apa itu observational research

Pengertian Metode Observasi dan Contohnya

Pengertian Metode Observasi dan Contohnya – Setiap peneliti tentunya mencurahkan perhatiannya kepada sesuatu dan mengamati fakta yang terdapat di dalamnya. Hal ini tentu saja didorong oleh rasa keingintahuan yang tinggi terkait pemahaman fakta yang diamati secara lebih mendalam. Pada hakikatnya, seorang peneliti pastinya memunculkan berbagai pertanyaan. Pengamatan terhadap fakta, identifikasi atas masalah, dan usaha untuk menjawab rumusan masalah didasarkan pada teori. Hal ini merupakan esensi dari sebuah riset.

Riset dapat disebut sebagai suatu usaha yang sistematis untuk mengatur dan menyelidiki masalah serta menjawab pertanyaan yang muncul dan terkait dengan fakta dan fenomena. Oleh karena itu, riset merupakan hal yang sangat penting karena berupa penyelidikan yang sistematis, terkontrol, empiris dan kritis tentang fenomena alami dengan dipandu oleh teori dan hipotesis mengenai hubungan yang dianggap terdapat di antara fenomena itu.

Berdasarkan teknik, pengolahan data dalam sebuah penelitian dibagi menjadi dua, yaitu penelitian kuantitatif dan penelitian kualitatif. Salah satu teknik pengolahan data yang seringkali digunakan dalam penelitian, yaitu teknik observasi. Observasi ini memiliki peran penting dalam arti penelitian sebagai salah satu metode penelitian ilmiah yang dapat dilakukan dengan bermacam-macam cara. Namun, kebutuhan untuk reproduktifitas mensyaratkan bahwa observasi oleh pengamat yang berbeda dapat dibandingkan.

Dalam suatu penelitian, metode observasi akan digambarkan sebagai metode yang dipergunakan dalam mengamati dan mendeskripsikan tingkah laku subjek. Seperti namanya, observasi ini adalah cara mengumpulkan informasi dan data yang relevan dengan mengamati, sehingga dalam hal ini observasi disebut sebagai studi partisipatif karena si peneliti harus menjalin hubungan dengan responden dan untuk ini harus membenamkan dirinya dalam pengaturan yang sama dengan mereka.

Hanya dengan begitu peneliti dapat menggunakan metode observasi untuk mencatat data yang dibutuhkan. Metode observasi digunakan jika peneliti ingin menghindari kesalahan yang dapat menjadi hasil bias selama proses evaluasi dan interpretasi. Penggunaan teknik observasi ini biasanya dijadikan sebagai pendukung dalam suatu riset untuk mengamati fenomena yang terjadi di lokasi penelitian.

Terdapat bermacam-macam teknik yang dipergunakan dalam observasi oleh seorang peneliti sesuai kebutuhan data yang ingin mereka dapatkan. Kira-kira apa saja teknik-teknik yang seringkali digunakan untuk keperluan observasi dalam sebuah penelitian?

Dalam artikel kali ini, kita akan membahas mengenai teknik observasi sebagai salah satu contoh dari teknik pengolahan data. Dengan harapan bisa menjadi tambahan insight dan rekomendasi bagi kalian calon praktisi data, peneliti, maupun data enthusiast . Jangan lewatkan artikel berikut ini, pastikan simak baik-baik, stay tune and keep scrolling on this article guys!

Mengungkap Rahasia Sukses Leonard Hartono dalam Buku A Book by Overpost: Business 101

Pengertian Observasi

Metode observasi seringkali menjadi pelengkap data yang diperoleh dari wawancara mendalam dan survei. Observasi bisanya dipahami sebagai upaya untuk memperoleh data secara ”natural”. Pengertian paling sederhana dari metode observasi adalah melihat dan mendengarkan peristiwa atau tindakan yang dilakuakan oleh orang-orang yang diamati, kemudian merekam hasil pengamatannya dengan catatan atau alat bantu lainnya.

Observasi berarti pula mengamati, menyaksikan, memperhatikan sebagai metode pengumpulan data penelitian. Artikel ini akan membahas tentang metode observasi dalam penelitian sosial. Kita sudah mendefinsikan secara sederhana apa itu observasi di paragraf pertama. Berikutnya, kita akan ulas secara lebih mendalam tentang cara melakukan observasi dan masalah yang biasanya dihadapi peneliti.

Tak jarang, metode observasi dipahami secara keliru. Observasi memang mengamati dengan melihat dan mendengar. Namun, observasi sebagai metode penelitian memiliki karakteristik dan teknik tertentu. Barangkali beberapa pembaca sudah pernah mendengar istilah observasi partisipatoris. Kita akan ulas tentang pengertian observasi menurut para ahli dan jenis-jenis observasi sebelum membahas masalah dalam metode observasi.

Dikutip dari buku Evaluasi Pembelajaran: Konsep Dasar, Prinsip, Teknik, dan Prosedur (2020) oleh Muhammad Ilyas Ismail, observasi adalah salah satu teknik pengumpulan data yang sifatnya lebih spesifik dibanding teknik lainnya. Beberapa pengertian observasi menurut para ahli adalah sebagai berikut:

1. Gibson R.L. dan Mitchell M.H.

Observasi merupakan teknik yang digunakan sebagai seleksi derajat untuk menentukan sebuah keputusan serta konklusi terhadap orang yang sedang diamati.

2. Larry Christensen

Observasi adalah cara untuk mendapatkan informasi penting mengenai orang, karena apa yang dikatakan belum tentu sesuai dengan yang dikerjakan.

3. Creswell

Observasi adalah proses pemerolehan data dari tangan pertama, dengan cara melakukan pengamatan orang serta lokasi dilakukannya penelitian.

Observasi merupakan metode yang sifatnya akurat dan spesifik untuk mengumpulkan data dan mencari informasi mengenai segala kegiatan yang dijadikan obyek kajian penelitian.

5. Sutrisno Hadi

Obervasi merupakan sebuah proses yang sangat kompleks, terdiri atas berbagai macam proses, baik biologis maupun psikologis, yang mana lebih memprioritaskan proses ingatan serta pengamatan.

6. Eko Putro Widyoko

Observasi adalah pengamatan dan pencatatan secara sistematis terhadap unsur-unsru yang tampak dalam suatu gejala pada obyek penelitian.

7. Sugiyono

Dikutip dari buku Metode Penelitian Pendidikan Pendekatan Kuantitatif (2014), observasi adalah proses yang kompleks, suatu proses yang tersusun dari pelbagai proses biologis dan psikologis.

Dalam bukunya Metodologi Penelitian Pendidikan (2010), dijelaskan bahwa observasi merupakan metode pengumpulan data yang menggunakan pengamatan secara langsung maupun tidak langsung.

Teknik dalam Observasi

1. observasi terkontrol.

Observasi terkontrol dilakukan di ruang tertutup. Peneliti yang memiliki kewenangan untuk menentukan tempat dan waktu di mana dan kapan observasi akan dilakukan. Dia juga memutuskan siapa partisipannya dan dalam keadaan apa dia akan menggunakan proses standar.

Partisipan dipilih untuk kelompok variabel penelitian secara acak. Peneliti mengamati dan mencatat data perilaku yang rinci dan deskriptif dan membaginya ke dalam kategori yang berbeda. Kadang-kadang peneliti mengkodekan tindakan sesuai skala yang disepakati dengan menggunakan daftar perilaku.

Pengkodean dapat mencakup huruf atau angka atau rentang untuk mengukur intensitas perilaku dan menggambarkan karakteristiknya. Data yang terkumpul seringkali diubah menjadi statistik. Dalam metode observasi terkontrol, partisipan diinformasikan oleh peneliti tentang tujuan penelitian. Hal ini membuat mereka sadar sedang diamati. Peneliti menghindari kontak langsung selama metode observasi dan umumnya menggunakan cermin dua arah untuk mengamati dan mencatat detail.

2. Observasi Partisipatif

Metode observasi partisipatif sering dianggap sebagai varian dari metode observasi naturalistik karena memiliki kemiripan. Perbedaannya adalah peneliti bukan lagi pengamat jarak jauh karena ia telah bergabung dengan partisipan dan menjadi bagian dari kelompoknya.

Seorang peneliti melakukan ini untuk mendapatkan wawasan yang lebih mendalam dan lebih dalam tentang kehidupan mereka. Peneliti berinteraksi dengan anggota lain dari kelompok secara bebas, berpartisipasi dalam aktivitas mereka, mempelajari perilaku mereka dan memperoleh cara hidup yang berbeda. Pengamatan partisipan bisa terbuka atau terselubung.

• Overt (terbuka), ketika peneliti meminta izin dari suatu kelompok untuk berbaur. Ia melakukannya dengan mengungkapkan tujuan sebenarnya dan identitas aslinya kepada kelompok yang ingin diajak bergaul.
• Covert (terselubung), jika peneliti tidak menunjukkan identitas atau arti sebenarnya kepada kelompok yang ingin ia ikuti. Ia merahasiakan keduanya dan mengambil peran dan identitas palsu untuk masuk dan berbaur dalam grup. Dia biasanya bertindak seolah-olah dia adalah anggota asli dari grup itu

3. Observasi Naturalistik

Ilmuwan sosial dan psikolog umumnya menggunakan metode observasi naturalistik. Prosesnya melibatkan mengamati dan mempelajari perilaku spontan para partisipan di lingkungan terbuka atau alami. Peran peneliti adalah menemukan dan merekam apa saja yang dapat dilihat dan diamati di habitat aslinya.

Teknik ini melibatkan pengamatan dan mempelajari perilaku spontan partisipan di lingkungan alami mereka. Peneliti hanya mencatat apa yang mereka lihat dengan cara apapun yang mereka bisa. Dalam observasi tidak terstruktur, peneliti mencatat semua perilaku yang relevan tanpa sistem. Mungkin ada terlalu banyak untuk dicatat dan perilaku yang dicatat belum tentu menjadi yang paling penting, sehingga pendekatan ini biasanya digunakan sebagai studi percontohan untuk melihat jenis perilaku apa yang akan dicatat. Dibandingkan dengan pengamatan terkontrol, ini seperti perbedaan antara mempelajari hewan liar di kebun binatang dan mempelajarinya di habitat aslinya.

4. Observasi Terstruktur

Observasi terstruktur terdiri atas definisi kategori yang cermat di mana informasi akan dicatat, standarisasi kondisi pengamatan, dan sebagian besar digunakan dalam studi yang dirancang untuk memberikan deskripsi sistematis atau untuk menguji hipotesis kausal.

Penggunaan teknik observasi terstruktur mengandaikan bahwa penyidik mengetahui aspek apa dari situasi yang diteliti yang relevan dengan tujuan penelitiannya dan oleh karena itu berada dalam posisi untuk mengembangkan rencana khusus untuk membuat dan merekam pengamatan sebelum dia benar-benar memulai pengumpulan data.

Pengamatan terstruktur dapat digunakan dalam pengaturan lapangan alami atau pengaturan laboratorium. Pengamatan terstruktur, sejauh ini digunakan terutama dalam penelitian yang dimulai dengan formulasi yang relatif spesifik, biasanya memungkinkan kebebasan memilih yang jauh lebih sedikit sehubungan dengan isi pengamatan daripada yang diizinkan dalam pengamatan tidak terstruktur.

Dikarenakan situasi dan masalahnya sudah eksplisit, pengamat berada dalam posisi untuk menetapkan terlebih dahulu kategori-kategori yang akan dianalisis situasi tersebut. Kategori ditentukan dengan jelas untuk memberikan data yang dapat diandalkan tentang pertanyaan yang akan ditanyakan.

Contoh Metode Observasi

Pada dasarnya, ada dua jenis metode observasi dalam penelitian; partisipatoris dan non-partisipatoris. Motivasi utama pembedaan ini adalah pada istilah yang disebut tingkat reaktivitas. Reaktivitas sangat menentukan kualitas data penelitian. Kita bisa memahami reaktivitas sebagai seberapa reaktif perilaku orang-orang yang sedang diteliti atau sedang diamati. Semakin reaktif, maka data yang dihasilkan dari observasi semakin rendah kualitasnya. Reaktivitas bisa dilihat pula sebagai sumber error.

Sebagai contoh, kita akan melakukan observasi pada komunitas hijau di Yogyakarta. Dalam konteks natural (tanpa penelitian), ekspresi wajah beberapa anggota komunitas terlihat muram ketika menjalankan kegiatan menanam di kebun. Di hari lain, ketika seorang peneliti dari luar negeri datang untuk melakukan observasi, ekspresi wajah para anggota tersebut terlihat bersemangat sekali. Mimik muka yang terlihat bersemangat itu adalah bentuk reaktivitas karena dilakukan dengan penuh kesadaran bahwa dirinya sedang di bawah pengamatan. Dengan kata lain, tidak ”natural”.

Kualitas data hasil observasi yang tidak ”natural” boleh dikatakan lemah atau bahkan error. Tingkat seberapa reaktif data yang diperoleh nantinya harus dipikirkan terlebih dahulu oleh peneliti sebelum turun lapangan. Setelah menilai potensi reaktivitas, baru peneliti menentukan apakah akan memilih metode observasi partisipatoris atau non-partisipatoris.

1. Metode Observasi Partisipatoris

Metode observasi partisipatoris bisa dideskripsikan sebagai metode pengamatan dimana peneliti memposisikan dirinya sebagai partisipan sebagaimana orang lain yang sedang diobservasi. Dalam memposisikan diri sebagai partisipan, peneliti tetap harus menjaga jarak agar unsur objektivitas tetap terjaga.

