EndNote to latex reference export

I have already exported my .txt file from EndNote but how to compile it with TeX work? Here is my exported file:

@article{h2,
author = {Acock, Alan C.},
title = {Working With Missing Values},
journal = {Journal of Marriage & Family},
volume = {67},
number = {4},
pages = {1012-1028},
note = {Journal of Marriage & Family
Authors:Acock, Alan C.; Physical Description: Bibliography; Diagram; Graph; Table; Subject: Missing observations (Statistics); Subject: Families; Subject: Families -- Research; Subject: Research; Number of Pages: 17p; Record Type: Article},
abstract = {Less than optimum strategies for missing values can produce biased estimates, distorted statistical power, and invalid conclusions. After reviewing traditional approaches (listwise, pairwise, and mean substitution), selected alternatives are covered including single imputation, multiple imputation, and full information maximum likelihood estimation. The effects of missing values are illustrated for a linear model, and a series of recommendations is provided. When missing values cannot be avoided, multiple imputation and full information methods offer substantial improvements over traditional approaches. Selected results using SPSS, NORM, Stata (mvis/micombine), and Mplus are included as is a table of available software and an appendix with examples of programs for Stata and Mplus. Reprinted by permission of the publisher.},
keywords = {Missing observations (Statistics)
Families
Families -- Research
Research},
year = {2005}
}

@article{
author = {Baker, Stuart G. and Laird, Nan M.},
title = {Regression Analysis for Categorical Variables With Outcome Subject to Nonignorable Nonresponse},
journal = {Journal of the American Statistical Association},
volume = {83},
number = {401},
pages = {62},
note = {Baker, Stuart G. 1; Laird, Nan M. 2; Affiliations: 1: Staff Fellow, Biometry Branch, Division of Cancer Prevention and Control, National Institutes of Health (NIH), Bethesda, MD 20892-4200.; 2: Professor of Biostatistics, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115.; Issue Info: Mar1988, Vol. 83 Issue 401, p62; Thesaurus Term: REGRESSION analysis; Thesaurus Term: STATISTICAL hypothesis testing; Thesaurus Term: MULTIVARIATE analysis; Thesaurus Term: MATHEMATICAL models; Subject Term: LOG-linear models; Subject Term: CONFIDENCE intervals; Subject Term: HYPOTHESIS; Author-Supplied Keyword: EM algorithm; Author-Supplied Keyword: Missing data.; Author-Supplied Keyword: Sample survey; Number of Pages: 8p; Document Type: Article},
abstract = {We develop a log-linear model for categorical response subject to nonignorable nonresponse. The paper differs from Fay (1986) in its focus on estimation and hypothesis testing in a regression setting, as opposed to imputation in a multivariate setting. We present several new results concerning the existence of solutions on the boundary of the parameter space and the construction of confidence intervals for estimates. We illustrate the method by estimating the proportion of voters preferring Truman in a 1948 preelection poll (Mosteller, Hyman, McCarthy, Marks, and Truman 1949). Results may depend strongly on the model assumed for nonresponse; goodness-of-fit tests are available for comparing alternative models. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the American Statistical Association is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)},
keywords = {REGRESSION analysis
STATISTICAL hypothesis testing
MULTIVARIATE analysis
MATHEMATICAL models
LOG-linear models
CONFIDENCE intervals
HYPOTHESIS
EM algorithm
Missing data.
Sample survey},
year = {1988}
}

@book{
author = {Bishop, Yvonne M. M. and Fienberg, Stephen E. and Holland, Paul W.},
title = {Discrete Multivariate Analysis : Theory and Practice},
publisher = {MIT Press},
note = {Accession Number: 49463. Edition: 12th repr. ed. Publication Type: eBook. Language: English.},
keywords = {Multivariate analysis
Statistical methods--Models},
pages = {560p.},
year = {1995}
}

