1 / 13

Explaining our negative feelings towards Politicians and Parties

Explaining our negative feelings towards Politicians and Parties. 2008 Student. Research Question. What predicts our positive or negative feelings about politicians and political parties?. Hypothesis. Age (Nevitte’s Decline of Deference) Family and Friends (Qualitative Research). Method.

wyman
Télécharger la présentation

Explaining our negative feelings towards Politicians and Parties

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Explaining our negative feelings towards Politicians and Parties 2008 Student

  2. Research Question What predicts our positive or negative feelings about politicians and political parties?

  3. Hypothesis Age (Nevitte’s Decline of Deference) Family and Friends (Qualitative Research)

  4. Method CES 2006 Survey Syntax available for replication Index: • Politicians don’t keep their promises • All parties are corrupt • Politicians are ready to lie to get elected • Government doesn’t care what people like me think • How (negative) do you feel about parties in general • How (negative) do you feel about politicians in general Cronbach’s Alpha: 0.7368

  5. Findings (positive relationships = more positive views of government) Extremely Weak but still significant • Family talked often about politics while growing up: Tau-c: .09262 • It is the duty if citizens to vote: Tau-c: .05377 • Discussed politics often in the last week: Tau-c: .09262 • Higher Income: Tau-b: .14045

  6. Findings (positive relationships = more positive views of government) Age had no significant difference at 95% - we are equally discontented. -Chi: .00864

  7. Findings (positive relationships = more positive views of government) Stronger (still weak) significant relationships: • Higher Education: tau-b: .15610 • Higher Interest in Politics: tau-b: .20449 • Party you voted for in 2004: Kramer’s V: .15210

  8. Education Level >>> Feelings about Politicians and PartiesANOVA Statistics.

  9. Education Level >>> Feelings about Politicians and PartiesScheffe Test.

  10. Party you voted for in 2004>>> Feelings about Politicians and Parties.Scheffe Test

  11. Party you voted for in 2004>>> Feelings about Politicians and Parties.Cross Tabulation

  12. Conclusions Substantive Conclusions • No strong explanatory variables • weak ones: education, incomes, political interest, party affiliation Possible Methodological Conclusions • A. haven’t found it. • B. Need better tools. • C. Explanations might be personal, not systematic.

  13. Syntax *constructing the index* missing values CPS_I606 (8, 9). missing values CPS_I706 (8, 9). missing values CPS_J606 (8, 9). missing values CPS_G120 (996, 998, 999). missing values CPS_G6 (996 thru 999). missing values CPS_P6 (7, 8, 9). compute feelpart = (CPS_G120/100). compute feelpol = (CPS_G6/100). recode CPS_I606 (7=1) (5=.75) (3=.25) (1=0) into likeme. recode CPS_I706 (7=1) (5=.75) (3=.25) (1=0) into pollie. recode CPS_J106 (7=1) (5=.75) (3=.25) (1=0) into corrup. recode CPS_P6 (1=1) (3=.5) (5=0) into promis. RELIABILITY VARIABLES= promis corrup pollie likeme feelpol feelpart /scale(Feel)= promis corrup pollie likeme feelpol feelpart /summary=All. compute rawindex= promis+corrup+pollie+likeme+feelpol+feelpart recode rawindex (0 thru 1.75=1)(1.76 thru 2.75=2)(2.76 thru 5.80=3) into feel value labels feel 1'low' 2'moderate' 3'high'. Freq var feel *Extremely weak relationships* missing values cps_c6 (8) crosstab tables=feel by cps_c6 /cells = count column/stats=ctau CHISQ missing values cps_p16 (8,9) crosstab tables=feel by cps_p16 /cells = count column/stats=ctau CHISQ missing values cps_s1 () recode cps_s1 (1902 thru 1955=1) (1956 thru 1970=2) (1971 thru 1986=3) into age value labels age 1'old' 2'middle' 3'young' /ranges=scheffe /statistics=all. crosstab tables=feel by age /cells = count column/stats=btau CHISQ missing values CPS_A906 (8) crosstab tables=feel by cps_A906 /cells = count column/stats=btau CHISQ missing values cps_s18 (97) recode cps_s18 (1,2=1) (4 thru 6=2) (8 thru 10=3) into inco value labels inco 1'little' 2'some' 3'lots' crosstab tables=feel by inco /cells = count column/stats=btau CHISQ *stronger relationships* missing values cps_s3 (98,99) recode cps_s3 (1 thru 4=1) (5 thru 8=2) (9 thru 11=3) into educ value labels educ 1'little' 2'some' 3'lots' crosstab tables=feel by educ /cells = count column/stats=btau CHISQ oneway feel by educ (1,3) /ranges=scheffe /statistics=all. missing values cps_A806 (98) recode cps_A806 (0 thru 4=1) (5 thru 7=2) (8 thru 10=3) into intr value labels intr 1'little' 2'some' 3'lots' crosstab tables=feel by intr /cells = count column/stats=btau CHISQ oneway feel by intr (1,3) /ranges=scheffe /statistics=all. missing values cps_q6 (0,98,99) recode cps_q6 (10,8=8) crosstab tables=feel by cps_q6 /cells = count column/stats=PHI oneway feel by cps_q6 (1,8) Syntax

More Related