2. Metode Observasi Non-Partisipatoris

Metode observasi non-partisipatoris bias dipahami sebagai metode pengamatan dimana peneliti memposisikan diri sebagai orang luar dari kelompok yang ditelitinya. Metode ini sering kali memberi jarak yang cukup jauh antara peneliti dengan objek yang diteliti karena pengamatan dilakukan dari luar. Pada level yang ekstrim, metode non-partisipatoris dapat dilihat sebagai metode yang sering dipraktikkan oleh mata-mata dalam mengamati suatu kasus.

Melanjutkan isu reaktivitas yang telah disinggung di awal, menurut sosiolog Martyn Hammersley dalam tulisannya di The Blackwell Encyclopedia of Sociology (2007) berjudul “Observation”, masalah yang dihadapi metode observasi tidak hanya isu reaktivitas. Beberapa isu lain yang dihadapi peneliti meliputi; problem memperoleh akses, sampling, variasi data yang dihasilkan, dan problem etika.

Cara Mendapatkan Data Hasil Observasi yang Berkualitas

Berikut ini beberapa isu lain yang harus diperhatikan agar data hasil observasi yang diperoleh berkualitas, sehingga hasil riset juga berkualitas.

• Masalah memperoleh akses bisa terdiri dari beragam bentuk, tergantung pada peran yang akan dimainkan peneliti dan keputusan sebjek penelitian. Ketika penelitian dilakukan secara terbuka, artinya peneliti memperkenalkan diri dan risetnya, akses untuk melakukan observasi akan tergantung pada proses negosiasi. Dalam proses negosiasi, kesepakatan terkait penelitian harus dicapai diawal agar tidak ada pihak yang dirugikan nantinya. Persetujuan untuk melakukan observasi bisa pula tergantung pada karakteristik dan kualitas personal dan sosial penelitinya.
• Sampling bisa pula melibatkan observasi. Sebagai contoh, peneliti mengamati situasi kampung atau komunitas yang sedang diteliti, misalnya. Pengamatan awal untuk sampling ini bisa membantu menentukan siapa saja orang yang akan dijadikan informan, kapan mereka bisa ditemui atau dihubungi, dan lain sebagainya. Ada beberapa strategi yang bisa diterapkan di sini, misalnya, apakah peneliti akan meletakkan fokus perhatiannya pada tempat yang diteliti atau perilaku orang-orangnya. Berapa lama melakukan observasi juga harus ditentukan sejak awal.
• Variasi data yang dihasilkan tergantung pada apakah observasi dilakukan secara terstruktur atau tidak terstruktur. Observasi yang terstruktur mengikuti desain perencanaan detail yang dibuat sebelum observasi dilakukan. Dengan kata lain, peneliti melakukan observasi sesuai panduan observasi. Pengamatan yang tidak terstruktur artinya observasi dilakukan secara fleksibel. Data yang dihasilkan dari observasi tak terstruktur biasanya lebih beragam karena melibatkan beberapa instrumen penelitian yang digunakan sesuai kebutuhan, misalnya, buku harian, catatan lapangan, alat rekam suara, alat rekam gambar, alat rekam video, dan sebagainya.
• Masalah etika harus dijelaskan terlebih dahulu di awal agar peneliti tidak tersandung masalah etis yang bisa menurunkan reputasinya sebagai peneliti. Observasi bisa dilakukan secara tertutup atau terbuka. Prosedur etis pada umumnya menghendaki observasi terbuka dimana identitas peneliti dan penelitiannya diketahui oleh orang yang diobservasi. Di lain sisi, observasi tertutup sering ditolak karena biasanya diselimuti kebohongan, misalnya menyembunyikan identitas asli peneliti dan menggunakan identitas palsu. Subjek penelitian juga berpotensi terganggu privasinya. Namun demikian, pilihan apakah akan menerapkan observasi terbuka atau tertutup tergantung pada tingkatannya. Observasi yang terlalu terbuka juga rentan terhadap error.

Keuntungan dan Kekurangan Metode Observasi

Berikut penjelasan keuntungan dan kerugian metode observasi:

1. Keuntungan Observasi

Keuntungan pelaksanaan pengamatan langsung atau observasi dalam proses pengumpulan data, yaitu:

• Observasi sangat mudah dilaksanakan.
• Metode pengamatan langsung mampu menjawab atau memenuhi rasa ingin tahu seseorang, sehingga pada akhirnya proses yang sudah dilalui memberikan makna atau nilai tersendiri. Dengan metode pengamatan langsung bisa menjadi bukti dan tidak adanya manipulasi.
• Observasi bisa membuat seseorang lebih termotivasi dan juga memiliki rasa ingin tahu yang cukup besar. Metode ini bisa digunakan sebagai alat penyelidikan.

2. Kekurangan Observasi

Beberapa kekurangan metode observasi, yaitu:

• Pengamat membutuhkan waktu untuk menunggu tindakan tertentu.
• Terdapat beberapa data yang tidak bisa dilakukan dengan observasi, misalnya rahasia pribadi seseorang.
• Kecenderungan seseorang yang sedang diobservasi untuk berperilaku atau bersikap sesuai dengan yang diharapkan pengamat.

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Metode Observasi dalam Penelitian Kualitatif, Beserta Penjelasannya

Dalam penelitian kualitatif, tentu diperlukan yang namanya pengumpulan data untuk menyusun sebuah laporan penelitian. Berdasarkan manfaat empiris, bahwa metode pengumpulan data kualitatif yang paling independen. Terhadap semua metode pengumpulan data, dan teknik analisis data. Salah satunya adalah metode observasi. Menurut sumber buku Penelitian Kualitatif edisi ke-2 (2007). Berikut adalah penjelasan mengenai metode observasi dalam penelitian kualitatif. Simak dibawah ini, ya!

Baca Juga : Metode Wawancara dalam Penelitian Kualitatif, Beserta Penjelasannya

Metode Observasi dalam Penelitian Kualitatif

Beberapa bentuk observasi yang dapat digunakan dalam penelitian kualitatif adalah sebagai berikut.

1. Observasi Partisipasi (Participant Observer)

Definisi observasi atau pengamatan adalah kegiatan keseharian manusia dengan menggunakan panca indra mata sebagai alat bantu utamanya. Lainnya seperti telinga, penciuman, mulut, dan kulit. Karena itu, observasi adalah kemampuan seseorang untuk menggunakan pengamatannya. Melalui hasil kerja panca indra mata serta dibantu dengan panca indra lainnya. Di dalam pembahasan ini kata observasi dan pengamatan digunakan secara bergantian. Seseorang yang sedang melakukan pengamatan tidak selamanya menggunakan apa yang terlihat di mata saja. Tetapi selalu mengaitkan apa yang dilihatnya dengan apa yang dihasilkan oleh anggota tubuh lainnya. Seperti apa yang ia dengar, apa yang ia cicipi, apa yang ia cium dari penciumannya. Bahkan dari apa yang ia rasakan dari sentuhan-sentuhan kulitnya.

Dari pemahaman observasi atau pengamatan di atas. Sesungguhnya yang dimaksud dengan metode observasi. Adalah metode pengumpulan data yang digunakan untuk menghimpun data penelitian melalui pengamatan dan pengindraan. Suatu kegiatan pengamatan baru dikategorikan sebagai kegiatan pengumpulan data penelitian. Apabila memiliki kriteria sebagai berikut:

• Pertama, pengamatan digunakan dalam penelitian dan telah direncanakan secara serius.
• Kedua, pengamatan harus berkaitan dengan tujuan penelitian yang telah ditetapkan.
• Ketiga, pengamatan dicatat secara sistematik dan dihubungkan dengan proposisi. Bukan dipaparkan sebagai suatu yang hanya menarik perhatian.
• Keempat, pengamatan dapat di cek dan dikontrol mengenai keabsahannya.

2. Observasi Tidak Berstruktur

Maksud dari observasi tidak berstruktur, yaitu observasi dilakukan tanpa menggunakan guide observasi . Dengan demikian, pada observasi ini pengamat harus mampu secara pribadi mengembangkan daya pengamatannya, dalam mengamati suatu objek. Pada observasi ini, yang terpenting adalah pengamat harus menguasai “ilmu” tentang objek secara umum. Dari apa yang hendak diamati, hal mana yang membedakannya dengan observasi partisipasi. Yaitu pengamat tidak perlu memahami secara teoritis terlebih dahulu objek penelitian. Dengan demikian, akan membantu lebih banyak pekerjaannya dalam mengamati objek yang baru itu.

3. Observasi Kelompok

Bentuk observasi lain yang sering digunakan pula adalah observasi kelompok. Biasanya observasi ini dilakukan secara berkelompok terhadap suatu atau beberapa objek sekaligus. Misalnya, suatu tim peneliti yang sedang mengamati gejolak perubahan harga pasar. Akibat kenaikan BBM biasanya bekerja dengan mengamati sekian banyak gejala lain. Dalam hal ini, yang berpengaruh terhadap perubahan harga pasar tersebut.

Hal-hal yang perlu diperhatikan dalam melakukan Observasi

Beberapa hal yang perlu diperhatikan dalam melakukan pengamatan, yaitu:

• Hal-hal apa yang hendak diamati,
• Bagaimana mencatat pengamatan,
• Alat bantu pengamatan,
• Bagaimana mengatur jarak antara pengamat dan objek yang diamati.

Kemudian hal-hal tersebut di atas hendaknya dipertimbangkan sebelum seseorang melakukan observasi. Karena hal-hal tersebut di atas amat menentukan berhasil tidaknya pengamat melakukan tugasnya.

Nah, itu tadi penjelasan mengenai metode observasi dalam penelitian kualitatif. Semoga artikel ini bermanfaat, jangan lupa cek postingan artikel yang lainnya juga, ya!

Baca juga : Perbandingan Desain Penelitian Kualitatif Burhan Bungin dan Craswell

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Non-Experimental Research

32 Observational Research

Learning objectives.

• List the various types of observational research methods and distinguish between each.
• Describe the strengths and weakness of each observational research method. 

What Is Observational Research?

The term observational research is used to refer to several different types of non-experimental studies in which behavior is systematically observed and recorded. The goal of observational research is to describe a variable or set of variables. More generally, the goal is to obtain a snapshot of specific characteristics of an individual, group, or setting. As described previously, observational research is non-experimental because nothing is manipulated or controlled, and as such we cannot arrive at causal conclusions using this approach. The data that are collected in observational research studies are often qualitative in nature but they may also be quantitative or both (mixed-methods). There are several different types of observational methods that will be described below.

Naturalistic Observation

Naturalistic observation  is an observational method that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr.  Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation  could more simply involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are not aware that they are being studied. Such an approach is called disguised naturalistic observation .  Ethically, this method is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated. 

In cases where it is not ethical or practical to conduct disguised naturalistic observation, researchers can conduct  undisguised naturalistic observation where the participants are made aware of the researcher presence and monitoring of their behavior. However, one concern with undisguised naturalistic observation is  reactivity. Reactivity refers to when a measure changes participants’ behavior. In the case of undisguised naturalistic observation, the concern with reactivity is that when people know they are being observed and studied, they may act differently than they normally would. This type of reactivity is known as the Hawthorne effect . For instance, you may act much differently in a bar if you know that someone is observing you and recording your behaviors and this would invalidate the study. So disguised observation is less reactive and therefore can have higher validity because people are not aware that their behaviors are being observed and recorded. However, we now know that people often become used to being observed and with time they begin to behave naturally in the researcher’s presence. In other words, over time people habituate to being observed. Think about reality shows like Big Brother or Survivor where people are constantly being observed and recorded. While they may be on their best behavior at first, in a fairly short amount of time they are flirting, having sex, wearing next to nothing, screaming at each other, and occasionally behaving in ways that are embarrassing.

Participant Observation

Another approach to data collection in observational research is participant observation. In  participant observation , researchers become active participants in the group or situation they are studying. Participant observation is very similar to naturalistic observation in that it involves observing people’s behavior in the environment in which it typically occurs. As with naturalistic observation, the data that are collected can include interviews (usually unstructured), notes based on their observations and interactions, documents, photographs, and other artifacts. The only difference between naturalistic observation and participant observation is that researchers engaged in participant observation become active members of the group or situations they are studying. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. Like naturalistic observation, participant observation can be either disguised or undisguised. In disguised participant observation , the researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.

In a famous example of disguised participant observation, Leon Festinger and his colleagues infiltrated a doomsday cult known as the Seekers, whose members believed that the apocalypse would occur on December 21, 1954. Interested in studying how members of the group would cope psychologically when the prophecy inevitably failed, they carefully recorded the events and reactions of the cult members in the days before and after the supposed end of the world. Unsurprisingly, the cult members did not give up their belief but instead convinced themselves that it was their faith and efforts that saved the world from destruction. Festinger and his colleagues later published a book about this experience, which they used to illustrate the theory of cognitive dissonance (Festinger, Riecken, & Schachter, 1956) [1] .

In contrast with undisguised participant observation ,  the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation.  First no informed consent can be obtained and second deception is being used. The researcher is deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further, disguised participant observation is less prone to reactivity than undisguised participant observation. 

Rosenhan’s study (1973) [2]   of the experience of people in a psychiatric ward would be considered disguised participant observation because Rosenhan and his pseudopatients were admitted into psychiatric hospitals on the pretense of being patients so that they could observe the way that psychiatric patients are treated by staff. The staff and other patients were unaware of their true identities as researchers.