@article{
author = {Blumenthal, Saul},
title = {MULTINOMIAL SAMPLING WITH PARTIALLY CATEGORIZED DATA},
journal = {Journal of the American Statistical Association},
volume = {63},
number = {322},
pages = {542},
note = {Blumenthal, Saul 1; Affiliations: 1: New York University.; Issue Info: Jun68, Vol. 63 Issue 322, p542; Thesaurus Term: SAMPLING (Statistics); Thesaurus Term: PROBABILITY theory; Thesaurus Term: WORK sampling; Thesaurus Term: WORK measurement; Thesaurus Term: MATHEMATICAL models; Thesaurus Term: TIME study; Thesaurus Term: VARIANCES; Subject Term: CATEGORIES (Mathematics); NAICS/Industry Codes: 541910 Marketing Research and Public Opinion Polling; Number of Pages: 10p; Document Type: Article},
abstract = {In sampling situations with categorical observations the problem of uncategorized or partially categorized data sometimes arises. For instance, in signal detection, the problem can arise when an observer detects the presence of a signal, but cannot distinguish among several possible types of signal he may be receiving. In work sampling, it rises when an observer must classify the subject's activity into one of several major categories (which may be easy to identify) and then into one of several subcategories which may sometimes be difficult to distinguish. The observer may prefer not to make a distinction in doubtful cases or because of cost or time considerations may be told to make the distinction in only a selected fraction of the cases. Examples can be found in many other fields as well. Estimation of the probabilities of the subcategories is the subject of concern here and is made difficult by the unclassified observations. The effects of partial classification on the means and variances of the maximum likelihood estimates will be studied.},
keywords = {SAMPLING (Statistics)
PROBABILITY theory
WORK sampling
WORK measurement
MATHEMATICAL models
TIME study
VARIANCES
CATEGORIES (Mathematics)},
year = {1968}
}

@book{
author = {Carpenter, James R., Kenward Michael G.},
title = {Multiple imputation and its application},
publisher = {Wiley},
edition = {1st edition},
abstract = {: "This book is written with three main aims; to provide a thorough introduction to the general MI methods, to provide a detailed discussion of the practical use of the MI method and to present real-world examples drawn from the field of biostatistics"--Provided by publisher. Contents: Introduction -- The multiple imputation procedure and its justification -- Multiple imputation of quantitative data -- Multiple imputation of binary and ordinal data -- Multiple imputation of unordered categorical data -- Nonlinear relationships -- Interactions -- Survival data, skips and large datasets -- Multilevel multiple imputation -- Sensitivity analysis: MI unleashed -- Including survey weights -- Robust multiple imputation. Access: Materials specified: John Wileyhttp://dx.doi.org/10.1002/9781119942283 Materials specified: Wiley Online Libraryhttp://onlinelibrary.wiley.com/book/10.1002/9781119942283 http://site.ebrary.com/id/10662548 Materials specified: ebrary SUBJECT(S) Descriptor: Multiple imputation (Statistics) Missing observations (Statistics) Social sciences -- Statistical methods. Medical statistics. Medicine -- Research -- Statistical methods. Data Interpretation, Statistical. Biomedical Research -- methods. Medical statistics. Medicine -- Research -- Statistical methods. Missing observations (Statistics) Multiple imputation (Statistics) Social sciences -- Statistical methods. Note(s): Includes bibliographical references (pages 316-326) and indexes. General Info: Other format available: Print version:; Carpenter, James R.; Multiple imputation and its application.; 1st ed.; Chichester, West Sussex : John Wiley & Sons, 2013 Class Descriptors: LC: QA277; Dewey: 610.72/4; NLM: WA 950 Responsibility: James R. Carpenter and Michael G. Kenward, Department of Medical Statistics, London School of Hygiene and Tropical Medicine, UK. Vendor Info: Coutts Information Services YBP Library Services ebrary (COUT YANK EBRY) Material Type: Document (dct); Internet resource (url); eBook (ebk) Document Type: Internet Resource; Computer File Date of Entry: 20120718 Update: 20130905 Accession No: OCLC: 801051995 Provider: OCLC Database: Ebooks --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------},
year = {2013}
}


Your given bib file is not compilable because there are some errors: Some keys are missing (I added h3 ... h6) and you have to write \& instead of & in the title or abstract of a book (bib entry h2) to get an printed & in your title.

Load the following MWE with TeXWorks, choose pdfLaTeX+MakeIndex+BibTeX and compile three times.

