Study on Psychological Crisis Evaluation Combining Factor Analysis and Neural Networks *

Transcription

1 Psychology Vol.2, No.2, Copyrght 2011 ScRes. DOI: /psych Study on Psychologcal Crss Evaluaton Combnng Factor Analyss and Neural Networks * Hu Ln 1, Yngb Zhang 1, Hengqng Tong 1, Qaolng Tong 2, Dongsheng Lu 2 1 Department of Mathematcs, Wuhan Unversty of Technology, Wuhan, Chna; 2 Department of Electronc Scence & Technology, Huazhong Unversty of Scence & Technology, Wuhan, Chna Emal: @qq.com, Dasc163@163.com Receved August 17 th, 2010; revsed December 16 th, 2010; accepted January 15 th, Effectve and rapd psychologcal crss evaluaton under emergency s the bass to carry out psychologcal crss nterventon (PCI). In ths paper, based on exstng research, an ndex system to evaluate the state of psychologcal crss s establshed and the ndex system s smplfed by the model combnng factor analyss and neural networks. Experments llustrate that the tranng tmes, tranng tme and maxmum error of the combnaton model are 1445, (s), respectvely whle the general neural networks are 5581, (s), wth 92 samples and the fnal dagnoss by the combnaton model are also exact. Keywords: Psychologcal Crss, Evaluaton, Index System, Factor Analyss, Neural Networks Introducton In 1964, Amercan psychologst G. Gaplan orgnally brought up psychologcal crss (PC). He defned psychologcal crss as the status of psychologcal unbalance n whch ndvduals could nether avod nor cope wth sudden or serous lfe events (Gaplan, 1964). Once the psychologcal crss occurs, the psychologcal crss nterventon (PCI) s needed mmedately to recalbrate the mental stuaton to normal level, n case the ndvduals fall nto a status of sufferng, anxety, coupled wth desperaton, autonomc symptoms and behavor dsorder. However, the effectve crss nterventon s dependent on accurate evaluaton. Through evaluaton, the psychologst can understand the ndvduals crss stuaton and ther reactons thus effectve crss nterventon can be carred out as soon as possble. Myer and Wllams (1992) proposed a three-dmensonal trage evaluaton model whch provdes a framework for understandng ndvduals reactons durng a crss. The model presumes that reactons to crss events are seen n three domans: cogntve, affectve and behavoral (Myer, Wllam, Ottens & Schmdt, 1992). Brende (1998) presented an evaluaton model of phases based on hs research on unprecedented and destructve floods from 1987 to 1998 n USA. In ths artcle he says that survvors predctable emotonal and physologcal responses usually process through fve phrases over a perod of tme toward ether resoluton or symptom development. To preclude more severe and chronc symptoms, survvors should be debrefed by traned professonals wthn 48 hours (Brende, 1998). Wlson (1999) brought up a person-env- ronment nteractonal model to explan the typologes of traumatc events and stressor dmensons. Ths model pays attenton to comprehenson of stress and ts factors as well as the dversty of traumatc * The project was supported by the Natonal Natural Scence Foundaton of Chna ( , ). events (Wlson, 1999). In ths artcle, we propose a novel and effectve method to evaluate PC. Frstly ntegratng the core dea of Myer and Wllams, we establsh an evaluaton ndex system for psychologcal crss. Further, consderng the evaluaton should be effectve and vald, we put forward an evaluaton model combnng factor analyss and back-propagaton neural networks. Through factor analyss, the ntrnsc relatonshps among ndces are elmnated, and the dmenson of these ndces s compressed whle enough nformaton s mantaned. Moreover, the structure of the neural networks s smplfed. Overall, the accuracy of networks output s mproved and the evaluaton tme s reduced. Establshng Evaluaton Index System Evaluaton ndex system has montorng functon whch adopts one or more rgorous theores to analyze the causal relatonshp between ndvduals and development of crss accordng to the status of the ndvduals. The three-dmensonal trage evaluaton model proposed by Myer and Wllams s consdered to be a smple and rapd evaluaton system. Whle n a state of emergency, t also requres that the evaluaton and the dagnoss should be accurate; therefore the artcle starts to establsh an evaluaton ndex system to evaluate the state of psychologcal crss. When ndvduals face a crss, they wll conduct a seres of physcal and psychologcal reactons, and the reactons to the crss are manly n physcal, emotonal, cogntve and behavoral domans. Accordngly, the psychologcal crss evaluaton ndex system can be establshed from these four domans. The ndex system s shown n Table 1. Usng a 90-tem self-report symptom nventory (SCL-90) for reference, we dvde each specfc ndex of the evaluaton ndex system nto fve levels: not at all, a lttle bt, moderately, qute a bt, extremely (Hol, 2003). Each level s assgned a score from