Another example of participant observation comes from a study by sociologist Amy Wilkins on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008) [3] . Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

One of the primary benefits of participant observation is that the researchers are in a much better position to understand the viewpoint and experiences of the people they are studying when they are a part of the social group. The primary limitation with this approach is that the mere presence of the observer could affect the behavior of the people being observed. While this is also a concern with naturalistic observation, additional concerns arise when researchers become active members of the social group they are studying because that they may change the social dynamics and/or influence the behavior of the people they are studying. Similarly, if the researcher acts as a participant observer there can be concerns with biases resulting from developing relationships with the participants. Concretely, the researcher may become less objective resulting in more experimenter bias.

Structured Observation

Another observational method is structured observation . Here the investigator makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic or participant observation. Often the setting in which the observations are made is not the natural setting. Instead, the researcher may observe people in the laboratory environment. Alternatively, the researcher may observe people in a natural setting (like a classroom setting) that they have structured some way, for instance by introducing some specific task participants are to engage in or by introducing a specific social situation or manipulation.

Structured observation is very similar to naturalistic observation and participant observation in that in all three cases researchers are observing naturally occurring behavior; however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic or participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Researchers Robert Levine and Ara Norenzayan used structured observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999) [4] . One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds. When structured observation  takes place in the complex and even chaotic “real world,” the questions of when, where, and under what conditions the observations will be made, and who exactly will be observed are important to consider. Levine and Norenzayan described their sampling process as follows:

“Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities.” (p. 186).

Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.  In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance.

As another example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979) [5] . But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

In yet another example (this one in a laboratory environment), Dov Cohen and his colleagues had observers rate the emotional reactions of participants who had just been deliberately bumped and insulted by a confederate after they dropped off a completed questionnaire at the end of a hallway. The confederate was posing as someone who worked in the same building and who was frustrated by having to close a file drawer twice in order to permit the participants to walk past them (first to drop off the questionnaire at the end of the hallway and once again on their way back to the room where they believed the study they signed up for was taking place). The two observers were positioned at different ends of the hallway so that they could read the participants’ body language and hear anything they might say. Interestingly, the researchers hypothesized that participants from the southern United States, which is one of several places in the world that has a “culture of honor,” would react with more aggression than participants from the northern United States, a prediction that was in fact supported by the observational data (Cohen, Nisbett, Bowdle, & Schwarz, 1996) [6] .

When the observations require a judgment on the part of the observers—as in the studies by Kraut and Johnston and Cohen and his colleagues—a process referred to as   coding is typically required . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that guides different observers to code them in the same way. This difficulty with coding illustrates the issue of interrater reliability, as mentioned in Chapter 4. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

One of the primary benefits of structured observation is that it is far more efficient than naturalistic and participant observation. Since the researchers are focused on specific behaviors this reduces time and expense. Also, often times the environment is structured to encourage the behaviors of interest which again means that researchers do not have to invest as much time in waiting for the behaviors of interest to naturally occur. Finally, researchers using this approach can clearly exert greater control over the environment. However, when researchers exert more control over the environment it may make the environment less natural which decreases external validity. It is less clear for instance whether structured observations made in a laboratory environment will generalize to a real world environment. Furthermore, since researchers engaged in structured observation are often not disguised there may be more concerns with reactivity.

Case Studies

A  case study   is an in-depth examination of an individual. Sometimes case studies are also completed on social units (e.g., a cult) and events (e.g., a natural disaster). Most commonly in psychology, however, case studies provide a detailed description and analysis of an individual. Often the individual has a rare or unusual condition or disorder or has damage to a specific region of the brain.

Like many observational research methods, case studies tend to be more qualitative in nature. Case study methods involve an in-depth, and often a longitudinal examination of an individual. Depending on the focus of the case study, individuals may or may not be observed in their natural setting. If the natural setting is not what is of interest, then the individual may be brought into a therapist’s office or a researcher’s lab for study. Also, the bulk of the case study report will focus on in-depth descriptions of the person rather than on statistical analyses. With that said some quantitative data may also be included in the write-up of a case study. For instance, an individual’s depression score may be compared to normative scores or their score before and after treatment may be compared. As with other qualitative methods, a variety of different methods and tools can be used to collect information on the case. For instance, interviews, naturalistic observation, structured observation, psychological testing (e.g., IQ test), and/or physiological measurements (e.g., brain scans) may be used to collect information on the individual.

HM is one of the most notorious case studies in psychology. HM suffered from intractable and very severe epilepsy. A surgeon localized HM’s epilepsy to his medial temporal lobe and in 1953 he removed large sections of his hippocampus in an attempt to stop the seizures. The treatment was a success, in that it resolved his epilepsy and his IQ and personality were unaffected. However, the doctors soon realized that HM exhibited a strange form of amnesia, called anterograde amnesia. HM was able to carry out a conversation and he could remember short strings of letters, digits, and words. Basically, his short term memory was preserved. However, HM could not commit new events to memory. He lost the ability to transfer information from his short-term memory to his long term memory, something memory researchers call consolidation. So while he could carry on a conversation with someone, he would completely forget the conversation after it ended. This was an extremely important case study for memory researchers because it suggested that there’s a dissociation between short-term memory and long-term memory, it suggested that these were two different abilities sub-served by different areas of the brain. It also suggested that the temporal lobes are particularly important for consolidating new information (i.e., for transferring information from short-term memory to long-term memory).

The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 6.1 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [7] , who allegedly learned to fear a white rat—along with other furry objects—when the researchers repeatedly made a loud noise every time the rat approached him.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [8] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst. (p. 9)

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return. (p.9)

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, he believed that her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Case studies are useful because they provide a level of detailed analysis not found in many other research methods and greater insights may be gained from this more detailed analysis. As a result of the case study, the researcher may gain a sharpened understanding of what might become important to look at more extensively in future more controlled research. Case studies are also often the only way to study rare conditions because it may be impossible to find a large enough sample of individuals with the condition to use quantitative methods. Although at first glance a case study of a rare individual might seem to tell us little about ourselves, they often do provide insights into normal behavior. The case of HM provided important insights into the role of the hippocampus in memory consolidation.

However, it is important to note that while case studies can provide insights into certain areas and variables to study, and can be useful in helping develop theories, they should never be used as evidence for theories. In other words, case studies can be used as inspiration to formulate theories and hypotheses, but those hypotheses and theories then need to be formally tested using more rigorous quantitative methods. The reason case studies shouldn’t be used to provide support for theories is that they suffer from problems with both internal and external validity. Case studies lack the proper controls that true experiments contain. As such, they suffer from problems with internal validity, so they cannot be used to determine causation. For instance, during HM’s surgery, the surgeon may have accidentally lesioned another area of HM’s brain (a possibility suggested by the dissection of HM’s brain following his death) and that lesion may have contributed to his inability to consolidate new information. The fact is, with case studies we cannot rule out these sorts of alternative explanations. So, as with all observational methods, case studies do not permit determination of causation. In addition, because case studies are often of a single individual, and typically an abnormal individual, researchers cannot generalize their conclusions to other individuals. Recall that with most research designs there is a trade-off between internal and external validity. With case studies, however, there are problems with both internal validity and external validity. So there are limits both to the ability to determine causation and to generalize the results. A final limitation of case studies is that ample opportunity exists for the theoretical biases of the researcher to color or bias the case description. Indeed, there have been accusations that the woman who studied HM destroyed a lot of her data that were not published and she has been called into question for destroying contradictory data that didn’t support her theory about how memories are consolidated. There is a fascinating New York Times article that describes some of the controversies that ensued after HM’s death and analysis of his brain that can be found at: https://www.nytimes.com/2016/08/07/magazine/the-brain-that-couldnt-remember.html?_r=0

Archival Research

Another approach that is often considered observational research involves analyzing archival data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005) [9] . In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988) [10] . In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men. Pearson’s  r  was +.25.

This method is an example of  content analysis —a family of systematic approaches to measurement using complex archival data. Just as structured observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Media Attributions

• What happens when you remove the hippocampus? – Sam Kean by TED-Ed licensed under a standard YouTube License
• Pappenheim 1882  by unknown is in the  Public Domain .
• Festinger, L., Riecken, H., & Schachter, S. (1956). When prophecy fails: A social and psychological study of a modern group that predicted the destruction of the world. University of Minnesota Press. ↵
• Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵
• Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301. ↵
• Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205. ↵
• Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553. ↵
• Cohen, D., Nisbett, R. E., Bowdle, B. F., & Schwarz, N. (1996). Insult, aggression, and the southern culture of honor: An "experimental ethnography." Journal of Personality and Social Psychology, 70 (5), 945-960. ↵
• Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
• Freud, S. (1961).  Five lectures on psycho-analysis . New York, NY: Norton. ↵
• Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110. ↵
• Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27. ↵

Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.

An observational method that involves observing people’s behavior in the environment in which it typically occurs.

When researchers engage in naturalistic observation by making their observations as unobtrusively as possible so that participants are not aware that they are being studied.

Where the participants are made aware of the researcher presence and monitoring of their behavior.

Refers to when a measure changes participants’ behavior.

In the case of undisguised naturalistic observation, it is a type of reactivity when people know they are being observed and studied, they may act differently than they normally would.

Researchers become active participants in the group or situation they are studying.

Researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.

Researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation.

When a researcher makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic or participant observation.

A part of structured observation whereby the observers use a clearly defined set of guidelines to "code" behaviors—assigning specific behaviors they are observing to a category—and count the number of times or the duration that the behavior occurs.

An in-depth examination of an individual.

A family of systematic approaches to measurement using qualitative methods to analyze complex archival data.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Statistics By Jim

Making statistics intuitive

What is an Observational Study: Definition & Examples

By Jim Frost 10 Comments

What is an Observational Study?

An observational study uses sample data to find correlations in situations where the researchers do not control the treatment, or independent variable, that relates to the primary research question. The definition of an observational study hinges on the notion that the researchers only observe subjects and do not assign them to the control and treatment groups. That’s the key difference between an observational study vs experiment. These studies are also known as quasi-experiments and correlational studies .

True experiments assign subject to the experimental groups where the researchers can manipulate the conditions. Unfortunately, random assignment is not always possible. For these cases, you can conduct an observational study.

In this post, learn about the types of observational studies, why they are susceptible to confounding variables, and how they compare to experiments. I’ll close this post by reviewing a published observational study about vitamin supplement usage.

Observational Study Definition

In an observational study, the researchers only observe the subjects and do not interfere or try to influence the outcomes. In other words, the researchers do not control the treatments or assign subjects to experimental groups. Instead, they observe and measure variables of interest and look for relationships between them. Usually, researchers conduct observational studies when it is difficult, impossible, or unethical to assign study participants to the experimental groups randomly. If you can’t randomly assign subjects to the treatment and control groups, then you observe the subjects in their self-selected states.

Observational Study vs Experiment

Randomized experiments provide better results than observational studies. Consequently, you should always use a randomized experiment whenever possible. However, if randomization is not possible, science should not come to a halt. After all, we still want to learn things, discover relationships, and make discoveries. For these cases, observational studies are a good alternative to a true experiment. Let’s compare the differences between an observational study vs. an experiment.

Random assignment in an experiment reduces systematic differences between experimental groups at the beginning of the study, which increases your confidence that the treatments caused any differences between groups you observe at the end of the study. In contrast, an observational study uses self-formed groups that can have pre-existing differences, which introduces the problem of confounding variables. More on that later!

In a randomized experiment, randomization tends to equalize confounders between groups and, thereby, prevents problems. In my post about random assignment , I describe that process as an elegant solution for confounding variables. You don’t need to measure or even know which variables are confounders, and randomization will still mitigate their effects. Additionally, you can use control variables in an experiment to keep the conditions as consistent as possible. For more detail about the differences, read Observational Study vs. Experiment .

If you’re looking for a middle ground choice between observational studies vs experiments, consider using a quasi-experimental design. These methods don’t require you to randomly assign participants to the experimental groups and still allow you to draw better causal conclusions about an intervention than an observational study. Learn more about Quasi-Experimental Design Overview & Examples .

Related posts : Experimental Design: Definition and Examples , Randomized Controlled Trials (RCTs) , and Control Groups in Experiments

Observational Study Examples

Consider using an observational study when random assignment for an experiment is problematic. This approach allows us to proceed and draw conclusions about effects even though we can’t control the independent variables. The following observational study examples will help you understand when and why to use them.

For example, if you’re studying how depression affects performance of an activity, it’s impossible to assign subjects to the depression and control group randomly. However, you can have subjects with and without depression perform the activity and compare the results in an observational study.

Or imagine trying to assign subjects to cigarette smoking and non-smoking groups randomly?! However, you can observe people in both groups and assess the differences in health outcomes in an observational study.

Suppose you’re studying a treatment for a disease. Ideally, you recruit a group of patients who all have the disease, and then randomly assign them to the treatment and control group. However, it’s unethical to withhold the treatment, which rules out a control group. Instead, you can compare patients who voluntarily do not use the medicine to those who do use it.

In all these observational study examples, the researchers do not assign subjects to the experimental groups. Instead, they observe people who are already in these groups and compare the outcomes. Hence, the scientists must use an observational study vs. an experiment.

Types of Observational Studies

The observational study definition states that researchers only observe the outcomes and do not manipulate or control factors . Despite this limitation, there various types of observational studies.

The following experimental designs are three standard types of observational studies.

