose to defect, knowing that if they cooperate but their partners don’t, they won’t win anything. The strategies of U.S. and Chinese students were compared. The researchers hypothesized that those from the market economy (United States) would cooperate less (i.e., would defect more often) than would those from the nonmarket economy (China).

1. 28NsMKjW/N15fTnjv8uD7UmZzr8KfOy4iArtBH3cp6kTviJ4ZSGsgxalI0XtSDahfhYuNFEufaIC7mLFHWwWw0rAe9r+1PDXFjX5eKC4N9F3pHLBr3aADUI5tIGBvCzJ8A1TzRMDeqh0EnU5/B5sFQ==
2. zoXcTNicX8QeE3BMwHS9uRdpbR5UhaCaD23INGFmT3syN/SjVoXi3J7dU2mtusdls6pxbZ0NGrjcUYDgUrZ/D125z8d/trL34xUzH27lr27c71Yd1hlJCQqfxvAW2pek
3. Rd08IiMneIb6X0YoOE0xoHAvEYiZbUoptJlzEhRATJcCYNuh0pGis0B/VvxHf+6ajmCLZHhfCK8vMqLxfgZvPrdBMc8WNr352hVkwaFycLwDxDz8gj9aV4hi2SvNgJqf+py/ZQ==
4. 8appu4Gx8v+3cp/QaSi+ge3RBWrM/CQ833YK2v28lQntUcDofRvk0jH9b4SmwgK2WrjI/nzXCLx5+nFGAG9qe7LaRcNW2z9AQ6WHF845HRzVI9oWeYQgl+hXlue1Kh1BKzyiDvYbau2iux9YaozBSA1GIfQLFvXtAXE89DrVhbg=
5. DZKfNPtlKJ0+BroVpfah5gIaC2uZBYxw43AgSr8NNS6QCEMw6XbZ5Q1P5Wdyq40wgC02mHVpADrYmYLwBmZnx2XtwuyTEmCE
6. pnuWJtxLNhrH3Ery2FFND7qSs6jHuTJJCkmDTRt58h/q1ChOkLYCLFT3p5c1dpfeIfdF0jU+mQgqcsTNw9WKWvquhp3SRoKztxv1Oue2eL+PQ6F1Rdd5xHr6DmOFIxqGaZ3zfEbJQUusZPdUDsZpM4ZmTwPNSpYDMe0btd0g/GU=
7. WJYj8Zq6vOM1lAQ5/OL5vPNkbxC8AdIf16GNrdkZKjnt6rZXzZowXmVAYoaDRx8leNmKQScR0oz9ACAEBtlDZkxDhFn+FAMrFS2bcpz9Xk2aR37H1eM/GZOa7yc=
8. 2xaG3EdGS9BtGQ30uGwLS+ycSsKH0HjkkBXUOxxVPHpzUpK/eN51COSWeGm8hqMeBIK+UhK7iKoG+lsx3q5iS9zZbADzweh2Mq5GpeDJMe+FNRZOpEwdsH3Akbo36kjvT3kEUbK6C20avRVpKGr4pA==
9. kku9nVDdu1NUrZ9boAnxTU8BeEaUyzLOpENh2DT7KxMvYSIgfj0M46b+Y954H5KE+JFRwgoO5eXw2iRqf9xtmkO5x53Uyi5UxOCbxWm20F1e92Fwvls/RMA1A1luGptgU6addLZaXPmsD8u/8X5gZQ0hNcjKxW2afu/UNIxXDmkUUhCsQh005GiBw71cuMmLnw+7QYhe94LRE2J3bJrzrcxT/B4=
10. dC4JqLyBfMUx+AGEIH6+maPxSkhOStt6kdnB+H8zwiNVG4/Oa5P+SRW+ZzZj+Dv1afK7szFOz8OueLC6p+RR1A==
11. JShQHQ83UvyFYenk69Nb8GZ/mMcgpKaOz22DwFEAnPgdMNCWH/YPmRFaeubUXy50OZSUebYbjYmTnEHKTwKL28jVT47LyH6NN7/gi8bfpqU9/K6junJMK3N1u2wGZwOfPe+X+uUP0IuALLI3/jnw7FhzUA8ragEYDFJA8oywewhl/p7jtsH67x640Ao=
12. dC4JqLyBfMUx+AGEIH6+maPxSkhOStt6kdnB+H8zwiNVG4/Oa5P+SRW+ZzZj+Dv1afK7szFOz8OueLC6p+RR1A==
13. XcEy6hAntzre/IQG2U+4bUaz+XyCGbNKdqY+DYrPkh2mJI5pe5UgqHKWicP+wHUWkU31OKW9IhRCSZZsfTY4u+PpfhybIyjdJ2YGvgM4izqUSQ+7FmOAzaEc2rCXeuInt2ZJTAr2MkUmqlbWYzj3/YHTC4JybMTWdlYoGyf/jnw=