2 H. LIN ET AL. 139 Table 1. Psychologcal crss evaluaton ndex system. reactons physcal emotonal cogntve behavoral ndces gastrontestnal dscomfort or darrhea, poor appette C11 headache C12 Fatgue, nsomna, nghtmares, easly startled C13 Dffculty n breathng or there s a sense of chokng or nfarct C14 Muscular tenson C15 scared or suspcous C21 Depressed or sad C22 Irrtable C23 helpless, nsenstve C24 negatve or lonely C25 dsturbed or nervous C26 self-condemned C27 Too senstve or alert, unable to relax C28 Contnuously concerns over the safety of famly members, fear of death C29 Immersed n the gref of body and mnd, leadng to changes n memory and percepton C31 Trouble concentratng and the relatons experenced between thngs are ambguous, leadng the ablty of makng decsons and solvng problems affected C32 Sometmes be afrad of beng mad C33 Lack of confdence, easly forgetful, performance degradaton, could not turn attenton from crss to other thngs C34 Can not concentrate on studes or work C41 Avod other people or make oneself feel not lonely n a specal way C42 Have mplemented dsruptve behavors on oneself or around C43 Refused to help and t s weak to accept the help C44 Behavor s nconsstent wth thnkng and emotons C45 Appear typcal behavors whch dd not occur n the past C46 one to fve respectvely. Accordng to the research on relablty and valdty of SCL-90 by Chen Shuln and L Lngjang (2003), the relablty of SCL-90 s good overall, the nter-tem consstency relablty of the general scale s 0.97 and those of subscales are over 0.69, the test-retest relablty s over The construct valdty of SCL-90 s also good, the correlaton coeffcents between the general scale and subscales are , and correlaton coeffcents among subscales are (Chen & L, 2003). In SCL-90, the fnal score s the sum of all scores ganed from each tem and the mnmum score s 90. Once the fnal score s more than 160 ponts, the examnee requres psychologcal counselng or advce. In ths evaluaton system, we set the fnal score to be the average of total scores, lowest score s 1.0, and the hghest s 5.0. If the crtcal average score s more than 1.77 (160/90), the examnee has a certan mental dsorder and the PCI s requred. When the fnal score s n the nterval of [1.0, 1.77) or [1.77, 2.77) or [2.77, 3.77) or [3.77, 4.77) or [4.77, 5.0], whch ndcates the examnees fve states of psychologcal crss. Factor Analyss Snce the evaluaton ndex system has been establshed, now the factor analyss (FA) wll be ntroduced to ndex compresson. FA s a wdely used method of multvarate statstcal analyss. Ths method s used to descrbe varablty among observed varables n terms of a potentally lower number of unobserved varables called factors. If Z s the standardzed varable of X, Z can be expressed a lnear combnaton of factor varables F n and error varable, the weght coeffcents of F and respectvely are c and d, that s n m n n n n 1 Z cf d (1) where cn s a factor loadng expressng the lnear correlaton between factor p and varable. Estmatng factor loadngs are ntended to nterpret the varaton of data as much as possble. The frst man factor has the strongest explanatory power for varaton, whle the second man factor s nferor and so on. We desgned the questonnares accordng to the evaluaton ndex system and sent out 110 questonnares n the Wenchuan Earthquake place, we randomly chose the local resdents as our examnees. At last we collected 103 samples, among them only 92 samples are vald, so we use the 92 vald samples to conduct the data analyss. Before mplementng FA, we frstly adopt Kaser-Meyer-