• Cohort Study : A longitudinal observational study that follows a group who share a defining characteristic. These studies frequently determine whether exposure to risk factor affects an outcome over time.
• Case-Control Study : A retrospective observational study that compares two existing groups—the case group with the condition and the control group without it. Researchers compare the groups looking for potential risk factors for the condition.
• Cross-Sectional Study : Takes a snapshot of a moment in time so researchers can understand the prevalence of outcomes and correlations between variables at that instant.

Qualitative research studies are usually observational in nature, but they collect non-numeric data and do not perform statistical analyses.

Retrospective studies must be observational.

Later in this post, we’ll closely examine a quantitative observational study example that assesses vitamin supplement consumption and how that affects the risk of death. It’s possible to use random assignment to place each subject in either the vitamin treatment group or the control group. However, the study assesses vitamin consumption in 40,000 participants over the course of two decades. It’s unrealistic to enforce the treatment and control protocols over such a long time for so many people!

Drawbacks of Observational Studies

While observational studies get around the inability to assign subjects randomly, this approach opens the door to the problem of confounding variables. A confounding variable, or confounder, correlates with both the experimental groups and the outcome variable. Because there is no random process that equalizes the experimental groups in an observational study, confounding variables can systematically differ between groups when the study begins. Consequently, confounders can be the actual cause for differences in outcome at the end of the study rather than the primary variable of interest. If an experiment does not account for confounding variables, confounders can bias the results and create spurious correlations .

Performing an observational study can decrease the internal validity of your study but increase the external validity. Learn more about internal and external validity .

Let’s see how this works. Imagine an observational study that compares people who take vitamin supplements to those who do not. People who use vitamin supplements voluntarily will tend to have other healthy habits that exist at the beginning of the study. These healthy habits are confounding variables. If there are differences in health outcomes at the end of the study, it’s possible that these healthy habits actually caused them rather than the vitamin consumption itself. In short, confounders confuse the results because they provide alternative explanations for the differences.

Despite the limitations, an observational study can be a valid approach. However, you must ensure that your research accounts for confounding variables. Fortunately, there are several methods for doing just that!

Learn more about Correlation vs. Causation: Understanding the Differences .

Accounting for Confounding Variables in an Observational Study

Because observational studies don’t use random assignment, confounders can be distributed disproportionately between conditions. Consequently, experimenters need to know which variables are confounders, measure them, and then use a method to account for them. It involves more work, and the additional measurements can increase the costs. And there’s always a chance that researchers will fail to identify a confounder, not account for it, and produce biased results. However, if randomization isn’t an option, then you probably need to consider an observational study.

Trait matching and statistically controlling confounders using multivariate procedures are two standard approaches for incorporating confounding variables.

Related post : Causation versus Correlation in Statistics

Matching in Observational Studies

Matching is a technique that involves selecting study participants with similar characteristics outside the variable of interest or treatment. Rather than using random assignment to equalize the experimental groups, the experimenters do it by matching observable characteristics. For every participant in the treatment group, the researchers find a participant with comparable traits to include in the control group. Matching subjects facilitates valid comparisons between those groups. The researchers use subject-area knowledge to identify characteristics that are critical to match.

For example, a vitamin supplement study using matching will select subjects who have similar health-related habits and attributes. The goal is that vitamin consumption will be the primary difference between the groups, which helps you attribute differences in health outcomes to vitamin consumption. However, the researchers are still observing participants who decide whether they consume supplements.

Matching has some drawbacks. The experimenters might not be aware of all the relevant characteristics they need to match. In other words, the groups might be different in an essential aspect that the researchers don’t recognize. For example, in the hypothetical vitamin study, there might be a healthy habit or attribute that affects the outcome that the researchers don’t measure and match. These unmatched characteristics might cause the observed differences in outcomes rather than vitamin consumption.

Learn more about Matched Pairs Design: Uses & Examples .

Using Multiple Regression in Observational Studies

Random assignment and matching use different methods to equalize the experimental groups in an observational study. However, statistical techniques, such as multiple regression analysis , don’t try to equalize the groups but instead use a model that accounts for confounding variables. These studies statistically control for confounding variables.

In multiple regression analysis, including a variable in the model holds it constant while you vary the variable/treatment of interest. For information about this property, read my post When Should I Use Regression Analysis?

As with matching, the challenge is to identify, measure, and include all confounders in the regression model. Failure to include a confounding variable in a regression model can cause omitted variable bias to distort your results.

Next, we’ll look at a published observational study that uses multiple regression to account for confounding variables.

Related post : Independent and Dependent Variables in a Regression Model

Vitamin Supplement Observational Study Example

Murso et al. (2011)* use a longitudinal observational study that ran 22 years to assess differences in death rates for subjects who used vitamin supplements regularly compared to those who did not use them. This study used surveys to record the characteristics of approximately 40,000 participants. The surveys asked questions about potential confounding variables such as demographic information, food intake, health details, physical activity, and, of course, supplement intake.

Because this is an observational study, the subjects decided for themselves whether they were taking vitamin supplements. Consequently, it’s safe to assume that supplement users and non-users might be different in other ways. From their article, the researchers found the following pre-existing differences between the two groups:

Supplement users had a lower prevalence of diabetes mellitus, high blood pressure, and smoking status; a lower BMI and waist to hip ratio, and were less likely to live on a farm. Supplement users had a higher educational level, were more physically active and were more likely to use estrogen replacement therapy. Also, supplement users were more likely to have a lower intake of energy, total fat, and monounsaturated fatty acids, saturated fatty acids and to have a higher intake of protein, carbohydrates, polyunsaturated fatty acids, alcohol, whole grain products, fruits, and vegetables.

Whew! That’s a long list of differences! Supplement users were different from non-users in a multitude of ways that are likely to affect their risk of dying. The researchers must account for these confounding variables when they compare supplement users to non-users. If they do not, their results can be biased.

This example illustrates a key difference between an observational study vs experiment. In a randomized experiment, the randomization would have equalized the characteristics of those the researchers assigned to the treatment and control groups. Instead, the study works with self-sorted groups that have numerous pre-existing differences!

Using Multiple Regression to Statistically Control for Confounders

To account for these initial differences in the vitamin supplement observational study, the researchers use regression analysis and include the confounding variables in the model.

The researchers present three regression models. The simplest model accounts only for age and caloric intake. Next, are two models that include additional confounding variables beyond age and calories. The first model adds various demographic information and seven health measures. The second model includes everything in the previous model and adds several more specific dietary intake measures. Using statistical significance as a guide for specifying the correct regression model , the researchers present the model with the most variables as the basis for their final results.

It’s instructive to compare the raw results and the final regression results.

Raw results

The raw differences in death risks for consumers of folic acid, vitamin B6, magnesium, zinc, copper, and multivitamins are NOT statistically significant. However, the raw results show a significant reduction in the death risk for users of B complex, C, calcium, D, and E.

However, those are the raw results for the observational study, and they do not control for the long list of differences between the groups that exist at the beginning of the study. After using the regression model to control for the confounding variables statistically, the results change dramatically.

Adjusted results

Of the 15 supplements that the study tracked in the observational study, researchers found consuming seven of these supplements were linked to a statistically significant INCREASE in death risk ( p-value < 0.05): multivitamins (increase in death risk 2.4%), vitamin B6 (4.1%), iron (3.9%), folic acid (5.9%), zinc (3.0%), magnesium (3.6%), and copper (18.0%). Only calcium was associated with a statistically significant reduction in death risk of 3.8%.

In short, the raw results suggest that those who consume supplements either have the same or lower death risks than non-consumers. However, these results do not account for the multitude of healthier habits and attributes in the group that uses supplements.

In fact, these confounders seem to produce most of the apparent benefits in the raw results because, after you statistically control the effects of these confounding variables, the results worsen for those who consume vitamin supplements. The adjusted results indicate that most vitamin supplements actually increase your death risk!

This research illustrates the differences between an observational study vs experiment. Namely how the pre-existing differences between the groups allow confounders to bias the raw results, making the vitamin consumption outcomes look better than they really are.

In conclusion, if you can’t randomly assign subjects to the experimental groups, an observational study might be right for you. However, be aware that you’ll need to identify, measure, and account for confounding variables in your experimental design.

Jaakko Mursu, PhD; Kim Robien, PhD; Lisa J. Harnack, DrPH, MPH; Kyong Park, PhD; David R. Jacobs Jr, PhD; Dietary Supplements and Mortality Rate in Older Women: The Iowa Women’s Health Study ; Arch Intern Med . 2011;171(18):1625-1633.

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Reader Interactions

December 30, 2023 at 5:05 am

I see, but our professor required us to indicate what year it was put into the article. May you tell me what year was this published originally? <3

December 30, 2023 at 3:40 pm

December 29, 2023 at 10:46 am

Hi, may I use your article as a citation for my thesis paper? If so, may I know the exact date you published this article? Thank you!

December 29, 2023 at 2:13 pm

Definitely feel free to cite this article! 🙂

When citing online resources, you typically use an “Accessed” date rather than a publication date because online content can change over time. For more information, read Purdue University’s Citing Electronic Resources .

November 18, 2021 at 10:09 pm

Love your content and has been very helpful!

Can you please advise the question below using an observational data set:

I have three years of observational GPS data collected on athletes (2019/2020/2021). Approximately 14-15 athletes per game and 8 games per year. The GPS software outputs 50+ variables for each athlete in each game, which we have narrowed down to 16 variables of interest from previous research.

2 factors 1) Period (first half, second half, and whole game), 2) Position (two groups with three subgroups in each – forwards (group 1, group 2, group 3) and backs (group 1, group 2, group 3))

16 variables of interest – all numerical and scale variables. Some of these are correlated, but not all.

My understanding is that I can use a oneway ANOVA for each year on it’s own, using one factor at a time (period or position) with post hoc analysis. This is fine, if data meets assumptions and is normally distributed. This tells me any significant interactions between variables of interest with chosen factor. For example, with position factor, do forwards in group 1 cover more total running distance than forwards in group 2 or backs in group 3.

However, I want to go deeper with my analysis. If I want to see if forwards in group 1 cover more total running distance in period 1 than backs in group 3 in the same period, I need an additional factor and the oneway ANOVA does not suit. Therefore I can use a twoway ANOVA instead of 2 oneway ANOVA’s and that solves the issue, correct?

This is complicated further by looking to compare 2019 to 2020 or 2019 to 2021 to identify changes over time, which would introduce a third independent variable.

I believe this would require a threeway ANOVA for this observational data set. 3 factors – Position, Period, and Year?

Are there any issues or concerns you see at first glance?

I appreciate your time and consideration.

April 12, 2021 at 2:02 pm

Could an observational study use a correlational design.

e.g. measuring effects of two variables on happiness, if you’re not intervening.

April 13, 2021 at 12:14 am

Typically, with observational studies, you’d want to include potential confounders, etc. Consequently, I’ve seen regression analysis used more frequently for observational studies to be able to control for other things because you’re not using randomization. You could use correlation to observe the relationship. However, you wouldn’t be controlling for potential confounding variables. Just something to consider.

April 11, 2021 at 1:28 pm

Hi, If I am to administer moderate doses of coffee for a hypothetical experiment, does it raise ethical concerns? Can I use random assignment for it?

April 11, 2021 at 4:06 pm

I don’t see any inherent ethical problems here as long as you describe the participant’s experience in the experiment including the coffee consumption. They key with human subjects is “informed consent.” They’re agreeing to participate based on a full and accurate understanding of what participation involves. Additionally, you as a researcher, understand the process well enough to be able to ensure their safety.

In your study, as long as subject know they’ll be drinking coffee and agree to that, I don’t see a problem. It’s a proven safe substance for the vast majority of people. If potential subjects are aware of the need to consume coffee, they can determine whether they are ok with that before agreeing to participate.

June 17, 2019 at 4:51 am

Really great article which explains observational and experimental study very well. It presents broad picture with the case study which helped a lot in understanding the core concepts. Thanks

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Observational and interventional study design types; an overview

The appropriate choice in study design is essential for the successful execution of biomedical and public health research. There are many study designs to choose from within two broad categories of observational and interventional studies. Each design has its own strengths and weaknesses, and the need to understand these limitations is necessary to arrive at correct study conclusions.

Observational study designs, also called epidemiologic study designs, are often retrospective and are used to assess potential causation in exposure-outcome relationships and therefore influence preventive methods. Observational study designs include ecological designs, cross sectional, case-control, case-crossover, retrospective and prospective cohorts. An important subset of observational studies is diagnostic study designs, which evaluate the accuracy of diagnostic procedures and tests as compared to other diagnostic measures. These include diagnostic accuracy designs, diagnostic cohort designs, and diagnostic randomized controlled trials.

Interventional studies are often prospective and are specifically tailored to evaluate direct impacts of treatment or preventive measures on disease. Each study design has specific outcome measures that rely on the type and quality of data utilized. Additionally, each study design has potential limitations that are more severe and need to be addressed in the design phase of the study. This manuscript is meant to provide an overview of study design types, strengths and weaknesses of common observational and interventional study designs.

