Context

The Race IAT measures implicit attitudes toward black people. For this class, the 2020 population of Race IAT participants was limited to US citizens with a complete IAT session, reported birth month, and reported birth year.

The population of 2020 Race IAT participants were asked, "Should law enforcement officers pay particular attention to those social groups more heavily involved in crime, even if this means focusing on members of particular ethnic groups?" We'll use StatCrunch and a random sample from the 2020 population of Race IAT participants to investigate whether birth gender is related to participants' attitudes about law enforcement's crime focus.

Variables

• Birth-Gender (Participant's assigned gender at birth)
• Crime-Focus (Participant's response to the question, "Should law enforcement officers pay particular attention to those social groups more heavily involved in crime, even if this means focusing on members of particular ethnic groups?")

To learn more about these variables, review theRace IAT variable descriptions(opens in a new tab).

Data

Your instructor used StatCrunch to obtain a random sample from the 2020 population of Race IAT participants. The filename for the random sample is Race-IAT Project Sample.

Prompt

we will use StatCrunch and theRace-IAT Project Sample to investigate the question the following question.

For the population of Race IAT participants, is birth gender related to attitudes about law enforcement's crime focus?

List of StatCrunch Directions

Each link will open in a new window. To return to this discussion, either close the new tab or select the tab for this discussion.

• Open StatCrunch
• Open a datafile in the Stats at Cuyamaca College group
• Download StatCrunch Output Window (no screenshots; please use these directions)
• Embed Pictures in a Textbox (no attachments; please use these directions)
• Copy and Paste a StatCrunch Table
• Chi-square Test for Independence (Summarized Data)
• Chi-square Test for Independence (Row Data)

	Disagree	Moderately Disagree	Slightly Disagree	Neither Agree Nor Disagree	Slightly Agree	Moderately Agree	Strongly Agree		Total
Male	95 (39.75%) (102.99)	44 (18.41%) (49.36)	16 (6.69%) (16.34)	15 (6.28%) (20.24)	37 (15.48%) (28.41)	18 (7.53%) (12.43)	14 (5.86%) (9.23)		239 (100%)
Female	195 (44.93%) (187.01)	95 (21.89%) (89.64)	30 (6.91%) (29.66)	42 (9.68%) (36.76)	43 (9.91%) (51.59)	17 (3.92%) (22.57)	12 (2.76%) (16.77)		434 (100%)
Total	290 (43.09%)	139 (20.65%)	46 (6.84%)	57 (8.47%)	80 (11.89%)	35 (5.2%)	26 (3.86%)		673 (100%)

(Q1) Interpret the expected counts for each response in the "Neither agree nor disagree" column of your table. No need to show how the expected counts were calculated, but each interpretation should include a relevant percentage.

(Q2) Determine whether conditions are met to use the chi-square test of independence. For each condition explain why the condition is met or not met.

(Q3)If conditions are met, use the StatCrunch chi-square output (included with your contingency table) to state your conclusion in context.

If conditions are not met, write "Conditions are not met, so we cannot use the chi-square test of independence to investigate whether birth gender is related to participants' attitudes about law enforcement'sprime focus."