3 140 H. LIN ET AL. Olkn (KMO) test and Bartlett s test of sphercty to test the data whether they are ft for FA. The values of KMO statstc between 0.7 and 0.8 are good, values between 0.8 and 0.9 are great and values above 0.9 are superb (Hutcheson & Sofronou, 1999). For these samples, the value s 0.732, whch falls nto the range of beng good, so we are confdent that the FA s approprate for these data. The Bartlett s test of sphercty measures the null hypothess that the orgnal correlaton matrx s an dentty matrx. For a satsfactory FA to proceed, some relatonshp between varables are needed, n other words, we want ths test to be sgnfcant as a sgnfcant test tells us the matrx s not an dentty matrx. For these data, Bartlett s test s hghly sgnfcant (p <.001). Therefore, FA s approprate (Feld, 2005). We adopt prncpal component analyss to extract factors and varmax rotaton method to progress factor rotaton. Egenvalues, the percent of varance attrbutable to each factor and the cumulatve varance of the frst 14 factors are shown n Table 2. From Table 2, we can see that the cumulatve varance of the frst 14 factors has reached %. To ensure adequate nformaton s mantaned and the egenvalue s greater than one, ultmately there are 12 factors to replace the orgnal 24 varables and % of total nformaton s guaranteed. Accordng to rotated component score coeffcent matrx, every factor can be expressed by a lnear combnaton of the orgnal varables. The man ndces (loadngs are more than 0.2) explan the 12 factors are shown n Table 3. Further, f we use each factor s percentage of varance explaned as weght and sum up these 12 factors, the composte score of each sample can be ganed. From the above analyss, only 12 factors can well reflect the % nformaton of orgnal 24 varables, thus greatly reducng the dmenson of evaluaton and the correlaton between varables. Back Propagaton Neural Networks Back propagaton neural networks (BPNN) are multlayer feed-forward neural networks (NN) based on back-propagaton algorthm. The nonlnear processng ablty of BPNN can process cogntve judgments n varous complex envronments effectvely such as vague, ncomplete and conflctng nformaton. It s the most wdely used NN model. BPNN s a supervsed learnng algorthm. It s necessarly a multlayer percepton (wth nput layer, hdden layers and output layer). The network structure of BPNN s n Fgure 1: nput vector s x 1, x 2,, xm and output vector s y 1, y,, 2 yn. The learnng process can be dvded nto two phases: (a) the nformaton flow goes through nput layer, hdden layers, output layer; (b) error back propagaton network process, f the NN model does not get expected output value n output layer, the error sgnal propagates backward along the orgnal pathway layer by layer, and adjusts ts weghts and threshold value. Kolmogorov theorem of neural networks has proved that a full learnng three-layer BPNN can approxmate any functons. Therefore we choose a three-layer BPNN wth only one hdden layer. There s no theoretcal gudance n selectng the number of hdden layer nodes currently. Too many nodes wll ncrease the tranng tme and weaken the networks generalzaton and predctve ablty, whle too few nodes cannot reflect the correlaton between the follow-up value and prevous value and the model s nsuffcent. The number of nodes n hdden layer can refer to the followng formula: m1 m n a, where m 1 s the number of hdden layer nodes, m 1 s the number of nput layer nodes, n s the number of output layer nodes, a s a constant between one and ten (Rafael, 2004). Sample Classfcaton and Neural Networks Desgn Choose 10 testng samples from 92 samples as a test set. In test set each of the fve states (not-at-all, a lttle bt, moderately, qute-a-bt, extremely) has two samples respectvely. The other 82 samples are as a tranng set. To reflect the dfference whether carryng out FA before BPNN or not, we desgn two BPNN structures. In partcular, we name the former one as factor-analyss-back-propagaton (FABP) neural networks, whle name the latter one as nonfactor-analyss-back propagaton (NFABP) neural networks. FABP: after FA, 12 factors are ganed. Take the values of 12 factors n tranng set as nput, well then the number of nput layer nodes s 12. Take the correspondng composte scores of tranng samples as output and then the number of output layer nodes s one. Accordng to the formula m 1 m n a and experment results, we decde the number of hdden layer nodes s eght. The maxmum tranng tmes are NFABP: take the orgnal data of tranng samples as nput, well then the number of nput layer nodes s 24; take the correspondng composte scores of tranng samples Table 2. Man varance explaned. Factors F 1 F 2 F 3 F 4 F 5 F 6 F 7 Egenvalues Varance explaned % Accumulatve varance explaned% Factors F 8 F 9 F 10 F 11 F 12 F 13 F 14 Egenvalues Varance explaned % Accumulatve varance explaned %