Introduction

Study design plays an important role in the quality, execution, and interpretation of biomedical and public health research ( 1 – 12 ). Each study design has their own inherent strengths and weaknesses, and there can be a general hierarchy in study designs, however, any hierarchy cannot be applied uniformly across study design types ( 3 , 5 , 6 , 9 ). Epidemiological and interventional research studies include three elements; 1) definition and measure of exposure in two or more groups, 2) measure of health outcome(s) in these same groups, and 3) statistical comparison made between groups to assess potential relationships between the exposure and outcome, all of which are defined by the researcher ( 1 – 4 , 8 , 13 ). The measure of exposure in epidemiologic studies may be tobacco use (“Yes” vs . “No”) to define the two groups and may be the treatment (Active drug vs . placebo) in interventional studies. Health outcome(s) can be the development of a disease or symptom (e.g. lung cancer) or curing a disease or symptom (e.g. reduction of pain). Descriptive studies, which are not epidemiological or interventional, lack one or more of these elements and have limited application. High quality epidemiological and interventional studies contain detailed information on the design, execution and interpretation of results, with methodology clearly written and able to be reproduced by other researchers.

Research is generally considered as primary or secondary research. Primary research relies upon data gathered from original research expressly for that purpose ( 1 , 3 , 5 ). Secondary research focuses on single or multiple data sources that are not collected for a single research purpose ( 14 , 15 ). Secondary research includes meta-analyses and best practice guidelines for treatments. This paper will focus on the study designs and their strengths, weaknesses, and common statistical outcomes of primary research.

The choice of a study design hinges on many factors, including prior research, availability of study participants, funding, and time constraints. One common decision point is the desire to suggest causation. The most common causation criteria are proposed by Hill ( 16 ). Of these, demonstrating temporality is the only mandatory criterion for suggesting temporality. Therefore, prospective studies that follow study participants forward through time, including prospective cohort studies and interventional studies, are best suited for suggesting causation. Causal conclusions cannot be proven from an observational study. Additionally, causation between an exposure and an outcome cannot be proven by one study alone; multiple studies across different populations should be considered when making causation assessments ( 17 ).

Primary research has been categorized in different ways. Common categorization schema include temporal nature of the study design (retrospective or prospective), usability of the study results (basic or applied), investigative purpose (descriptive or analytical), purpose (prevention, diagnosis or treatment), or role of the investigator (observational or interventional). This manuscript categorizes study designs by observational and interventional criteria, however, other categorization methods are described as well.

Observational and interventional studies

Within primary research there are observational studies and interventional studies. Observational studies, also called epidemiological studies, are those where the investigator is not acting upon study participants, but instead observing natural relationships between factors and outcomes. Diagnostic studies are classified as observational studies, but are a unique category and will be discussed independently. Interventional studies, also called experimental studies, are those where the researcher intercedes as part of the study design. Additionally, study designs may be classified by the role that time plays in the data collection, either retrospective or prospective. Retrospective studies are those where data are collected from the past, either through records created at that time or by asking participants to remember their exposures or outcomes. Retrospective studies cannot demonstrate temporality as easily and are more prone to different biases, particularly recall bias. Prospective studies follow participants forward through time, collecting data in the process. Prospective studies are less prone to some types of bias and can more easily demonstrate that the exposure preceded the disease, thereby more strongly suggesting causation. Table 1 describes the broad categories of observational studies: the disease measures applicable to each, the appropriate measures of risk, and temporality of each study design. Epidemiologic measures include point prevalence, the proportion of participants with disease at a given point in time, period prevalence, the proportion of participants with disease within a specified time frame, and incidence, the accumulation of new cases over time. Measures of risk are generally categorized into two categories: those that only demonstrate an association, such as an odds ratio (and some other measures), and those that demonstrate temporality and therefore suggest causation, such as hazard ratio. Table 2 outlines the strengths and weaknesses of each observational study design.

Observational study design measures of disease, measures of risk, and temporality.

Observational study design strengths and weaknesses.

Observational studies

Ecological study design.

The most basic observational study is an ecological study. This study design compares clusters of people, usually grouped based on their geographical location or temporal associations ( 1 , 2 , 6 , 9 ). Ecological studies assign one exposure level for each distinct group and can provide a rough estimation of prevalence of disease within a population. Ecological studies are generally retrospective. An example of an ecological study is the comparison of the prevalence of obesity in the United States and France. The geographic area is considered the exposure and the outcome is obesity. There are inherent potential weaknesses with this approach, including loss of data resolution and potential misclassification ( 10 , 11 , 13 , 18 , 19 ). This type of study design also has additional weaknesses. Typically these studies derive their data from large databases that are created for purposes other than research, which may introduce error or misclassification ( 10 , 11 ). Quantification of both the number of cases and the total population can be difficult, leading to error or bias. Lastly, due to the limited amount of data available, it is difficult to control for other factors that may mask or falsely suggest a relationship between the exposure and the outcome. However, ecological studies are generally very cost effective and are a starting point for hypothesis generation.

Proportional mortality ratio study design

Proportional mortality ratio studies (PMR) utilize the defined well recorded outcome of death and subsequent records that are maintained regarding the decedent ( 1 , 6 , 8 , 20 ). By using records, this study design is able to identify potential relationships between exposures, such as geographic location, occupation, or age and cause of death. The epidemiological outcomes of this study design are proportional mortality ratio and standardized mortality ratio. In general these are the ratio of the proportion of cause-specific deaths out of all deaths between exposure categories ( 20 ). As an example, these studies can address questions about higher proportion of cardiovascular deaths among different ethnic and racial groups ( 21 ). A significant drawback to the PMR study design is that these studies are limited to death as an outcome ( 3 , 5 , 22 ). Additionally, the reliance on death records makes it difficult to control for individual confounding factors, variables that either conceal or falsely demonstrate associations between the exposure and outcome. An example of a confounder is tobacco use confounding the relationship between coffee intake and cardiovascular disease. Historically people often smoked and drank coffee while on coffee breaks. If researchers ignore smoking they would inaccurately find a strong relationship between coffee use and cardiovascular disease, where some of the risk is actually due to smoking. There are also concerns regarding the accuracy of death certificate data. Strengths of the study design include the well-defined outcome of death, the relative ease and low cost of obtaining data, and the uniformity of collection of these data across different geographical areas.

Cross-sectional study design

Cross-sectional studies are also called prevalence studies because one of the main measures available is study population prevalence ( 1 – 12 ). These studies consist of assessing a population, as represented by the study sample, at a single point in time. A common cross-sectional study type is the diagnostic accuracy study, which is discussed later. Cross-sectional study samples are selected based on their exposure status, without regard for their outcome status. Outcome status is obtained after participants are enrolled. Ideally, a wider distribution of exposure will allow for a higher likelihood of finding an association between the exposure and outcome if one exists ( 1 – 3 , 5 , 8 ). Cross-sectional studies are retrospective in nature. An example of a cross-sectional study would be enrolling participants who are either current smokers or never smokers, and assessing whether or not they have respiratory deficiencies. Random sampling of the population being assessed is more important in cross-sectional studies as compared to other observational study designs. Selection bias from non-random sampling may result in flawed measure of prevalence and calculation of risk. The study sample is assessed for both exposure and outcome at a single point in time. Because both exposure and outcome are assessed at the same time, temporality cannot be demonstrated, i.e. it cannot be demonstrated that the exposure preceded the disease ( 1 – 3 , 5 , 8 ). Point prevalence and period prevalence can be calculated in cross-sectional studies. Measures of risk for the exposure-outcome relationship that can be calculated in cross-sectional study design are odds ratio, prevalence odds ratio, prevalence ratio, and prevalence difference. Cross-sectional studies are relatively inexpensive and have data collected on an individual which allows for more complete control for confounding. Additionally, cross-sectional studies allow for multiple outcomes to be assessed simultaneously.

Case-control study design

Case-control studies were traditionally referred to as retrospective studies, due to the nature of the study design and execution ( 1 – 12 , 23 , 24 ). In this study design, researchers identify study participants based on their case status, i.e. diseased or not diseased. Quantification of the number of individuals among the cases and the controls who are exposed allow for statistical associations between exposure and outcomes to be established ( 1 – 3 , 5 , 8 ). An example of a case control study is analysing the relationship between obesity and knee replacement surgery. Cases are participants who have had knee surgery, and controls are a random sampling of those who have not, and the comparison is the relative odds of being obese if you have knee surgery as compared to those that do not. Matching on one or more potential confounders allows for minimization of those factors as potential confounders in the exposure-outcome relationship ( 1 – 3 , 5 , 8 ). Additionally, case-control studies are at increased risk for bias, particularly recall bias, due to the known case status of study participants ( 1 – 3 , 5 , 8 ). Other points of consideration that have specific weight in case-control studies include the appropriate selection of controls that balance generalizability and minimize bias, the minimization of survivor bias, and the potential for length time bias ( 25 ). The largest strength of case-control studies is that this study design is the most efficient study design for rare diseases. Additional strengths include low cost, relatively fast execution compared to cohort studies, the ability to collect individual participant specific data, the ability to control for multiple confounders, and the ability to assess multiple exposures of interest. The measure of risk that is calculated in case-control studies is the odds ratio, which are the odds of having the exposure if you have the disease. Other measures of risk are not applicable to case-control studies. Any measure of prevalence and associated measures, such as prevalence odds ratio, in a case-control study is artificial because the researcher arbitrarily sets the proportion of cases to non-cases in this study design. Temporality can be suggested, however, it is rarely definitively demonstrated because it is unknown if the development of the disease truly preceded the exposure. It should be noted that for certain outcomes, particularly death, the criteria for demonstrating temporality in that specific exposure-outcome relationship are met and the use of relative risk as a measure of risk may be justified.

Case-crossover study design

A case-crossover study relies upon an individual to act as their own control for comparison issues, thereby minimizing some potential confounders ( 1 , 5 , 12 ). This study design should not be confused with a crossover study design which is an interventional study type and is described below. For case-crossover studies, cases are assessed for their exposure status immediately prior to the time they became a case, and then compared to their own exposure at a prior point where they didn’t become a case. The selection of the prior point for comparison issues is often chosen at random or relies upon a mean measure of exposure over time. Case-crossover studies are always retrospective. An example of a case-crossover study would be evaluating the exposure of talking on a cell phone and being involved in an automobile crash. Cases are drivers involved in a crash and the comparison is that same driver at a random timeframe where they were not involved in a crash. These types of studies are particularly good for exposure-outcome relationships where the outcome is acute and well defined, e.g. electrocutions, lacerations, automobile crashes, etc. ( 1 , 5 ). Exposure-outcome relationships that are assessed using case-crossover designs should have health outcomes that do not have a subclinical or undiagnosed period prior to becoming a “case” in the study ( 12 ). The exposure is cell phone use during the exposure periods, both before the crash and during the control period. Additionally, the reliance upon prior exposure time requires that the exposure not have an additive or cumulative effect over time ( 1 , 5 ). Case-crossover study designs are at higher risk for having recall bias as compared with other study designs ( 12 ). Study participants are more likely to remember an exposure prior to becoming a case, as compared to not becoming a case.

Retrospective and prospective cohort study design

Cohort studies involve identifying study participants based on their exposure status and either following them through time to identify which participants develop the outcome(s) of interest, or look back at data that were created in the past, prior to the development of the outcome. Prospective cohort studies are considered the gold standard of observational research ( 1 – 3 , 5 , 8 , 10 , 11 ). These studies begin with a cross-sectional study to categorize exposure and identify cases at baseline. Disease-free participants are then followed and cases are measured as they develop. Retrospective cohort studies also begin with a cross-sectional study to categorize exposure and identify cases. Exposures are then measured based on records created at that time. Additionally, in an ideal retrospective cohort, case status is also tracked using historical data that were created at that point in time. Occupational groups, particularly those that have regular surveillance or certifications such as Commercial Truck Drivers, are particularly well positioned for retrospective cohort studies because records of both exposure and outcome are created as part of commercial and regulatory purposes ( 8 ). These types of studies have the ability to demonstrate temporality and therefore identify true risk factors, not associated factors, as can be done in other types of studies.

Cohort studies are the only observational study that can calculate incidence, both cumulative incidence and an incidence rate ( 1 , 3 , 5 , 6 , 10 , 11 ). Also, because the inception of a cohort study is identical to a cross-sectional study, both point prevalence and period prevalence can be calculated. There are many measures of risk that can be calculated from cohort study data. Again, the measures of risk for the exposure-outcome relationship that can be calculated in cross-sectional study design of odds ratio, prevalence odds ratio, prevalence ratio, and prevalence difference can be calculated in cohort studies as well. Measures of risk that leverage a cohort study’s ability to calculate incidence include incidence rate ratio, relative risk, risk ratio, and hazard ratio. These measures that demonstrate temporality are considered stronger measures for demonstrating causation and identification of risk factors.

Diagnostic testing and evaluation study designs

A specific study design is the diagnostic accuracy study, which is often used as part of the clinical decision making process. Diagnostic accuracy study designs are those that compare a new diagnostic method with the current “gold standard” diagnostic procedure in a cross-section of both diseased and healthy study participants. Gold standard diagnostic procedures are the current best-practice for diagnosing a disease. An example is comparing a new rapid test for a cancer with the gold standard method of biopsy. There are many intricacies to diagnostic testing study designs that should be considered. The proper selection of the gold standard evaluation is important for defining the true measures of accuracy for the new diagnostic procedure. Evaluations of diagnostic test results should be blinded to the case status of the participant. Similar to the intention-to-treat concept discussed later in interventional studies, diagnostic tests have a procedure of analyses called intention to diagnose (ITD), where participants are analysed in the diagnostic category they were assigned, regardless of the process in which a diagnosis was obtained. Performing analyses according to an a priori defined protocol, called per protocol analyses (PP or PPA), is another potential strength to diagnostic study testing. Many measures of the new diagnostic procedure, including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio can be calculated. These measures of the diagnostic test allow for comparison with other diagnostic tests and aid the clinician in determining which test to utilize.

