Network Structure Explains The Impact Of Attitudes On-Books Pdf

Network Structure Explains the Impact of Attitudes on
25 May 2020 | 20 views | 0 downloads | 12 Pages | 1.97 MB

Share Pdf : Network Structure Explains The Impact Of Attitudes On

Download and Preview : Network Structure Explains The Impact Of Attitudes On


Report CopyRight/DMCA Form For : Network Structure Explains The Impact Of Attitudes On



Transcription

www nature com scientificreports,OPEN Network Structure Explains the. Impact of Attitudes on Voting, Received 30 December 2016 Jonas Dalege Denny Borsboom Frenk van Harreveld Lourens J Waldorp. Accepted 2 June 2017 Han L J van der Maas,Published xx xx xxxx. Attitudes can have a profound impact on socially relevant behaviours such as voting However this. effect is not uniform across situations or individuals and it is at present difficult to predict whether. attitudes will predict behaviour in any given circumstance Using a network model we demonstrate. that a more strongly connected attitude networks have a stronger impact on behaviour and b. within any given attitude network the most central attitude elements have the strongest impact We. test these hypotheses using data on voting and attitudes toward presidential candidates in the US. presidential elections from 1980 to 2012 These analyses confirm that the predictive value of attitude. networks depends almost entirely on their level of connectivity with more central attitude elements. having stronger impact The impact of attitudes on voting behaviour can thus be reliably determined. before elections take place by using network analyses. Suppose you are one of the more than 130 million Americans who voted in the presidential election in 2016 Let. us further assume that you were supportive of Hillary Clinton You mostly held positive beliefs e g you thought. she was a good leader and a knowledgeable person and you had positive feelings toward her e g she made you. feel hopeful and proud representing a positive attitude toward Hillary Clinton1 4 However you also held a few. negative beliefs toward her e g you thought that Hillary Clinton was not very honest Did your overall positive. attitude cause you to vote for Hillary Clinton Here we show that the answer to this question depends on the net. work structure of your attitude First we show that the impact of attitudes i e average of the attitude elements. on behavioural decisions depends on the connectivity of the attitude network e g the network of your positive. attitude toward Hillary Clinton was highly connected so you probably voted for Hillary Clinton Second we. show that central attitude elements have a stronger impact on behavioural decisions than peripheral attitude. elements e g your positive beliefs about Hillary Clinton were more central in your attitude network than your. negative beliefs so the chance that you voted for Hillary Clinton further increased We thus provide insight into. how structural properties of attitudes determine the extent to which attitudes cause behaviour. In network theory dynamical systems are modelled as a set of nodes representing autonomous entities and. edges representing interactions between the nodes5 The set of nodes and edges jointly defines a network struc. ture Modelling complex systems in this way has probably become the most promising data analytic tool to tackle. complexity in many fields6 such as physics7 8 biology9 and psychology10 13 Recently network analysis has also. been introduced to the research on attitudes in the form of the Causal Attitude Network CAN model1 In this. model attitudes are conceptualized as networks in which nodes represent attitude elements that are connected by. direct causal interactions see Fig 1 The CAN model further assumes that the Ising model14 which originated. from statistical physics represents an idealized model of attitude dynamics. In the Ising model the probability of configurations i e the states of all nodes in the network which rep. resents the overall state of the attitude network depends on the amount of energy of a given configuration The. energy of a given configuration can be calculated using the Hamiltonian function. H x ixi ixixj, Here k distinct attitude elements 1 i j k are represented as nodes that engage in pairwise interactions. the variables xi and xj represent the states of nodes i and j respectively The model is designed to represent the. Department of Psychology University of Amsterdam 1018 WT Amsterdam The Netherlands Correspondence and. requests for materials should be addressed to J D email j dalege uva nl. Scientific Reports 7 4909 DOI 10 1038 s41598 017 05048 y 1. www nature com scientificreports, Figure 1 Illustrations of the Causal Attitude Network model and the hypotheses of the current study Networks.
represent a hypothetical attitude network toward a presidential candidate consisting of six beliefs e g judging. the candidate as honest intelligent caring represented by nodes B1 to B6 four feelings e g feeling hope. anger toward the candidate represented by nodes F1 to F4 and the voting decision represented by the node. D Red nodes within the dashed square represent the part of the network on which connectivity and centrality. estimates are calculated Edges represent positive bidirectional causal influences correlations in the causal. network correlation networks with thicker edges representing higher influence correlations Note that in. this network we assume that positive negative states of all nodes indicate a positive negative evaluation e g. positive state of judging a candidate as honest dishonest would be to not endorse this judgment Size of. the red nodes corresponds to their closeness centrality see Methods for details on the network descriptives. In the CAN model temperature represents a formalized conceptualization of consistency pressure on. attitude networks The correlation networks illustrate that lower higher temperature implies higher lower. correlations between the attitude elements, probability of these states as a function of a number of parameters that encode the network structure The param. eter i is the threshold of node i which determines the disposition of that node to be in a positive state 1 endors. ing an attitude element or negative state 1 not endorsing an attitude element regardless of the state of the. other nodes in the network statistically this parameter functions as an intercept The parameter i represents. the edge weight i e the strength of interaction between nodes i and j As can be seen in this equation the. Hamiltonian energy decreases if nodes are in a state that is congruent with their threshold and when two nodes. having positive negative edge weights between them assume the same different state Assuming that attitude. elements of the same different valence are generally positively negatively connected attitude networks thus. strive for a consistent representation of the attitude The probability of a given configuration can be calculated. using the Gibbs distribution15, Scientific Reports 7 4909 DOI 10 1038 s41598 017 05048 y 2. www nature com scientificreports, in which represents the