Pretty printed and shortend MWE, including bib file with package filecontents):

\RequirePackage{filecontents}        % loading package filecontents
% writing file \jobname.bib, for example mb-bibtex.bib.
\begin{filecontents*}{\jobname.bib}
@Book{companion,
author    = {Goossens, Michel and Mittelbach, Frank and Samarin, Alexander},
title     = {The LaTeX Companion},
edition   = {1},
year      = {1994},
}
@article{h2,
author   = {Acock, Alan C.},
title    = {Working With Missing Values},
journal  = {Journal of Marriage \& Family},
volume   = {67},
number   = {4},
pages    = {1012--1028},
note     = {Journal of Marriage \& Family. Reprinted by permission of the publisher.},
keywords = {Missing observations (Statistics) Families Research},
year     = {2005},
}
@article{h3,
author   = {Baker, Stuart G. and Laird, Nan M.},
title    = {Regression Analysis for Categorical Variables With Outcome Subject to Nonignorable Nonresponse},
journal  = {Journal of the American Statistical Association},
volume   = {83},
number   = {401},
pages    = {62},
note     = {Document Type: Article},
abstract = {[ABSTRACT FROM AUTHOR]},
keywords = {REGRESSION analysis STATISTICAL hypothesis testing MULTIVARIATE analysis MATHEMATICAL models LOG-linear models CONFIDENCE intervals HYPOTHESIS EM algorithm Missing data. Sample survey},
year     = {1988},
}
@book{h4,
author    = {Bishop, Yvonne M. M. and Fienberg, Stephen E. and Holland, Paul W.},
title     = {Discrete Multivariate Analysis : Theory and Practice},
publisher = {MIT Press},
note      = {Accession Number: 49463. Edition: 12th repr. ed. Publication Type: eBook. Language: English.},
keywords  = {Multivariate analysis Statistical methods--Models},
pages     = {560},
year      = {1995},
}
@article{h5,
author   = {Blumenthal, Saul},
title    = {MULTINOMIAL SAMPLING WITH PARTIALLY CATEGORIZED DATA},
journal  = {Journal of the American Statistical Association},
volume   = {63},
number   = {322},
pages    = {542},
note     = {Document Type: Article},
abstract = {In sampling situations with categorical observations the problem of uncategorized or partially categorized data sometimes arises. For instance, in signal detection, the problem can arise when an observer detects the presence of a signal, but cannot distinguish among several possible types of signal he may be receiving. In work sampling, it rises when an observer must classify the subject's activity into one of several major categories (which may be easy to identify) and then into one of several subcategories which may sometimes be difficult to distinguish. The observer may prefer not to make a distinction in doubtful cases or because of cost or time considerations may be told to make the distinction in only a selected fraction of the cases. Examples can be found in many other fields as well. Estimation of the probabilities of the subcategories is the subject of concern here and is made difficult by the unclassified observations. The effects of partial classification on the means and variances of the maximum likelihood estimates will be studied.},
keywords = {SAMPLING (Statistics) PROBABILITY theory WORK sampling WORK measurement MATHEMATICAL models TIME study VARIANCES CATEGORIES (Mathematics)},
year     = {1968},
}
@book{h6,
author    = {Carpenter, James R., Kenward Michael G.},
title     = {Multiple imputation and its application},
publisher = {Wiley},
edition   = {1st edition},
year      = {2013},
}
\end{filecontents*}

\documentclass{article}

\usepackage[numbers]{natbib}         % bibliography style
\usepackage[colorlinks]{hyperref}    % better urls in bibliography

\begin{document}
Test of bibliography:
\nocite{*}

\bibliographystyle{plainnat}  % needs package natbib
\bibliography{\jobname}       % uses \jobname.bib, according to \jobname.tex
\end{document}


Change bibliography style for your needs (I used plainnat which needs package natbib).

You should add it somewhere in the code with appropriate package and command. For example: 1) in the preambule:

\usepackage[options]{natbib}


2)in the place you want to print it:

\bibliographystyle{"some-nice-style"}
\bibliography{"name-of-the-bib-file"}