4 H. LIN ET AL. 141 Table 3. Factors explaned by man ndces. Factors The man ndces F 1 C C C C C C F 2 C C C C C C F 3 C C C C F 4 C C C C F 5 C C F 6 C C F 7 C C C C F 8 C C C F 9 C C C C C C F 10 C C C C F 11 C C C C F 12 C C C Table 4. Three parameters comparson between FABP and NFABP. Types of NN Tranng tmes Tranng tme(s) Maxmum error FABP NFABP Table 5. Comparson between the expected output and the actual output from FABP. Expected output Actual output Error Fnal dagnoss Not at all Not at all A lttle bt A lttle bt moderately moderately Qute a bt Qute a bt extremely extremely Fgure 1. Network structure of BP. as output, and then the number of output layer nodes s one. Accordng to the formula m 1 m n a and experment results, we decde the number of hdden layer nodes s 15. The maxmum tranng tmes are Now we determne other parameters n NN. Transfer functon n hdden layer s tan-sgmod whle n output layer s log-sgmod. Make use of Levenberg-Marquardt back propagaton algorthm to tran the BPNN. Learnng rate s 0.2 and momentum parameter s 0.8 wth the tranng am s After tranng, two BPNNs are formed, one s FABP wth structure of and the other s NFABP wth the structure of Results and Dscusson In order to compare the merts of FABP wth the NFABP neural networks, record three parameters durng the tranng phase: tranng tmes, tranng tme and maxmum error between the output and correspondng known composte scores. Arrange these results n Table 4. From the above table, we can see that f we use raw data as nput for NFABP, the tranng for the neural networks wll take a long tme and the test accuracy s not as hgh as FABP whch has compressed the dmenson of the nput layer, and the maxmum error of NFABP s larger. Testng results of 10 testng samples by FABP s n Table 5. The results from FABP s almost accurate and the fnally dagnoss s exact, so the FABP s a relable evaluaton method. Ths paper proposes a novel and effectve method whch s the combnaton of FA and BPNN to evaluate psychologcal crss statue. Ths combnaton model has the followng advantages: FA can compress the dmenson of the evaluaton ndex system and elmnate the correlaton between ndces and factors. Takng 12 factors as nput of the neural networks, ths streamlnes the structure of neural networks and reduces tranng costs and mproves the output accuracy. The FABP neural networks model overcomes the subjectvty of tradtonal psychologcal crss evaluaton scale, whch wll gve some deas to psychologcal crss evaluaton. In psychologcal crss nterventon, the evaluaton s the prerequstes. Va evaluaton, the psychologst can defne the examnees psychologcal condton, and then take steps to carry out the crss nterventon as soon as possble, such as to use the medcne or psychotherapy to adjust examnees mental stuaton, to maxmally release the negatve mpact on the examnees, physcally and mentally, and then to lead them havng a postve vew of lfe. References Brende, J. O. (1998). Copng wth floods: evaluaton, nterventon, and recovery processes for survvors and helpers. Journal of Contemporary Psychotherapy, 28, do: /a: Chen S. L., & L L. J. (2003). Re-testng relablty, valdty and norm applcablty of SCL-90. Chnese journal of Nervous and Mental Dseases, 29,

5 142 H. LIN ET AL. Feld, A. P. (2005). Dscoverng statstcs usng SPSS (2nd ed.). London: Sage Publcaton. Gaplan, G. (1964). The prncples of preventve Psychatry. New York: Basc Books. Hol, M. (2003). Evaluaton of psychatrc symptoms usng the SCL-90. Academc Dssertaton, Helsnk: Department of Psychatry Helsnk Unversty Fnland. Hutcheson G., & Sofronou N. (1999). The multvarate socal scentst: ntroductory statstcs usng generalzed lnear models. London: Sage Publcaton. Myer, R. A., Wllams, R. C. Ottens, A..J., & Schmdt, A. E. (1992a). Crss evaluaton: A three-dmensonal model for trage. Journal of Mental Health Counselng, 14, Rafael, M., & Abdellah, E. F. (2004). Multlayer neural networks: An expermental evaluaton of on-lne tranng methods. Computers and Operatons Research, 31, do: /s (03) Wlson, J. P. (1999). Theoretcal perspectves of traumatc stress and debrefngs. Internatonal Journal of Emergency Mental health, 1, 1999,