inverse temperature of the system which can be seen as consistency pressures on. attitude networks reducing increasing the temperature of the system results in stronger weaker influence of. the thresholds and weights thereby scaling the entropy of the Ising network model16 17 An Ising model with low. high temperature results in a highly weakly connected correlation network see Fig 1 The denominator Z. represents the sum of the energies of all possible configurations which acts as a normalizing factor to ensure that. the sum of the probabilities adds up to 1, Conceptualizing attitudes as Ising models allows for the derivation of several hypotheses and a crucial test of. this conceptualization is whether it can advance the understanding of the relation between attitudes and behav. ioural decisions In the present paper we apply the CAN model and are the first to a formalize and b test. hypotheses based on the CAN model regarding the impact of attitudes on behaviour. The impact of attitudes on behaviour has been one of the central research themes in Social Psychology in. recent decades18 20 The bulk of the research on the relation between attitudes and behaviour has been done under. the umbrella definition of attitude strength which holds that one central feature of strong attitudes is that they. have a strong impact on behaviour21 Several lines of research have identified factors related to attitude strength. Among the most widely researched of these are attitude accessibility attitude importance and attitudinal ambiv. alence Studies have shown that accessible attitudes i e attitudes that can be easily retrieved from memory have. more impact on behaviour19 22 Similarly higher levels of subjective attitude importance i e attitudes to which. a person attaches subjective importance are related to increased accessibility of attitudes23 and to higher levels. of consistency between attitudes and behaviour24 25 Ambivalent attitudes i e attitudes that are based on both. negative and positive associations are less predictive of behaviour than univalent attitudes26 27 While these and. other attitude strength attributes such as certainty and extremity are generally interrelated28 29 a framework that. unifies these different attributes has long been absent in the literature Recently however based on the devel. opment of the CAN model attitude strength was formally conceptualized as network connectivity1 The CAN. model might thus provide the basis for a comprehensive and formalized framework of the relationship between. attitudes and behaviour Our current aim is to develop and test such a framework To do so we first formally. derive hypotheses regarding the impact of attitudes on behaviour from the CAN model Second we test these. hypotheses in the context of voting decisions in the US American presidential elections. From the CAN model the hypothesis follows that highly connected attitude networks i e attitude networks. that are based on Ising models with low temperature have a strong impact on behaviour As can be seen in Fig 1. low temperature results in strong connections both between non behavioural attitude elements i e beliefs and. feelings and between non behavioural attitude elements and behaviours e g behavioural decisions 1 Attitude. elements in highly connected networks are thus expected to have a strong impact on behavioural decisions This. leads to the hypothesis that the overall impact of attitudes depends on the connectivity of the attitude network. While the connectivity of attitude networks provides a novel formalization of attitude strength earlier approaches. to understanding the structure of attitudes fit very well within this framework For example studies have shown. that important attitudes are more coherent than unimportant attitudes30 31 and that strong attitudes have a more. consistent structure between feelings and beliefs than weak attitudes32 Also Phillip E Converse s distinction. between attitudes and nonattitudes based on stability of responses33 relates to our connectivity framework1. In addition to predicting the overall impact of an attitude from the connectivity of the attitude network the. CAN model predicts that the specific impact of attitude elements depends on their centrality as defined by their. closeness Closeness refers to how strongly a given node is connected both directly and indirectly to all other. nodes in the network34 35 In contrast to connectivity which represents a measure of the whole network centrality. is a measure that applies to individual nodes within the network Attitude elements high in closeness are good. proxies of the overall state of the attitude network as they hold more information about the rest of the network. than peripheral attitude elements rendering closeness the optimal measure of centrality for our current purposes. We therefore expect central attitude elements to have a stronger impact directly or indirectly on a behavioural. decision no matter which attitude elements are direct causes of this decision This can also be seen in Fig 1 as. there is a strong relation between a given node s centrality and it s correlation with the behavioural decision It. is important to note here that centrality of attitude elements does not refer to the classical definition of attitude. centrality but to the network analytical meaning of centrality Specific impact of attitude elements has received. somewhat less attention in the attitude literature than the global impact of attitudes with studies either focusing. on the primacy of feelings or beliefs in determining behaviour36 38 or on the subjective importance of attitude. elements39 40 and these different lines of research have been carried out much in isolation from each other and. from the attitude strength research paradigm for an exception see ref 39 It is our view that an advantage of the. approach we take in this article is that our framework holds promise in unifying these different approaches to. understanding the relation between attitudes and behaviour. In this paper we first show that the hypotheses put forward here above directly follow from conceptualizing. attitudes as networks with a simulation study We then test these hypotheses using data on attitudes toward can. didates and voting in the American presidential elections from 1980 2012 In doing so we test whether the CAN. model provides a comprehensive framework on whether attitudes and which attitude elements drive behavioural. decisions Voting decisions are a perfect test of this postulate because political attitudes often but not always drive. voting decisions22 36 37 41 42, Scientific Reports 7 4909 DOI 10 1038 s41598 017 05048 y 3.
www nature com scientificreports, Attitude element Included in data set Substituted by. Network Structure Explains the Impact of Attitudes on Voting Decisions Jonas Dalege Denny Borsboom Frenk van Harreveld Lourens J Waldorp amp Han L J van der Maas Attitudes can have a profound impact on socially relevant behaviours such as voting However this effect is not uniform across situations or individuals and it is at present difficult to predict whether attitudes will predict