Interventional study designs

Interventional study designs, also called experimental study designs, are those where the researcher intervenes at some point throughout the study. The most common and strongest interventional study design is a randomized controlled trial, however, there are other interventional study designs, including pre-post study design, non-randomized controlled trials, and quasi-experiments ( 1 , 5 , 13 ). Experimental studies are used to evaluate study questions related to either therapeutic agents or prevention. Therapeutic agents can include prophylactic agents, treatments, surgical approaches, or diagnostic tests. Prevention can include changes to protective equipment, engineering controls, management, policy or any element that should be evaluated as to a potential cause of disease or injury.

Pre-post study design

A pre-post study measures the occurrence of an outcome before and again after a particular intervention is implemented. A good example is comparing deaths from motor vehicle crashes before and after the enforcement of a seat-belt law. Pre-post studies may be single arm, one group measured before the intervention and again after the intervention, or multiple arms, where there is a comparison between groups. Often there is an arm where there is no intervention. The no-intervention arm acts as the control group in a multi-arm pre-post study. These studies have the strength of temporality to be able to suggest that the outcome is impacted by the intervention, however, pre-post studies do not have control over other elements that are also changing at the same time as the intervention is implemented. Therefore, changes in disease occurrence during the study period cannot be fully attributed to the specific intervention. Outcomes measured for pre-post intervention studies may be binary health outcomes such as incidence or prevalence, or mean values of a continuous outcome such as systolic blood pressure may also be used. The analytic methods of pre-post studies depend on the outcome being measured. If there are multiple treatment arms, it is also likely that the difference from beginning to end within each treatment arm are analysed.

Non-randomized trial study design

Non-randomized trials are interventional study designs that compare a group where an intervention was performed with a group where there was no intervention. These are convenient study designs that are most often performed prospectively and can suggest possible relationships between the intervention and the outcome. However, these study designs are often subject to many types of bias and error and are not considered a strong study design.

Randomized controlled trial study design

Randomized controlled trials (RCTs) are the most common type of interventional study, and can have many modifications ( 26 – 28 ). These trials take a homogenous group of study participants and randomly divide them into two separate groups. If the randomization is successful then these two groups should be the same in all respects, both measured confounders and unmeasured factors. The intervention is then implemented in one group and not the other and comparisons of intervention efficacy between the two groups are analysed. Theoretically, the only difference between the two groups through the entire study is the intervention. An excellent example is the intervention of a new medication to treat a specific disease among a group of patients. This randomization process is arguably the largest strength of an RCT ( 26 – 28 ). Additional methodological elements are utilized among RCTs to further strengthen the causal implication of the intervention’s impact. These include allocation concealment, blinding, measuring compliance, controlling for co-interventions, measuring dropout, analysing results by intention to treat, and assessing each treatment arm at the same time point in the same manner.

Crossover randomized controlled trial study design

A crossover RCT is a type of interventional study design where study participants intentionally “crossover” to the other treatment arm. This should not be confused with the observational case-crossover design. A crossover RCT begins the same as a traditional RCT, however, after the end of the first treatment phase, each participant is re-allocated to the other treatment arm. There is often a wash-out period in between treatment periods. This design has many strengths, including demonstrating reversibility, compensating for unsuccessful randomization, and improving study efficiency by not using time to recruit subjects.

Allocation concealment theoretically guarantees that the implementation of the randomization is free from bias. This is done by ensuring that the randomization scheme is concealed from all individuals involved ( 26 – 30 ). A third party who is not involved in the treatment or assessment of the trial creates the randomization schema and study participants are randomized according to that schema. By concealing the schema, there is a minimization of potential deviation from that randomization, either consciously or otherwise by the participant, researcher, provider, or assessor. The traditional method of allocation concealment relies upon sequentially numbered opaque envelopes with the treatment allocation inside. These envelopes are generated before the study begins using the selected randomization scheme. Participants are then allocated to the specific intervention arm in the pre-determined order dictated by the schema. If allocation concealment is not utilized, there is the possibility of selective enrolment into an intervention arm, potentially with the outcome of biased results.

Blinding in an RCT is withholding the treatment arm from individuals involved in the study. This can be done through use of placebo pills, deactivated treatment modalities, or sham therapy. Sham therapy is a comparison procedure or treatment which is identical to the investigational intervention except it omits a key therapeutic element, thus rendering the treatment ineffective. An example is a sham cortisone injection, where saline solution of the same volume is injected instead of cortisone. This helps ensure that patients do not know if they are receiving the active or control treatment. The process of blinding is utilized to help ensure equal treatment of the different groups, therefore continuing to isolate the difference in outcome between groups to only the intervention being administered ( 28 – 31 ). Blinding within an RCT includes patient blinding, provider blinding, or assessor blinding. In some situations it is difficult or impossible to blind one or more of the parties involved, but an ideal study would have all parties blinded until the end of the study ( 26 – 28 , 31 , 32 ).

Compliance is the degree of how well study participants adhere to the prescribed intervention. Compliance or non-compliance to the intervention can have a significant impact on the results of the study ( 26 – 29 ). If there is a differentiation in the compliance between intervention arms, that differential can mask true differences, or erroneously conclude that there are differences between the groups when one does not exist. The measurement of compliance in studies addresses the potential for differences observed in intervention arms due to intervention adherence, and can allow for partial control of differences either through post hoc stratification or statistical adjustment.

Co-interventions, interventions that impact the outcome other than the primary intervention of the study, can also allow for erroneous conclusions in clinical trials ( 26 – 28 ). If there are differences between treatment arms in the amount or type of additional therapeutic elements then the study conclusions may be incorrect ( 29 ). For example, if a placebo treatment arm utilizes more over-the-counter medication than the experimental treatment arm, both treatment arms may have the same therapeutic improvement and show no effect of the experimental treatment. However, the placebo arm improvement is due to the over-the-counter medication and if that was prohibited, there may be a therapeutic difference between the two treatment arms. The exclusion or tracking and statistical adjustment of co-interventions serves to strengthen an RCT by minimizing this potential effect.

Participants drop out of a study for multiple reasons, but if there are differential dropout rates between intervention arms or high overall dropout rates, there may be biased data or erroneous study conclusions ( 26 – 28 ). A commonly accepted dropout rate is 20% however, studies with dropout rates below 20% may have erroneous conclusions ( 29 ). Common methods for minimizing dropout include incentivizing study participation or short study duration, however, these may also lead to lack of generalizability or validity.

Intention-to-treat (ITT) analysis is a method of analysis that quantitatively addresses deviations from random allocation ( 26 – 28 ). This method analyses individuals based on their allocated intervention, regardless of whether or not that intervention was actually received due to protocol deviations, compliance concerns or subsequent withdrawal. By maintaining individuals in their allocated intervention for analyses, the benefits of randomization will be captured ( 18 , 26 – 29 ). If analysis of actual treatment is solely relied upon, then some of the theoretical benefits of randomization may be lost. This analysis method relies on complete data. There are different approaches regarding the handling of missing data and no consensus has been put forth in the literature. Common approaches are imputation or carrying forward the last observed data from individuals to address issues of missing data ( 18 , 19 ).

Assessment timing can play an important role in the impact of interventions, particularly if intervention effects are acute and short lived ( 26 – 29 , 33 ). The specific timing of assessments are unique to each intervention, however, studies that allow for meaningfully different timing of assessments are subject to erroneous results. For example, if assessments occur differentially after an injection of a particularly fast acting, short-lived medication the difference observed between intervention arms may be due to a higher proportion of participants in one intervention arm being assessed hours after the intervention instead of minutes. By tracking differences in assessment times, researchers can address the potential scope of this problem, and try to address it using statistical or other methods ( 26 – 28 , 33 ).

Randomized controlled trials are the principle method for improving treatment of disease, and there are some standardized methods for grading RCTs, and subsequently creating best practice guidelines ( 29 , 34 – 36 ). Much of the current practice of medicine lacks moderate or high quality RCTs to address what treatment methods have demonstrated efficacy and much of the best practice guidelines remains based on consensus from experts ( 28 , 37 ). The reliance on high quality methodology in all types of studies will allow for continued improvement in the assessment of causal factors for health outcomes and the treatment of diseases.

Standards of research and reporting

There are many published standards for the design, execution and reporting of biomedical research, which can be found in Table 3 . The purpose and content of these standards and guidelines are to improve the quality of biomedical research which will result in providing sound conclusions to base medical decision making upon. There are published standards for categories of study designs such as observational studies (e.g. STROBE), interventional studies (e.g. CONSORT), diagnostic studies (e.g. STARD, QUADAS), systematic reviews and meta-analyses (e.g. PRISMA ), as well as others. The aim of these standards and guideline are to systematize and elevate the quality of biomedical research design, execution, and reporting.

Published standard for study design and reporting.

• Consolidated Standards Of Reporting Trials (CONSORT, www.consort-statement.org ) are interventional study standards, a 25 item checklist and flowchart specifically designed for RCTs to standardize reporting of key elements including design, analysis and interpretation of the RCT.
• Strengthening the Reporting of Observational studies in Epidemiology (STROBE, www.strobe-statement.org ) is a collection of guidelines specifically for standardization and improvement of the reporting of observational epidemiological research. There are specific subsets of the STROBE statement including molecular epidemiology (STROBE-ME), infectious diseases (STROBE-ID) and genetic association studies (STREGA).
• Standards for Reporting Studies of Diagnos tic Accuracy (STARD, www.stard-statement.org ) is a 25 element checklist and flow diagram specifically designed for the reporting of diagnostic accuracy studies.
• Quality assessment of diagnostic accuracy studies (QUADAS, www.bris.ac.uk/quadas ) is a quality assessment of diagnostic accuracy studies.
• Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA, www.prisma-statement.org ) is a 27 element checklist and multiphase flow diagram to improve quality of reporting systematic reviews and meta-analyses. It replaces the QUOROM statement.
• Consolidated criteria for reporting qualitative research (COREQ) is a 32 element checklist designed for reporting of qualitative data from interviews and focus groups.
• Statistical Analyses and Methods in the Published Literature (SAMPL) is a guideline for statistical methods and analyses of all types of biomedical research.
• Consensus-based Clinical Case Reporting Guideline Development (CARE, www.carestatement.org ) is a checklist comprised of 13 elements and is designed only for case reports.
• Standards for Quality Improvement Reporting Excellence (SQUIRE, www.squire-statement.org ) are publication guidelines comprised of 19 elements, for authors aimed at quality improvement in health care reporting.
• Consolidated Health Economic Evaluation Reporting Standards (CHEERS) is a 24 element checklist of reporting practices for economic evaluations of interventional studies.
• Enhancing transparency in reporting the synthesis of qualitative research (ENTREQ) is a guideline specifically for standardizing and improving the reporting of qualitative biomedical research.

When designing or evaluating a study it may be helpful to review the applicable standards prior to executing and publishing the study. All published standards and guidelines are available on the web, and are updated based on current best practices as biomedical research evolves. Additionally, there is a network called “Enhancing the quality and transparency of health research” (EQUATOR, www.equator-network.org ) , which has guidelines and checklists for all standards reported in Table 3 and is continually updated with new study design or specialty specific standards.

The appropriate selection of a study design is only one element in successful research. The selection of a study design should incorporate consideration of costs, access to cases, identification of the exposure, the epidemiologic measures that are required, and the level of evidence that is currently published regarding the specific exposure-outcome relationship that is being assessed. Reviewing appropriate published standards when designing a study can substantially strengthen the execution and interpretation of study results.

Potential conflict of interest

None declared.

Metode Penelitian Deskriptif Observasional: Temukan Fakta-fakta Keaslian dengan Gaya Santai

• 1 Langkah 1: Pilih Objek Penelitian yang Sesuai
• 2 Langkah 2: Tetapkan Tujuan Penelitian
• 3 Langkah 3: Desain Rencana Observasi dan Pengumpulan Data
• 4 Langkah 4: Mulai Observasi dan Catat Semua Temuan Anda
• 5 Langkah 5: Analisis Data dan Interpretasi
• 6 Langkah 6: Buat Kesimpulan dan Saran
• 7.1 Kelebihan Metode Penelitian Deskriptif Observasional
• 7.2 Cara Melakukan Metode Penelitian Deskriptif Observasional
• 7.3 Kelemahan Metode Penelitian Deskriptif Observasional
• 8.1 1. Apa perbedaan antara metode penelitian deskriptif observasional dan metode penelitian eksperimental?
• 8.2 2. Apa saja jenis-jenis metode penelitian deskriptif observasional?
• 8.3 3. Bagaimana cara menghindari bias pengamat dalam metode penelitian deskriptif observasional?
• 8.4 4. Apa yang harus dilakukan jika terdapat banyak faktor lingkungan yang dapat memengaruhi hasil observasi dalam metode penelitian deskriptif observasional?
• 8.5 5. Bagaimana pentingnya metode penelitian deskriptif observasional dalam ilmu sosial dan kesehatan?
• 9.1 Share this:
• 9.2 Related posts:

Melalui metode penelitian deskriptif observasional ini, kamu dapat melakukan pengamatan langsung terhadap objek yang diteliti. Baik itu manusia, hewan, atau pun objek yang tidak hidup seperti lingkungan atau benda-benda. Pada dasarnya, metode penelitian ini bertujuan untuk menggambarkan secara jelas dan rinci tentang apa yang diamati.

