Dr. Ta­ma­ra Scham­ber­ger

Dr. Ta­ma­ra Scham­ber­ger

Post­doc­to­ral Re­se­ar­cher

Email
ta­ma­ra.scham­ber­ger@uni-​bielefeld.de

Room
V9-​134

Phone
+49 521 106-​4877
 

 

 

Re­se­arch In­te­rests

• Struc­tu­ral equa­ti­on mo­de­ling
• Emer­gent va­ria­bles
• Ma­xi­mum li­keli­hood esti­ma­ti­on
  
   

Pu­blished Pa­pers

• Schu­berth, F., Scham­ber­ger, T., Hen­se­ler, J. (2023). More power­ful pa­ra­me­ter tests? No, ra­ther bia­sed pa­ra­me­ter esti­ma­tes. Some re­flec­tions on path ana­ly­sis with weigh­ted com­po­si­tes. Be­ha­vi­or Re­se­arch Me­thods. https://doi.org/10.3758/s13428-​023-02256-5
• Schu­berth, F., Hu­bo­na, G., Roe­mer, E., Zaza, S., Scham­ber­ger, T., Chuah, F., Cepeda-​Carrión, G., Hen­se­ler, J. (2023). The choice of struc­tu­ral equa­ti­on mo­de­ling tech­ni­que mat­ters: A com­men­ta­ry on Dash and Paul (2021). Tech­no­lo­gi­cal Fo­re­cas­ting and So­cial Chan­ge, 194, 122665
• Scham­ber­ger, T. (2023). Con­duc­ting Monte Carlo Si­mu­la­ti­ons for PLS-​PM and other variance-​based esti­ma­tors for struc­tu­ral equa­ti­on mo­de­ling. In­dus­tri­al Ma­nage­ment & Data Sys­tems, 123(6), 1789-​1813, https://doi.org/10.1108/IMDS-​07-2022-0418
• Schu­berth, F., Scham­ber­ger, T., Rönk­kö, M., Liu, Y., Hen­se­ler, J. (2023). Pre­ma­tu­re Con­clu­si­ons about the Signal-​to-Noise Ratio in Struc­tu­ral Equa­ti­on Mo­de­ling Re­se­arch: A Com­men­ta­ry on Yuan and Fang (2023). Bri­tish Jour­nal of Ma­the­ma­ti­cal and Sta­tis­ti­cal Psy­cho­lo­gy, http://doi.org/10.1111/bmsp.12304
• Scham­ber­ger, T. (2022) Me­tho­do­lo­gi­cal Ad­van­ces in Composite-​based Struc­tu­ral Equa­ti­on Mo­de­ling. Uni­ver­si­ty of Würz­burg/ Uni­ver­si­ty of Twen­te, https://doi.org/10.3990/1.9789036553759
• Scham­ber­ger, T., Can­ta­lup­pi, G., Schu­berth, F. (ac­cep­ted). Re­vi­si­ting and Ex­ten­ding PLS for Or­di­nal Me­a­su­re­ment and Pre­dic­tion In H. Latan, J. F. Hair, & R. Noo­n­an (Eds.), Par­ti­al least squa­res path mo­de­ling: Basic con­cepts, me­tho­do­lo­gi­cal is­su­es, and ap­p­li­ca­ti­ons (2nd ed.). Cham, Switz­er­land: Sprin­ger.
• Scham­ber­ger, T., Schu­berth, F., & Hen­se­ler, J. (2023). Con­fir­ma­to­ry com­po­si­te ana­ly­sis in human de­ve­lo­p­ment re­se­arch. In­ter­na­tio­nal Jour­nal of Be­ha­vio­ral De­ve­lo­p­ment, 47(1), 89–100. https://doi.org/10.1177/01650254221117506
• Scham­ber­ger, T., Schu­berth, F., Hen­se­ler, J., Di­jk­s­tra T. K.  (2020). Ro­bust par­ti­al least squa­res path mo­de­ling. Be­ha­vior­me­tri­ka, https://doi.org/10.1007/s41237-​019-00088-2

​Tea­ching

• Zeit­rei­hen­ana­ly­se Vor­le­sung (Som­mer 2024)
• Zeit­rei­hen­ana­ly­se Prak­ti­sche Übung (Som­mer 2024)
• Grund­la­gen der Sta­tis­tik (Win­ter 2023)
• In­tro­duc­tion to Struc­tu­ral Equa­ti­on Mo­de­ling (Win­ter 2023)

Links

Goog­le Scholar

Git­Hub

ORCID

Re­se­arch­ga­te

Cur­ri­cu­lum vitae