Related Books

LCD Projector PT AE2000U

LCD Projector PT AE2000U

Panasonic Testing Center Panasonic Service Europe a division of Panasonic Marketing Europe GmbH Winsbergring 15 22525 Hamburg F R Germany WARNING Not for use in a computer room as defined in the Standard for the Protection of Electronic Computer Data Processing Equipment ANSI NFPA 75 Declaration of Conformity Model Number PT AE2000U

LCD Projector PT AE2000E panasonic com

LCD Projector PT AE2000E panasonic com

you to get the most out of your new product and that you will be pleased with your Panasonic LCD projector The serial number of your product may be f ound on its bottom You should note it in the space provided below and retain this booklet in case service is required Model number PT AE2000E Serial number

2 Spring Air BB Rifles Pyramyd Air

2 Spring Air BB Rifles Pyramyd Air

Spring Air BB Rifles BB Cal 4 5mm Steel Airgun Shot Daisy Outdoor Products Rogers AR 72757 0220 U S A 800 643 3458 www daisy com 11 07 10 SHOOTING SAFETY RULES 1 Always keep the muzzle pointed in a safe direction There are several safe carries depending on the situation NEVER ALLOW THE MUZZLE TO POINT IN THE DIRECTION OF A PERSON 2 Treat every gun as if it were loaded You

Application of 3S Techniques in the Study of Wetland

Application of 3S Techniques in the Study of Wetland

Key words Remote sensing system Geographic information system global position system Monitor of wetland Dongting Lake 2 Baicheng Xie Chunxia Zhang Xiqiang Shuai Boliang Luo 1 INTRODUCTION Wetland is a multifunctional ecosystem in the earth In addition to providing extremely rich resources for human production and living wetlands possess irreplaceable and enormous ecological functions

NOTICE This PDF file was adapted from an on line training

NOTICE This PDF file was adapted from an on line training

the organisms inhabiting wetland environments are referred to as food webs The combination of shallow water high levels of inorganic nutrients and high rates of primary productivity the synthesis of new plant biomass through photosynthesis in many wetlands is ideal for the development of organisms that form the base of the food web for example many species of insects mollusks and

Global inundation dynamics inferred from multiple

Global inundation dynamics inferred from multiple

boreal environments encompassing a wide variety of veg etation cover hydrological regime natural seasonality and land use impacts Existing global or regional surveys rep resent various components of wetland and open water distributions comprising inundated and noninundated wet lands lakes rivers and irrigated rice Matthews and Fung

SIOBHAN MILLER

SIOBHAN MILLER

SIOBHAN MILLER Fundadora de The Positive Birth Company BEB EN CAMINO C mo tener un parto positivo y empoderado con hipnoparto 001 272 bebe camino indd 5 14 01 2020 10 52 13

Hypnobirthing Training Academy Prospectus 2016

Hypnobirthing Training Academy Prospectus 2016

The Hypnobirthing Training Academy Hypnobirthing Practitioner Training Course Choosing the right hypnobirthing practitioner training course is a very important decision and one that you will wish to consider carefully Many practitioners are looking for a local training school but I urge you to consider who is going to be training you the curriculum on offer and what provisions and follow

April 2013 AHJ hypnoreunion com

April 2013 AHJ hypnoreunion com

HypnoBirthing outcomes submitted by Marilyn Colvin Boon 8 Ericksonian hypnosis and healing by Ann Laurence Fritsch 9 A therapist s life in Hong Kong by Victor Ching 11 State workshop reviews by Glen Green Chereyl Jackson amp Karen Verrall 12 The mystery of the human brain by Bruni Brewin 14

we must begin by healing birthing If we are the heal the

we must begin by healing birthing If we are the heal the

we must begin by healing birthing Rev Agnes Sallet von Tannenberg The brightness love amp joy at a baby s birth represents a stark contrast to the moment just before with the tension filled stillness in the air the first cry the sound of the little voice announces its first song then crashes thru all the fears and doubts

Psychological Self Help Bibliography

Psychological Self Help Bibliography

Psychological Self Help Bibliography by Clay Tucker Ladd c2004 Since the writing of this book has continued from the mid sixties to now 2005 the references span 50 years or so In fact the heyday of self help in the 1970s followed and was partly inspired by the revolutionary 1960s Therefore don t discount some of the older references cited here The early self help publications are