Langkah 1: Pilih Objek Penelitian yang Sesuai

Langkah 2: tetapkan tujuan penelitian, langkah 3: desain rencana observasi dan pengumpulan data, langkah 4: mulai observasi dan catat semua temuan anda, langkah 5: analisis data dan interpretasi, langkah 6: buat kesimpulan dan saran, apa itu metode penelitian deskriptif observasional, kelebihan metode penelitian deskriptif observasional.

• Memberikan gambaran yang jelas mengenai suatu fenomena tertentu.
• Berfokus pada pengamatan langsung yang membantu memperoleh data yang valid.
• Mampu menggambarkan karakteristik, pola, dan hubungan antara variabel-variabel yang diamati.
• Mudah dilakukan dan relatif hemat biaya.

Cara Melakukan Metode Penelitian Deskriptif Observasional

• Tentukan tujuan penelitian dan rumusan masalah yang ingin dipecahkan.
• Pilih target populasi atau sampel yang akan diamati.
• Buat rancangan observasi yang sesuai dengan tujuan penelitian.
• Lakukan pengamatan langsung terhadap fenomena yang diamati.
• Rekam data yang diperoleh sesuai dengan variabel yang ditetapkan.
• Analisis dan interpretasikan data yang telah dikumpulkan.
• Susun laporan penelitian yang berisi hasil dan kesimpulan dari penelitian.

Kelemahan Metode Penelitian Deskriptif Observasional

• Terbatasnya generalisasi hasil penelitian karena tidak ada penggunaan variabel bebas atau perlakuan tertentu.
• Potensi bias pengamat dalam proses pengamatan dan pencatatan data.
• Sensitivitas terhadap faktor lingkungan dan situasional yang dapat memengaruhi hasil observasi.

FAQ (Frequently Asked Questions) tentang Metode Penelitian Deskriptif Observasional

1. apa perbedaan antara metode penelitian deskriptif observasional dan metode penelitian eksperimental, 2. apa saja jenis-jenis metode penelitian deskriptif observasional, 3. bagaimana cara menghindari bias pengamat dalam metode penelitian deskriptif observasional, 4. apa yang harus dilakukan jika terdapat banyak faktor lingkungan yang dapat memengaruhi hasil observasi dalam metode penelitian deskriptif observasional, 5. bagaimana pentingnya metode penelitian deskriptif observasional dalam ilmu sosial dan kesehatan, share this:, related posts:.

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• What Is an Observational Study? | Guide & Examples

What Is an Observational Study? | Guide & Examples

Published on 5 April 2022 by Tegan George . Revised on 20 March 2023.

An observational study is used to answer a research question based purely on what the researcher observes. There is no interference or manipulation of the research subjects, and no control and treatment groups .

These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes. While quantitative observational studies exist, they are less common.

Observational studies are generally used in hard science, medical, and social science fields. This is often due to ethical or practical concerns that prevent the researcher from conducting a traditional experiment . However, the lack of control and treatment groups means that forming inferences is difficult, and there is a risk of confounding variables impacting your analysis.

Table of contents

Types of observation, types of observational studies, observational study example, advantages and disadvantages of observational studies, observational study vs experiment, frequently asked questions.

There are many types of observation, and it can be challenging to tell the difference between them. Here are some of the most common types to help you choose the best one for your observational study.

Prevent plagiarism, run a free check.

There are three main types of observational studies: cohort studies, case–control studies, and cross-sectional studies.

Cohort studies

Cohort studies are more longitudinal in nature, as they follow a group of participants over a period of time. Members of the cohort are selected because of a shared characteristic, such as smoking, and they are often observed over a period of years.

Case–control studies

Case–control studies bring together two groups, a case study group and a control group . The case study group has a particular attribute while the control group does not. The two groups are then compared, to see if the case group exhibits a particular characteristic more than the control group.

For example, if you compared smokers (the case study group) with non-smokers (the control group), you could observe whether the smokers had more instances of lung disease than the non-smokers.

Cross-sectional studies

Cross-sectional studies analyse a population of study at a specific point in time.

This often involves narrowing previously collected data to one point in time to test the prevalence of a theory—for example, analysing how many people were diagnosed with lung disease in March of a given year. It can also be a one-time observation, such as spending one day in the lung disease wing of a hospital.

Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps.

Step 1: Identify your research topic and objectives

The first step is to determine what you’re interested in observing and why. Observational studies are a great fit if you are unable to do an experiment for ethical or practical reasons, or if your research topic hinges on natural behaviors.

Step 2: Choose your observation type and technique

In terms of technique, there are a few things to consider:

• Are you determining what you want to observe beforehand, or going in open-minded?
• Is there another research method that would make sense in tandem with an observational study?
• If yes, make sure you conduct a covert observation.
• If not, think about whether observing from afar or actively participating in your observation is a better fit.
• How can you preempt confounding variables that could impact your analysis?
• You could observe the children playing at the playground in a naturalistic observation.
• You could spend a month at a day care in your town conducting participant observation, immersing yourself in the day-to-day life of the children.
• You could conduct covert observation behind a wall or glass, where the children can’t see you.

Overall, it is crucial to stay organised. Devise a shorthand for your notes, or perhaps design templates that you can fill in. Since these observations occur in real time, you won’t get a second chance with the same data.

Step 3: Set up your observational study

Before conducting your observations, there are a few things to attend to:

• Plan ahead: If you’re interested in day cares, you’ll need to call a few in your area to plan a visit. They may not all allow observation, or consent from parents may be needed, so give yourself enough time to set everything up.
• Determine your note-taking method: Observational studies often rely on note-taking because other methods, like video or audio recording, run the risk of changing participant behavior.
• Get informed consent from your participants (or their parents) if you want to record:  Ultimately, even though it may make your analysis easier, the challenges posed by recording participants often make pen-and-paper a better choice.

Step 4: Conduct your observation

After you’ve chosen a type of observation, decided on your technique, and chosen a time and place, it’s time to conduct your observation.

Here, you can split them into case and control groups. The children with siblings have a characteristic you are interested in (siblings), while the children in the control group do not.

When conducting observational studies, be very careful of confounding or ‘lurking’ variables. In the example above, you observed children as they were dropped off, gauging whether or not they were upset. However, there are a variety of other factors that could be at play here (e.g., illness).

Step 5: Analyse your data

After you finish your observation, immediately record your initial thoughts and impressions, as well as follow-up questions or any issues you perceived during the observation. If you audio- or video-recorded your observations, you can transcribe them.

Your analysis can take an inductive or deductive approach :

• If you conducted your observations in a more open-ended way, an inductive approach allows your data to determine your themes.
• If you had specific hypotheses prior to conducting your observations, a deductive approach analyses whether your data confirm those themes or ideas you had previously.

Next, you can conduct your thematic or content analysis . Due to the open-ended nature of observational studies, the best fit is likely thematic analysis.

Step 6: Discuss avenues for future research

Observational studies are generally exploratory in nature, and they often aren’t strong enough to yield standalone conclusions due to their very high susceptibility to observer bias and confounding variables. For this reason, observational studies can only show association, not causation .

If you are excited about the preliminary conclusions you’ve drawn and wish to proceed with your topic, you may need to change to a different research method , such as an experiment.

• Observational studies can provide information about difficult-to-analyse topics in a low-cost, efficient manner.
• They allow you to study subjects that cannot be randomised safely, efficiently, or ethically .
• They are often quite straightforward to conduct, since you just observe participant behavior as it happens or utilise preexisting data.
• They’re often invaluable in informing later, larger-scale clinical trials or experiments.

Disadvantages

• Observational studies struggle to stand on their own as a reliable research method. There is a high risk of observer bias and undetected confounding variables.
• They lack conclusive results, typically are not externally valid or generalisable, and can usually only form a basis for further research.
• They cannot make statements about the safety or efficacy of the intervention or treatment they study, only observe reactions to it. Therefore, they offer less satisfying results than other methods.

The key difference between observational studies and experiments is that a properly conducted observational study will never attempt to influence responses, while experimental designs by definition have some sort of treatment condition applied to a portion of participants.

However, there may be times when it’s impossible, dangerous, or impractical to influence the behavior of your participants. This can be the case in medical studies, where it is unethical or cruel to withhold potentially life-saving intervention, or in longitudinal analyses where you don’t have the ability to follow your group over the course of their lifetime.

An observational study may be the right fit for your research if random assignment of participants to control and treatment groups is impossible or highly difficult. However, the issues observational studies raise in terms of validity , confounding variables, and conclusiveness can mean that an experiment is more reliable.

If you’re able to randomise your participants safely and your research question is definitely causal in nature, consider using an experiment.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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George, T. (2023, March 20). What Is an Observational Study? | Guide & Examples. Scribbr. Retrieved 16 September 2024, from https://www.scribbr.co.uk/research-methods/observational-study/

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Research Methodologies

October 15, 2020

Observational Data Has Problems. Are Researchers Aware of Them?

Observational data is a tempting shortcut for insights but researchers must consider its potential shortfalls

by Ray Poynter

Managing Director at The Future Place

The world is shifting from asking questions to utilizing observational data (mostly for very good reasons) and this is creating a new set of problems that researchers need to recognize and address.

What is observational data?

In market research, observational data refers to information gathered without the subject of the research (for example an individual customer, patient, employee, etc.) having to be explicitly involved in recording what they are doing. For example, collecting data without people having to respond to a questionnaire, without having to take part in a depth interview, and without having to maintain a research diary.

Most big data is observational data, for example, the transaction records from a bank, people’s viewing habits on a video streaming service, or posts on social media. But, observational data can also be small data (based on just a few people). For example, participant ethnographic methods , used to study people in their everyday lives, collect observational data, that is clearly not ‘big data’.

Observational data in market research can be based on census or it can be based on sample. For example, a few years ago a leading mobile phone company was able to sell very detailed data about the movements of its contract customers (over ten million people), but it could not provide this information for its millions of ‘pay as you go’ customers. In this case, the mobile phone company was (depending on your view) offing a census of its contract customers, or it was offering a large sample of its total customer base (or a sample of all mobile phone users in the country).

In contrast, a food delivery company from a small town may have data on all of its one-thousand customers. The data might comprise: what was purchased when it was purchased (date and time), the price, the delivery time, and perhaps background variables such as the weather. This observational data would be a census, even though it was based on just 1000 customers.

Observational data can be relatively objective or more subjective. If, for example, the data comprises a digital record of all bank transactions it would be considered objective and numeric. If the data were ethnographic notes from a researcher observing the customers of a coffee shop, the data would be more subjective, and (in all likelihood) less numerical.

Observational data can be numbers, images, videos – indeed anything that can be recorded. Observational data can be recorded without people actively doing anything, for example monitoring their mobile phone connections to cells, or it can be the result of actions they take as part of their everyday life (for example things they post to social media).

Observational data can be mixed with question-type data. For example, a food delivery company may have numerous observational data points about each customer and each purchase, but they might also ask for a satisfaction score and a satisfaction comment – these two pieces of data are not observational data but can be used to help interpret observational data.

There are also some nuanced observational techniques that blend questions and observations, for example, an ad testing system where a sample of people watch one or more ads, answer some traditional questions, but they are also observed using techniques such as eye-tracking, facial coding, and perhaps some form of brain scanning. This is observational data, but not based on observing people in their natural environment, going about their everyday lives.

Why the shift to observational data?

There has been a major shift towards observational data in terms of gathering data to inform insights about people and the actions they take. This has been the result of several trends that have tended to pull in the same direction.

1. The growing realization of the limitations of questions

For example, the extent to which people are poor witnesses to their own motivations and plans. These issues have been highlighted by neuroscientists (e.g. Antonio Damasio) and behavioral economists (e.g. Daniel Kahneman and Dan Ariely) – but researchers have been aware of these issues for decades, and have sought to mitigate them.

2. The increased difficulty in conducting question-based research

For example, declining response rates and the problems of accessing representative samples.

3. Observational data is increasingly available

This availability change is largely because of the shift to a digital world. The internet and smart devices (smartcards, smartphones, smart homes, etc.) mean that people create a digital wake of information behind them that can be used to create observational data sets. Not only is this observational data widely available, but it is also often much cheaper than data collected via researchers asking questions.

4. Increased processing power

In the past, one of the reasons to focus on small amounts of qualitative data or the responses to highly structured questionnaires was the challenge of processing them. As computers and algorithms have become more powerful, the range of options has expanded.

5. From a sample to a census

In many cases, observational data allows researchers to work with a census rather than a sample. For example, studying the purchase/travel choices of every customer of a specific airline. This sometimes has genuine benefits (e.g. eliminating sampling error and potential sampling bias ) and frequently has ‘face value’ benefits).

What are the main problems with observational data?

Despite the attractiveness of real data, from real customers, living real, everyday lives, observational data creates its own problems. Researchers need to be aware of these problems and seek to address them. The problems include the following ten issues:

1. Where the observational data tells you the wrong thing

For example, when HRT was first assessed using observational data, it was decided that it reduced heart problems in women, which led to it being widely prescribed. Later a ‘proper’ randomized controlled test indicated that HRT was slightly worse for women’s hearts. The observational data had not accounted for the fact that wealthier/healthier women were more likely to be prescribed HRT.

The leading data scientist/commentator Nate Silver has said that as big data grows, the proportion of spurious correlations will grow much faster than the proportion of useful, meaningful findings. Within this category of problems are selection bias, survival bias, the  post hoc ergo propter hoc  fallacy, and random variation providing spurious correlations.

2. Confusing cause and effect

There may be a real relationship in the observational data, but the direction of causality may be wrongly determined. A rooster crows before dawn, but it does not cause the dawn; the impending dawn is the trigger for the rooster to crow. In terms of marketing, consider the case where somebody searches on Google, and because of what they find they decide to buy a specific smartwatch.

Alternatively, they might have decided to buy that watch because their friend recommended it and then used Google to find out which stores near them stock it. With observational data, identifying cause and effect can be difficult.

3. Ignoring the true driver

Consider the case where the head of social marketing shows the company’s Chief Marketing Office that the sales of his company’s ice cream appear to be driven by social media advertising. When the advertising spend goes up, the sales of ice cream go up, and when the advertising spend goes down, the sales of ice cream go down.

The CMO may (if she or he is savvy) point out that sales go up in the summer and down in the winter, and that the social media spend follows that pattern too (to maximize share of the market).

4. Where multicollinearity means that the individual factors can’t be measured

If all the brands in a market move their prices up and down together, it will not be possible to model the linkage between price and brands from observational data – because there is not sufficient variation.

If a complex, multi-channel advertising campaign is launched across all the channels at the same time, it will often be impossible to accurately measure the impact of one element of the campaign in isolation – for example, how much did adding that famous TV personality contribute to the change in sales?

5. The relationship is too complex and/or chaotic to measure

Some relationships can be approximated with relatively simple models (models made up of linear components) – however other relationships are more complex. Nate Silver contrasts weather forecasting (which has improved dramatically over the last thirty years) with the prediction of earthquakes (which has not really changed at all during that time, and some experts in the field fear it may never be predictable). Nassim Taleb has written (in his book ‘Black Swan’) about the rare events that are impossible to predict with traditional statistics.

6. Because of feedback loops between cause and effect

Economists use observational data to try to understand markets and to make predictions about things like recessions. However, economists have a very bad record at predicting recessions and this is largely for two reasons. Firstly, there are many more variables than recessions, which means that there is an infinite number solutions (if you think back to high school mathematics you will remember you need more observations than variables).

Secondly, economists look at previous recessions and deduce that when the currency does X, governments do Y, and investors and companies do Z, the result is A, B & C. However, governments, investors, and companies also look at what they did last time, and at what the economists have published, and the next time the currency does X, the governments, investors, and companies change their behaviour because of what was learned from the last iteration.

When smart metres are installed in people’s homes, they provide great observational data about how energy is consumed, but they also highlight this to the consumer, who may then adjust what they do (for example to reduce costs). Similarly, data from the effectiveness of digital campaigns is often used in real-time to adjust the campaign, this can lead to better results, but can confound the statistician’s ability to measure overall relationships.

7. The measurement effect

In many situations, when you measure something you change it. For example, if you put a thermometer in a glass of water to measure the temperature of the water you will (very slightly) change the temperature of the water. Researchers have shown that just by painting a pair of eyes over an honesty box for paying for donuts they can change human behaviour. As more people become aware that their behaviour is being measured, the behaviour we are seeking to measure may change.

8. Confusing influence and homophily

Sinan Aral has shown that researchers often confuse influence (the extent to which we copy somebody else) with homophily (where we hang out with people who choose the same things as us). For example, do smokers smoke because their friends do (influence), or do smokers hang out together because they smoke? Sinan Aral’s research, based on observational data generated by experiments has shown that observational data based on naturally occurring phenomena can be misleading if the wrong model is assumed (for example if the model assumes that behavior is driven by influence rather than homophily).

One example of this effect is when examining the impact of campaigns that use free samples, simple research can often show the ROI of the samples given away, but experiments may show that many of the people who were given the free samples would have bought anyway, changing the ROI.

9. Not explaining the why

Analysis of observational data may tell us that a specific pattern is happening, but it may not tell us why it is happening, and to utilise the pattern we may need to know ‘the Why’. For example, observational studies show that when rain is forecast, fewer people walk or cycle to work, and more people use private or public transport – in this case, the ‘Why?’ seems to be relatively straightforward. A nice example of where observational data does not provide the why is given by Ben Wellington in his 2014 New York TEDx video. From New York City data, he identifies which fire hydrants in Manhattan generate the highest revenue.

Two hydrants in particular generate many more fines than any other in the City – over $55,000 a day. But Wellington can’t intuit what is causing it. So, he visits the location, looks at it, photographs it, and identifies that it is because of unclear signage and a specific road layout. In research terms what he has done is use qualitative research to understand the why from a big data analysis.

10. Inability to research things that have not happened (yet)

A large proportion of market research relates to things that do not yet exist, for example, advertising and concept pre-testing. No amount of listening to social media or analysing purchase behaviour data is going to tell you whether the next ad for your airline is going to ‘work’. Purely observational data will not tell you which new flavours you should add to your drink range. In both of these cases, observational data can provide some useful input, but it can’t solve the problem.

What steps should researchers take to minimize the problems with observational data?

There is a wide variety of things that researchers can and should do to improve their use of observational data, including:

1. Consider the counterfactual

What would have happened if we had not done X. For example, if we had not used social media to promote our ice-cream in the summer, what would the sales have been? The counterfactual is likely to be an approximation, (for example, the sales for Jun-August will be the average of the last three years).

2. Make predictions in advance

There are so many ways of analysing observational data that if the analysis starts after the project has finished, positive news can likely be found – but this news may not be valid or sufficiently robust. The best practice is to say in advance that this activity (e.g. this new advertising campaign, is supposed to work by reaching this group, it is supposed to make them see the brand as more ‘edgy’, and it is supposed to increase trial by 10% and sales by 8%. Armed with these predictions, it is much easier to assess whether the campaign had the desired effect.

3. Try to model the data to mitigate various biases

Techniques used to mitigate the problems with observational data include weighing the data to make it better match the population, using matching to find similar people in the population who were not exposed to the stimuli, and utilising Bayesian statistics (based on the probability of X given that Y has happened). These models can be very complex, but that will not necessarily make them correct. If a systematic underlying bias has been missed, then this modeling will make the data more plausible, but not necessarily more accurate.

4. Build experiments into the observational process

If you want to measure the impact of a complex multi-channel advertising campaign, do not launch with a big bang. Try to vary the sequence and spend across groups that can be measured (in the old days this would be by geography, with digital it can be achieved by creating groups). At the 2018 ESOMAR APAC conference in Bangkok, Brent Smart, Chief Marketing Officer, IAG, (an Australian insurance company) showed a case where he experimented on a specific campaign – one group of people saw it, one group did not. The experiment showed the campaign delivered no additional sales, but the attribution analysis that was conducted (two versions) showed substantial incremental sales.

5. Use surveys and qual to explain the missing part of the picture

When you need to know ‘the Why’, or when you want to know how to change things, traditional research can be the extra ingredient that observational (especially big data) needs. In many cases, like the Ben Wellington example above, the research that will unlock quant, observational data will be qualitative.

The ethics of data collection

There are two main ethical issues about using observational data collection. The first is pretty obvious – do you have permission to collect and use the data? The debacle over Facebook and Cambridge Analytica and Europe’s GDPR requirements show the importance of ensuring that you have permission to process individual data, including observational data.

The second problem relates to information you might uncover and the actions you should take. This ethical problem is clearest in social and medical research. Two different treatments or plans are put into use – one group receive option A and the other group option B. However, if the results of the trial show that one of the treatments is less good (or actually harmful) then it may not be ethical to keep the trial going, which means the information may be compromised.

In a commercial situation, in many countries, if the analysis of observational data shows that people are on the wrong mobile phone plan, if they are paying too much for their energy, or if they could have bought a cheaper ticket, then it may be unethical not to intervene (indeed some governments/courts may also decide it is illegal mis-selling).

Observational research should be reviewed for its ethical implications – and the nature of what is ethical is likely to continuously evolve for the foreseeable future.

Want to find out more about this topic?

I submitted a paper to the IJMR (i.e. lots more references and less opinion Ray Poynter) – if you have material or ideas that you think I should address, please reach out to me.

Comments are moderated to ensure respect towards the author and to prevent spam or self-promotion. Your comment may be edited, rejected, or approved based on these criteria. By commenting, you accept these terms and take responsibility for your contributions.

Ray Poynter

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Online observation

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Online observation is a research method that involves selective and detailed viewing, monitoring, acquisition and recording of online phenomena. This can include noticing facts, taking measurements and recording judgements and inferences. In qualitative research online observation does not follow a set, pre-defined procedure and can, instead, be open, unstructured, flexible and diverse. Careful and systematic recording of all online observation is required in both qualitative and quantitative research. Online observation can be carried out overtly or covertly. In overt observation participants know that they are part of a research project and have given informed consent. B. Smart et al. provide a comprehensive guide, covering the historical development of observational methods and techniques, theoretical and philosophical understandings and assumptions and practical issues associated with conducting an observational study. Observation of online research communities and panels: this involves observation of the interaction, behaviour and activity of online research communities and panels.

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• What is Secondary Research? | Definition, Types, & Examples

What is Secondary Research? | Definition, Types, & Examples

Published on January 20, 2023 by Tegan George . Revised on January 12, 2024.

Secondary research is a research method that uses data that was collected by someone else. In other words, whenever you conduct research using data that already exists, you are conducting secondary research. On the other hand, any type of research that you undertake yourself is called primary research .

Secondary research can be qualitative or quantitative in nature. It often uses data gathered from published peer-reviewed papers, meta-analyses, or government or private sector databases and datasets.

Table of contents

When to use secondary research, types of secondary research, examples of secondary research, advantages and disadvantages of secondary research, other interesting articles, frequently asked questions.

Secondary research is a very common research method, used in lieu of collecting your own primary data. It is often used in research designs or as a way to start your research process if you plan to conduct primary research later on.

Since it is often inexpensive or free to access, secondary research is a low-stakes way to determine if further primary research is needed, as gaps in secondary research are a strong indication that primary research is necessary. For this reason, while secondary research can theoretically be exploratory or explanatory in nature, it is usually explanatory: aiming to explain the causes and consequences of a well-defined problem.

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Secondary research can take many forms, but the most common types are:

Statistical analysis

Literature reviews, case studies, content analysis.

There is ample data available online from a variety of sources, often in the form of datasets. These datasets are often open-source or downloadable at a low cost, and are ideal for conducting statistical analyses such as hypothesis testing or regression analysis .

Credible sources for existing data include:

• The government
• Government agencies
• Non-governmental organizations
• Educational institutions
• Businesses or consultancies
• Libraries or archives
• Newspapers, academic journals, or magazines

A literature review is a survey of preexisting scholarly sources on your topic. It provides an overview of current knowledge, allowing you to identify relevant themes, debates, and gaps in the research you analyze. You can later apply these to your own work, or use them as a jumping-off point to conduct primary research of your own.

Structured much like a regular academic paper (with a clear introduction, body, and conclusion), a literature review is a great way to evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

A case study is a detailed study of a specific subject. It is usually qualitative in nature and can focus on  a person, group, place, event, organization, or phenomenon. A case study is a great way to utilize existing research to gain concrete, contextual, and in-depth knowledge about your real-world subject.

You can choose to focus on just one complex case, exploring a single subject in great detail, or examine multiple cases if you’d prefer to compare different aspects of your topic. Preexisting interviews , observational studies , or other sources of primary data make for great case studies.

Content analysis is a research method that studies patterns in recorded communication by utilizing existing texts. It can be either quantitative or qualitative in nature, depending on whether you choose to analyze countable or measurable patterns, or more interpretive ones. Content analysis is popular in communication studies, but it is also widely used in historical analysis, anthropology, and psychology to make more semantic qualitative inferences.

Secondary research is a broad research approach that can be pursued any way you’d like. Here are a few examples of different ways you can use secondary research to explore your research topic .

Secondary research is a very common research approach, but has distinct advantages and disadvantages.

Advantages of secondary research

Advantages include:

• Secondary data is very easy to source and readily available .
• It is also often free or accessible through your educational institution’s library or network, making it much cheaper to conduct than primary research .
• As you are relying on research that already exists, conducting secondary research is much less time consuming than primary research. Since your timeline is so much shorter, your research can be ready to publish sooner.
• Using data from others allows you to show reproducibility and replicability , bolstering prior research and situating your own work within your field.

Disadvantages of secondary research

Disadvantages include:

• Ease of access does not signify credibility . It’s important to be aware that secondary research is not always reliable , and can often be out of date. It’s critical to analyze any data you’re thinking of using prior to getting started, using a method like the CRAAP test .
• Secondary research often relies on primary research already conducted. If this original research is biased in any way, those research biases could creep into the secondary results.

Many researchers using the same secondary research to form similar conclusions can also take away from the uniqueness and reliability of your research. Many datasets become “kitchen-sink” models, where too many variables are added in an attempt to draw increasingly niche conclusions from overused data . Data cleansing may be necessary to test the quality of the research.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2024, January 12). What is Secondary Research? | Definition, Types, & Examples. Scribbr. Retrieved September 16, 2024, from https://www.scribbr.com/methodology/secondary-research/

Largan, C., & Morris, T. M. (2019). Qualitative Secondary Research: A Step-By-Step Guide (1st ed.). SAGE Publications Ltd.

Peloquin, D., DiMaio, M., Bierer, B., & Barnes, M. (2020). Disruptive and avoidable: GDPR challenges to secondary research uses of data. European Journal of Human Genetics , 28 (6), 697–705. https://doi.org/10.1038/s41431-020-0596-x

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